1
|
Feng K, Shao Y, Li J, Guan X, Liu Q, Hu M, Chu M, Li H, Chen F, Yi Z, Zhang J. A lactate-responsive gene signature predicts the prognosis and immunotherapeutic response of patients with triple-negative breast cancer. CANCER INNOVATION 2024; 3:e124. [PMID: 38948251 PMCID: PMC11212277 DOI: 10.1002/cai2.124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 02/21/2024] [Accepted: 03/05/2024] [Indexed: 07/02/2024]
Abstract
Background Increased glycolytic activity and lactate production are characteristic features of triple-negative breast cancer (TNBC). The aim of this study was to determine whether a subset of lactate-responsive genes (LRGs) could be used to classify TNBC subtypes and predict patient outcomes. Methods Lactate levels were initially measured in different breast cancer (BC) cell types. Subsequently, MDA-MB-231 cells treated with 2-Deoxy-d-glucose or l-lactate were subjected to RNA sequencing (RNA-seq). The gene set variation analysis algorithm was utilized to calculate the lactate-responsive score, conduct a differential analysis, and establish an association with the extent of immune infiltration. Consensus clustering was then employed to classify TNBC patients. Tumor immune dysfunction and exclusion, cibersort, single-sample gene set enrichment analysis, and EPIC, were used to compare the tumor-infiltrating immune cells between TNBC subtypes and predict the response to immunotherapy. Furthermore, a prognostic model was developed by combining 98 machine learning algorithms, to assess the predictive significance of the LRG signature. The predictive value of immune infiltration and the immunotherapy response was also assessed. Finally, the association between lactate and various anticancer drugs was examined based on expression profile similarity principles. Results We found that the lactate levels of TNBC cells were significantly higher than those of other BC cell lines. Through RNA-seq, we identified 14 differentially expressed LRGs in TNBC cells under varying lactate levels. Notably, this LRG signature was associated with interleukin-17 signaling pathway dysregulation, suggesting a link between lactate metabolism and immune impairment. Furthermore, the LRG signature was used to categorize TNBC into two distinct subtypes, whereby Subtype A was characterized by immunosuppression, whereas Subtype B was characterized by immune activation. Conclusion We identified an LRG signature in TNBC, which could be used to predict the prognosis of patients with TNBC and gauge their response to immunotherapy. Our findings may help guide the precision treatment of patients with TNBC.
Collapse
Affiliation(s)
- Kaixiang Feng
- Department of Breast and Thyroid Surgery, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological BehaviorsHubei Cancer Clinical Study CenterWuhanChina
| | - Youcheng Shao
- Department of Pathology and Pathophysiology, Hubei Provincial Key Laboratory of Developmentally Originated Disease, TaiKang Medical School (School of Basic Medical Sciences)Wuhan UniversityWuhanChina
| | - Jun Li
- Department of Breast and Thyroid Surgery, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological BehaviorsHubei Cancer Clinical Study CenterWuhanChina
| | - Xiaoqing Guan
- Department of Pathology and Pathophysiology, Hubei Provincial Key Laboratory of Developmentally Originated Disease, TaiKang Medical School (School of Basic Medical Sciences)Wuhan UniversityWuhanChina
| | - Qin Liu
- Department of Pathology and Pathophysiology, Hubei Provincial Key Laboratory of Developmentally Originated Disease, TaiKang Medical School (School of Basic Medical Sciences)Wuhan UniversityWuhanChina
| | - Meishun Hu
- Department of Breast and Thyroid Surgery, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological BehaviorsHubei Cancer Clinical Study CenterWuhanChina
| | - Mengfei Chu
- Department of Human Anatomy, TaiKang Medical School (School of Basic Medical Sciences)Wuhan UniversityWuhanChina
| | - Hui Li
- Department of Pathology and Pathophysiology, Hubei Provincial Key Laboratory of Developmentally Originated Disease, TaiKang Medical School (School of Basic Medical Sciences)Wuhan UniversityWuhanChina
| | - Fangfang Chen
- Department of Breast and Thyroid Surgery, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological BehaviorsHubei Cancer Clinical Study CenterWuhanChina
| | - Zongbi Yi
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological BehaviorsHubei Cancer Clinical Study CenterWuhanChina
| | - Jingwei Zhang
- Department of Breast and Thyroid Surgery, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological BehaviorsHubei Cancer Clinical Study CenterWuhanChina
| |
Collapse
|
2
|
Chen H, Jing C, Shang L, Zhu X, Zhang R, Liu Y, Wang M, Xu K, Ma T, Jing H, Wang Z, Li X, Chong W, Li L. Molecular characterization and clinical relevance of metabolic signature subtypes in gastric cancer. Cell Rep 2024; 43:114424. [PMID: 38959111 DOI: 10.1016/j.celrep.2024.114424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 05/06/2024] [Accepted: 06/14/2024] [Indexed: 07/05/2024] Open
Abstract
Metabolic reprogramming dictates tumor molecular attributes and therapeutic potentials. However, the comprehensive metabolic characteristics in gastric cancer (GC) remain obscure. Here, metabolic signature-based clustering analysis identifies three subtypes with distinct molecular and clinical features: MSC1 showed better prognosis and upregulation of the tricarboxylic acid (TCA) cycle and lipid metabolism, combined with frequent TP53 and RHOA mutation; MSC2 had moderate prognosis and elevated nucleotide and amino acid metabolism, enriched by intestinal histology and mismatch repair deficient (dMMR); and MSC3 exhibited poor prognosis and enhanced glycan and energy metabolism, accompanied by diffuse histology and frequent CDH1 mutation. The Shandong Provincial Hospital (SDPH) in-house dataset with matched transcriptomic, metabolomic, and spatial-metabolomic analysis also validated these findings. Further, we constructed the metabolic subtype-related prognosis gene (MSPG) scoring model to quantify the activity of individual tumors and found a positive correlation with cuproptosis signaling. In conclusion, comprehensive recognition of the metabolite signature can enhance the understanding of diversity and heterogeneity in GC.
Collapse
Affiliation(s)
- Hao Chen
- Clinical Research Center of Shandong University, Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Jinan, Shandong 250012, China.
| | - Changqing Jing
- Department of Gastrointestinal Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Key Laboratory of Engineering of Shandong Province, Shandong Provincial Hospital, Jinan 250021, China; Department of Gastrointestinal Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Medical Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250021, China
| | - Liang Shang
- Department of Gastrointestinal Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Key Laboratory of Engineering of Shandong Province, Shandong Provincial Hospital, Jinan 250021, China; Department of Gastrointestinal Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Medical Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250021, China
| | - Xingyu Zhu
- Department of Gastrointestinal Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Key Laboratory of Engineering of Shandong Province, Shandong Provincial Hospital, Jinan 250021, China; Department of Gastrointestinal Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Medical Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250021, China
| | - Ronghua Zhang
- Department of Gastrointestinal Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Key Laboratory of Engineering of Shandong Province, Shandong Provincial Hospital, Jinan 250021, China; Department of Gastrointestinal Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Medical Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250021, China
| | - Yuan Liu
- Department of Gastrointestinal Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Key Laboratory of Engineering of Shandong Province, Shandong Provincial Hospital, Jinan 250021, China; Department of Gastrointestinal Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Medical Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250021, China
| | - Mingfei Wang
- Department of Gastrointestinal Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Key Laboratory of Engineering of Shandong Province, Shandong Provincial Hospital, Jinan 250021, China; Department of Gastrointestinal Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Medical Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250021, China
| | - Kang Xu
- Department of Gastrointestinal Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Key Laboratory of Engineering of Shandong Province, Shandong Provincial Hospital, Jinan 250021, China; Department of Gastrointestinal Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Medical Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250021, China
| | - Tianrong Ma
- Department of Gastrointestinal Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Key Laboratory of Engineering of Shandong Province, Shandong Provincial Hospital, Jinan 250021, China; Department of Gastrointestinal Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Medical Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250021, China
| | - Haiyan Jing
- Department of Pathology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - Ze Wang
- Clinical Research Center of Shandong University, Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Jinan, Shandong 250012, China
| | - Xin Li
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
| | - Wei Chong
- Department of Gastrointestinal Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Key Laboratory of Engineering of Shandong Province, Shandong Provincial Hospital, Jinan 250021, China; Department of Gastrointestinal Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Medical Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250021, China.
| | - Leping Li
- Department of Gastrointestinal Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Key Laboratory of Engineering of Shandong Province, Shandong Provincial Hospital, Jinan 250021, China; Department of Gastrointestinal Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Medical Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250021, China.
| |
Collapse
|
3
|
Wang L, Yang R, Kong Y, Zhou J, Chen Y, Li R, Chen C, Tang X, Chen X, Xia J, Chen X, Cheng B, Ren X. Integrative single-cell and bulk transcriptomes analyses reveals heterogeneity of serine-glycine-one-carbon metabolism with distinct prognoses and therapeutic vulnerabilities in HNSCC. Int J Oral Sci 2024; 16:44. [PMID: 38886346 PMCID: PMC11183126 DOI: 10.1038/s41368-024-00310-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 04/03/2024] [Accepted: 04/28/2024] [Indexed: 06/20/2024] Open
Abstract
Metabolic heterogeneity plays a central role in sustaining uncontrolled cancer cell proliferation and shaping the tumor microenvironment (TME), which significantly compromises the clinical outcomes and responses to therapy in head and neck squamous cell carcinoma (HNSCC) patients. This highlights the urgent need to delineate the intrinsic heterogeneity and biological roles of metabolic vulnerabilities to advance precision oncology. The metabolic heterogeneity of malignant cells was identified using single-cell RNA sequencing (scRNA-seq) profiles and validated through bulk transcriptomes. Serine-glycine-one-carbon (SGOC) metabolism was screened out to be responsible for the aggressive malignant properties and poor prognosis in HNSCC patients. A 4-SGOC gene prognostic signature, constructed by LASSO-COX regression analysis, demonstrated good predictive performance for overall survival and therapeutic responses. Patients in the low-risk group exhibited greater infiltration of exhausted CD8+ T cells, and demonstrated better clinical outcomes after receiving immunotherapy and chemotherapy. Conversely, high-risk patients exhibited characteristics of cold tumors, with enhanced IMPDH1-mediated purine biosynthesis, resulting in poor responses to current therapies. IMPDH1 emerged as a potential therapeutic metabolic target. Treatment with IMPDH inhibitors effectively suppressed HNSCC cell proliferation and metastasis and induced apoptosis in vitro and in vivo by triggering GTP-exhaustion nucleolar stress. Our findings underscore the metabolic vulnerabilities of HNSCC in facilitating accurate patient stratification and individualized precise metabolic-targeted treatment.
Collapse
Affiliation(s)
- Lixuan Wang
- Hospital of Stomatology, Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
- Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China
| | - Rongchun Yang
- Hospital of Stomatology, Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
- Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China
| | - Yue Kong
- Hospital of Stomatology, Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
- Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China
| | - Jing Zhou
- Hospital of Stomatology, Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
- Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China
| | - Yingyao Chen
- Hospital of Stomatology, Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
- Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China
| | - Rui Li
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Chuwen Chen
- Hospital of Stomatology, Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
- Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China
| | - Xinran Tang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xiaobing Chen
- Hospital of Stomatology, Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
- Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China
| | - Juan Xia
- Hospital of Stomatology, Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
- Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China
| | - Xijuan Chen
- Hospital of Stomatology, Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
- Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China
| | - Bin Cheng
- Hospital of Stomatology, Sun Yat-Sen University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China.
- Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China.
| | - Xianyue Ren
- Hospital of Stomatology, Sun Yat-Sen University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China.
- Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China.
| |
Collapse
|
4
|
Li S, Zheng Z, Wang B. Machine learning survival prediction using tumor lipid metabolism genes for osteosarcoma. Sci Rep 2024; 14:12934. [PMID: 38839983 PMCID: PMC11153634 DOI: 10.1038/s41598-024-63736-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 05/31/2024] [Indexed: 06/07/2024] Open
Abstract
Osteosarcoma is a primary malignant tumor that commonly affects children and adolescents, with a poor prognosis. The existence of tumor heterogeneity leads to different molecular subtypes and survival outcomes. Recently, lipid metabolism has been identified as a critical characteristic of cancer. Therefore, our study aims to identify osteosarcoma's lipid metabolism molecular subtype and develop a signature for survival outcome prediction. Four multicenter cohorts-TARGET-OS, GSE21257, GSE39058, and GSE16091-were amalgamated into a unified Meta-Cohort. Through consensus clustering, novel molecular subtypes within Meta-Cohort patients were delineated. Subsequent feature selection processes, encompassing analyses of differentially expressed genes between subtypes, univariate Cox analysis, and StepAIC, were employed to pinpoint biomarkers related to lipid metabolism in TARGET-OS. We selected the most effective algorithm for constructing a Lipid Metabolism-Related Signature (LMRS) by utilizing four machine-learning algorithms reconfigured into ten unique combinations. This selection was based on achieving the highest concordance index (C-index) in the test cohort of GSE21257, GSE39058, and GSE16091. We identified two distinct lipid metabolism molecular subtypes in osteosarcoma patients, C1 and C2, with significantly different survival rates. C1 is characterized by increased cholesterol, fatty acid synthesis, and ketone metabolism. In contrast, C2 focuses on steroid hormone biosynthesis, arachidonic acid, and glycerolipid and linoleic acid metabolism. Feature selection in the TARGET-OS identified 12 lipid metabolism genes, leading to a model predicting osteosarcoma patient survival. The LMRS, based on the 12 identified genes, consistently accurately predicted prognosis across TARGET-OS, testing cohorts, and Meta-Cohort. Incorporating 12 published signatures, LMRS showed robust and significantly superior predictive capability. Our results offer a promising tool to enhance the clinical management of osteosarcoma, potentially leading to improved clinical outcomes.
Collapse
Affiliation(s)
- Shuai Li
- Department of Spine Surgery, The Second Xiangya Hospital, Central South University, Renmin Middle Road 139, Changsha, 410011, Hunan, China
| | - Zhenzhong Zheng
- Department of Spine Surgery, The Second Xiangya Hospital, Central South University, Renmin Middle Road 139, Changsha, 410011, Hunan, China
| | - Bing Wang
- Department of Spine Surgery, The Second Xiangya Hospital, Central South University, Renmin Middle Road 139, Changsha, 410011, Hunan, China.
| |
Collapse
|
5
|
Reina C, Šabanović B, Lazzari C, Gregorc V, Heeschen C. Unlocking the future of cancer diagnosis - promises and challenges of ctDNA-based liquid biopsies in non-small cell lung cancer. Transl Res 2024; 272:41-53. [PMID: 38838851 DOI: 10.1016/j.trsl.2024.05.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 04/29/2024] [Accepted: 05/30/2024] [Indexed: 06/07/2024]
Abstract
The advent of liquid biopsies has brought significant changes to the diagnosis and monitoring of non-small cell lung cancer (NSCLC), presenting both promise and challenges. Molecularly targeted drugs, capable of enhancing survival rates, are now available to around a quarter of NSCLC patients. However, to ensure their effectiveness, precision diagnosis is essential. Circulating tumor DNA (ctDNA) analysis as the most advanced liquid biopsy modality to date offers a non-invasive method for tracking genomic changes in NSCLC. The potential of ctDNA is particularly rooted in its ability to furnish comprehensive (epi-)genetic insights into the tumor, thereby aiding personalized treatment strategies. One of the key advantages of ctDNA-based liquid biopsies in NSCLC is their ability to capture tumor heterogeneity. This capability ensures a more precise depiction of the tumor's (epi-)genomic landscape compared to conventional tissue biopsies. Consequently, it facilitates the identification of (epi-)genetic alterations, enabling informed treatment decisions, disease progression monitoring, and early detection of resistance-causing mutations for timely therapeutic interventions. Here we review the current state-of-the-art in ctDNA-based liquid biopsy technologies for NSCLC, exploring their potential to revolutionize clinical practice. Key advancements in ctDNA detection methods, including PCR-based assays, next-generation sequencing (NGS), and digital PCR (dPCR), are discussed, along with their respective strengths and limitations. Additionally, the clinical utility of ctDNA analysis in guiding treatment decisions, monitoring treatment response, detecting minimal residual disease, and identifying emerging resistance mechanisms is examined. Liquid biopsy analysis bears the potential of transforming NSCLC management by enabling non-invasive monitoring of Minimal Residual Disease and providing early indicators for response to targeted treatments including immunotherapy. Furthermore, considerations regarding sample collection, processing, and data interpretation are highlighted as crucial factors influencing the reliability and reproducibility of ctDNA-based assays. Addressing these challenges will be essential for the widespread adoption of ctDNA-based liquid biopsies in routine clinical practice, ultimately paving the way toward personalized medicine and improved outcomes for patients with NSCLC.
Collapse
Affiliation(s)
- Chiara Reina
- Pancreatic Cancer Heterogeneity, Candiolo Cancer Institute FPO-IRCCS, Candiolo, Turin, Italy
| | - Berina Šabanović
- Pancreatic Cancer Heterogeneity, Candiolo Cancer Institute FPO-IRCCS, Candiolo, Turin, Italy
| | - Chiara Lazzari
- Department of Medical Oncology, Cancer Institute FPO-IRCCS, Candiolo, Turin, Italy
| | - Vanesa Gregorc
- Department of Medical Oncology, Cancer Institute FPO-IRCCS, Candiolo, Turin, Italy
| | - Christopher Heeschen
- Pancreatic Cancer Heterogeneity, Candiolo Cancer Institute FPO-IRCCS, Candiolo, Turin, Italy;.
| |
Collapse
|
6
|
Gulyas L, Glaunsinger BA. The general transcription factor TFIIB is a target for transcriptome control during cellular stress and viral infection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.16.575933. [PMID: 38746429 PMCID: PMC11092454 DOI: 10.1101/2024.01.16.575933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Many stressors, including viral infection, induce a widespread suppression of cellular RNA polymerase II (RNAPII) transcription, yet the mechanisms underlying transcriptional repression are not well understood. Here we find that a crucial component of the RNA polymerase II holoenzyme, general transcription factor IIB (TFIIB), is targeted for post-translational turnover by two pathways, each of which contribute to its depletion during stress. Upon DNA damage, translational stress, apoptosis, or replication of the oncogenic Kaposi's sarcoma-associated herpesvirus (KSHV), TFIIB is cleaved by activated caspase-3, leading to preferential downregulation of pro-survival genes. TFIIB is further targeted for rapid proteasome-mediated turnover by the E3 ubiquitin ligase TRIM28. KSHV counteracts proteasome-mediated turnover of TFIIB, thereby preserving a sufficient pool of TFIIB for transcription of viral genes. Thus, TFIIB may be a lynchpin for transcriptional outcomes during stress and a key target for nuclear replicating DNA viruses that rely on host transcriptional machinery. Significance Statement Transcription by RNA polymerase II (RNAPII) synthesizes all cellular protein-coding mRNA. Many cellular stressors and viral infections dampen RNAPII activity, though the processes underlying this are not fully understood. Here we describe a two-pronged degradation strategy by which cells respond to stress by depleting the abundance of the key RNAPII general transcription factor, TFIIB. We further demonstrate that an oncogenic human gammaherpesvirus antagonizes this process, retaining enough TFIIB to support its own robust viral transcription. Thus, modulation of RNAPII machinery plays a crucial role in dictating the outcome of cellular perturbation.
Collapse
|
7
|
Yang H, Shi Y, Lin A, Qi C, Liu Z, Cheng Q, Miao K, Zhang J, Luo P. PESSA: A web tool for pathway enrichment score-based survival analysis in cancer. PLoS Comput Biol 2024; 20:e1012024. [PMID: 38717988 PMCID: PMC11078417 DOI: 10.1371/journal.pcbi.1012024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 03/26/2024] [Indexed: 05/12/2024] Open
Abstract
The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan-Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.
Collapse
Affiliation(s)
- Hong Yang
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Haizhu District, Guangzhou, Guangdong, China
- The First School of Clinical Medicine, Southern Medical University, Baiyun District, Guangzhou, Guangdong, China
| | - Ying Shi
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Haizhu District, Guangzhou, Guangdong, China
- The Second School of Clinical Medicine, Southern Medical University, Baiyun District, Guangzhou, Guangdong, China
| | - Anqi Lin
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Haizhu District, Guangzhou, Guangdong, China
| | - Chang Qi
- Institute of Logic and Computation, TU Wien, Austria
| | - Zaoqu Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Department of Pathophysiology, Peking Union Medical College, Beijing, China
| | - Quan Cheng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Kai Miao
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau SAR, China
- MoE Frontiers Science Center for Precision Oncology, University of Macau, Macau SAR, China
| | - Jian Zhang
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Haizhu District, Guangzhou, Guangdong, China
| | - Peng Luo
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Haizhu District, Guangzhou, Guangdong, China
| |
Collapse
|
8
|
Jiang Y, Chen P, Zhao Y, Zhang Y. Association of Cadherin-Related Family Member 1 with Traumatic Brain Injury. Cell Mol Neurobiol 2024; 44:41. [PMID: 38656449 PMCID: PMC11043179 DOI: 10.1007/s10571-024-01476-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 04/02/2024] [Indexed: 04/26/2024]
Abstract
The cadherin family plays a pivotal role in orchestrating synapse formation in the central nervous system. Cadherin-related family member 1 (CDHR1) is a photoreceptor-specific calmodulin belonging to the expansive cadherin superfamily. However, its role in traumatic brain injury (TBI) remains largely unknown. CDHR1 expression across various brain tissue sites was analyzed using the GSE104687 dataset. Employing a summary-data-based Mendelian Randomization (SMR) approach, integrated analyses were performed by amalgamating genome-wide association study abstracts from TBI with public data on expressed quantitative trait loci and DNA methylation QTL from both blood and diverse brain tissues. CDHR1 expression and localization in different brain tissues were meticulously delineated using western blotting, immunohistochemistry, and enzyme-linked immunosorbent assay. CDHR1 expression was consistently elevated in the TBI group compared to that in the sham group across multiple tissues. The inflammatory response emerged as a crucial biological mechanism, and pro-inflammatory and anti-inflammatory factors were not expressed in either group. Integrated SMR analyses encompassing both blood and brain tissues substantiated the heightened CDHR1 expression profiles, with methylation modifications emerging as potential contributing factors for increased TBI risk. This was corroborated by western blotting and immunohistochemistry, confirming augmented CDHR1 expression following TBI. This multi-omics-based genetic association study highlights the elevated TBI risk associated with CDHR1 expression coupled with putative methylation modifications. These findings provide compelling evidence for future targeted investigations and offer promising avenues for developing interventional therapies for TBI.
Collapse
Affiliation(s)
- Yong'An Jiang
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
- Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Peng Chen
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
- Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
| | - YangYang Zhao
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
- Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Yan Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China.
| |
Collapse
|
9
|
Zhao S, Zhang P, Niu S, Xie J, Liu Y, Liu Y, Zhao N, Cheng C, Lu P. Targeting nucleotide metabolic pathways in colorectal cancer by integrating scRNA-seq, spatial transcriptome, and bulk RNA-seq data. Funct Integr Genomics 2024; 24:72. [PMID: 38594466 PMCID: PMC11004054 DOI: 10.1007/s10142-024-01356-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 03/21/2024] [Accepted: 04/02/2024] [Indexed: 04/11/2024]
Abstract
BACKGROUND Colorectal cancer is a malignant tumor of the digestive system originating from abnormal cell proliferation in the colon or rectum, often leading to gastrointestinal symptoms and severe health issues. Nucleotide metabolism, which encompasses the synthesis of DNA and RNA, is a pivotal cellular biochemical process that significantly impacts both the progression and therapeutic strategies of colorectal cancer METHODS: For single-cell RNA sequencing (scRNA-seq), five functions were employed to calculate scores related to nucleotide metabolism. Cell developmental trajectory analysis and intercellular interaction analysis were utilized to explore the metabolic characteristics and communication patterns of different epithelial cells. These findings were further validated using spatial transcriptome RNA sequencing (stRNA-seq). A risk model was constructed using expression profile data from TCGA and GEO cohorts to optimize clinical decision-making. Key nucleotide metabolism-related genes (NMRGs) were functionally validated by further in vitro experiments. RESULTS In both scRNA-seq and stRNA-seq, colorectal cancer (CRC) exhibited unique cellular heterogeneity, with myeloid cells and epithelial cells in tumor samples displaying higher nucleotide metabolism scores. Analysis of intercellular communication revealed enhanced signaling pathways and ligand-receptor interactions between epithelial cells with high nucleotide metabolism and fibroblasts. Spatial transcriptome sequencing confirmed elevated nucleotide metabolism states in the core region of tumor tissue. After identifying differentially expressed NMRGs in epithelial cells, a risk prognostic model based on four genes effectively predicted overall survival and immunotherapy outcomes in patients. High-risk group patients exhibited an immunosuppressive microenvironment and relatively poorer prognosis and responses to chemotherapy and immunotherapy. Finally, based on data analysis and a series of cellular functional experiments, ACOX1 and CPT2 were identified as novel therapeutic targets for CRC. CONCLUSION In this study, a comprehensive analysis of NMRGs in CRC was conducted using a combination of single-cell sequencing, spatial transcriptome sequencing, and high-throughput data. The prognostic model constructed with NMRGs shows potential as a standalone prognostic marker for colorectal cancer patients and may significantly influence the development of personalized treatment approaches for CRC.
Collapse
Affiliation(s)
- Songyun Zhao
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China
- Department of Neurosurgery, Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Pengpeng Zhang
- Department of Lung Cancer Surgery, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Sen Niu
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China
- Department of General Surgery, Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Jiaheng Xie
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Yuankun Liu
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China
- Department of Neurosurgery, Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Yuan Liu
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China
- Department of General Surgery, Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Ning Zhao
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China
| | - Chao Cheng
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China.
- Department of Neurosurgery, Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China.
| | - Peihua Lu
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China.
- Department of Clinical Research Center, Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China.
| |
Collapse
|
10
|
Hu Z, Wu Z, Liu W, Ning Y, Liu J, Ding W, Fan J, Cai S, Li Q, Li W, Yang X, Dou Y, Wang W, Peng W, Lu F, Zhuang X, Qin T, Kang X, Feng C, Xu Z, Lv Q, Wang Q, Wang C, Wang X, Wang Z, Wang J, Jiang J, Wang B, Mills GB, Ma D, Gao Q, Li K, Chen G, Chen X, Sun C. Proteogenomic insights into early-onset endometrioid endometrial carcinoma: predictors for fertility-sparing therapy response. Nat Genet 2024; 56:637-651. [PMID: 38565644 DOI: 10.1038/s41588-024-01703-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 03/05/2024] [Indexed: 04/04/2024]
Abstract
Endometrial carcinoma remains a public health concern with a growing incidence, particularly in younger women. Preserving fertility is a crucial consideration in the management of early-onset endometrioid endometrial carcinoma (EEEC), particularly in patients under 40 who maintain both reproductive desire and capacity. To illuminate the molecular characteristics of EEEC, we undertook a large-scale multi-omics study of 215 patients with endometrial carcinoma, including 81 with EEEC. We reveal an unexpected association between exposome-related mutational signature and EEEC, characterized by specific CTNNB1 and SIGLEC10 hotspot mutations and disruption of downstream pathways. Interestingly, SIGLEC10Q144K mutation in EEECs resulted in aberrant SIGLEC-10 protein expression and promoted progestin resistance by interacting with estrogen receptor alpha. We also identified potential protein biomarkers for progestin response in fertility-sparing treatment for EEEC. Collectively, our study establishes a proteogenomic resource of EEECs, uncovering the interactions between exposome and genomic susceptibilities that contribute to the development of primary prevention and early detection strategies for EEECs.
Collapse
Affiliation(s)
- Zhe Hu
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | - Zimeng Wu
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | - Wei Liu
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, P. R. China
| | - Yan Ning
- Department of Pathology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, P. R. China
| | - Jingbo Liu
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | - Wencheng Ding
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | - Junpeng Fan
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | - Shuyan Cai
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, P. R. China
| | - Qinlan Li
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | - Wenting Li
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | - Xiaohang Yang
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | - Yingyu Dou
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | - Wei Wang
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | - Wenju Peng
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | - Funian Lu
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | - Xucui Zhuang
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | - Tianyu Qin
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | - Xiaoyan Kang
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | - Chenzhao Feng
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | - Zhiying Xu
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, P. R. China
| | - Qiaoying Lv
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, P. R. China
| | - Qian Wang
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, P. R. China
| | - Chao Wang
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, P. R. China
| | - Xinyu Wang
- The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, P. R. China
| | - Zhiqi Wang
- Department of Obstetrics and Gynecology, Peking University People's Hospital; Peking University People's Hospital, Xicheng District, Beijing, P. R. China
| | - Jianliu Wang
- Department of Obstetrics and Gynecology, Peking University People's Hospital; Peking University People's Hospital, Xicheng District, Beijing, P. R. China
| | - Jie Jiang
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, P. R. China
| | - Beibei Wang
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | | | - Ding Ma
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China
| | - Qinglei Gao
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China.
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China.
| | - Kezhen Li
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China.
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China.
| | - Gang Chen
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China.
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China.
| | - Xiaojun Chen
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, P. R. China.
| | - Chaoyang Sun
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China.
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China.
| |
Collapse
|
11
|
Qiu W, Dincer AB, Janizek JD, Celik S, Pittet M, Naxerova K, Lee SI. A deep profile of gene expression across 18 human cancers. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.17.585426. [PMID: 38559197 PMCID: PMC10980029 DOI: 10.1101/2024.03.17.585426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Clinically and biologically valuable information may reside untapped in large cancer gene expression data sets. Deep unsupervised learning has the potential to extract this information with unprecedented efficacy but has thus far been hampered by a lack of biological interpretability and robustness. Here, we present DeepProfile, a comprehensive framework that addresses current challenges in applying unsupervised deep learning to gene expression profiles. We use DeepProfile to learn low-dimensional latent spaces for 18 human cancers from 50,211 transcriptomes. DeepProfile outperforms existing dimensionality reduction methods with respect to biological interpretability. Using DeepProfile interpretability methods, we show that genes that are universally important in defining the latent spaces across all cancer types control immune cell activation, while cancer type-specific genes and pathways define molecular disease subtypes. By linking DeepProfile latent variables to secondary tumor characteristics, we discover that tumor mutation burden is closely associated with the expression of cell cycle-related genes. DNA mismatch repair and MHC class II antigen presentation pathway expression, on the other hand, are consistently associated with patient survival. We validate these results through Kaplan-Meier analyses and nominate tumor-associated macrophages as an important source of survival-correlated MHC class II transcripts. Our results illustrate the power of unsupervised deep learning for discovery of novel cancer biology from existing gene expression data.
Collapse
Affiliation(s)
- Wei Qiu
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA
| | - Ayse B. Dincer
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA
| | - Joseph D. Janizek
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA
- Medical Scientist Training Program, University of Washington, Seattle, WA
| | | | - Mikael Pittet
- Department of Pathology and Immunology, University of Geneva, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Switzerland
| | - Kamila Naxerova
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Su-In Lee
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA
| |
Collapse
|
12
|
Arslan S, Schmidt J, Bass C, Mehrotra D, Geraldes A, Singhal S, Hense J, Li X, Raharja-Liu P, Maiques O, Kather JN, Pandya P. A systematic pan-cancer study on deep learning-based prediction of multi-omic biomarkers from routine pathology images. COMMUNICATIONS MEDICINE 2024; 4:48. [PMID: 38491101 PMCID: PMC10942985 DOI: 10.1038/s43856-024-00471-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 02/29/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND The objective of this comprehensive pan-cancer study is to evaluate the potential of deep learning (DL) for molecular profiling of multi-omic biomarkers directly from hematoxylin and eosin (H&E)-stained whole slide images. METHODS A total of 12,093 DL models predicting 4031 multi-omic biomarkers across 32 cancer types were trained and validated. The study included a broad range of genetic, transcriptomic, and proteomic biomarkers, as well as established prognostic markers, molecular subtypes, and clinical outcomes. RESULTS Here we show that 50% of the models achieve an area under the curve (AUC) of 0.644 or higher. The observed AUC for 25% of the models is at least 0.719 and exceeds 0.834 for the top 5%. Molecular profiling with image-based histomorphological features is generally considered feasible for most of the investigated biomarkers and across different cancer types. The performance appears to be independent of tumor purity, sample size, and class ratio (prevalence), suggesting a degree of inherent predictability in histomorphology. CONCLUSIONS The results demonstrate that DL holds promise to predict a wide range of biomarkers across the omics spectrum using only H&E-stained histological slides of solid tumors. This paves the way for accelerating diagnosis and developing more precise treatments for cancer patients.
Collapse
Affiliation(s)
| | | | | | - Debapriya Mehrotra
- Panakeia Technologies, London, UK
- Department of Pathology, Barking, Havering and Redbridge University NHS Trust, Romford, UK
| | | | - Shikha Singhal
- Panakeia Technologies, London, UK
- Department of Pathology, The Royal Wolverhampton NHS Trust, Wolverhampton, UK
| | | | - Xiusi Li
- Panakeia Technologies, London, UK
| | | | - Oscar Maiques
- Cytoskeleton and Cancer Metastasis Group, Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, UK
- Cancer Biomarkers & Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, John Vane Science Building, London, UK
| | - Jakob Nikolas Kather
- Medical Oncology, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | | |
Collapse
|
13
|
Rodriguez E, Lindijer DV, van Vliet SJ, Garcia Vallejo JJ, van Kooyk Y. The transcriptional landscape of glycosylation-related genes in cancer. iScience 2024; 27:109037. [PMID: 38384845 PMCID: PMC10879703 DOI: 10.1016/j.isci.2024.109037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 09/12/2023] [Accepted: 01/23/2024] [Indexed: 02/23/2024] Open
Abstract
Changes in glycosylation patterns have been associated with malignant transformation and clinical outcomes in several cancer types, prompting ongoing research into the mechanisms involved and potential clinical applications. In this study, we performed an extensive transcriptomic analysis of glycosylation-related genes and pathways, using publicly available bulk and single cell transcriptomic datasets from tumor samples and cancer cell lines. We identified genes and pathways strongly associated with different tumor types, which may represent novel diagnostic biomarkers. By using single cell RNA-seq data, we characterized the contribution of different cell types to the overall tumor glycosylation. Transcriptomic analysis of cancer cell lines revealed that they present a simplified landscape of genes compared to tissue. Lastly, we describe the association of different genes and pathways with the clinical outcome of patients. These results can serve as a resource for future research aimed to unravel the role of the glyco-code in cancer.
Collapse
Affiliation(s)
- Ernesto Rodriguez
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Molecular Cell Biology and Immunology, De Boelelaan 1117, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, the Netherlands
- Amsterdam Institute for Immunology and Infectious Diseases, Cancer Immunology, Amsterdam, the Netherlands
| | - Dimitri V. Lindijer
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Molecular Cell Biology and Immunology, De Boelelaan 1117, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, the Netherlands
- Amsterdam Institute for Immunology and Infectious Diseases, Cancer Immunology, Amsterdam, the Netherlands
| | - Sandra J. van Vliet
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Molecular Cell Biology and Immunology, De Boelelaan 1117, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, the Netherlands
- Amsterdam Institute for Immunology and Infectious Diseases, Cancer Immunology, Amsterdam, the Netherlands
| | - Juan J. Garcia Vallejo
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Molecular Cell Biology and Immunology, De Boelelaan 1117, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, the Netherlands
- Amsterdam Institute for Immunology and Infectious Diseases, Cancer Immunology, Amsterdam, the Netherlands
| | - Yvette van Kooyk
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Molecular Cell Biology and Immunology, De Boelelaan 1117, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, the Netherlands
- Amsterdam Institute for Immunology and Infectious Diseases, Cancer Immunology, Amsterdam, the Netherlands
| |
Collapse
|
14
|
Wei C, Deng C, Dong R, Hou Y, Wang M, Wang L, Hou T, Chen Z. Multi-omics analysis reveals critical metabolic regulators in bladder cancer. Int Urol Nephrol 2024; 56:923-934. [PMID: 37882969 DOI: 10.1007/s11255-023-03841-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 09/09/2023] [Indexed: 10/27/2023]
Abstract
BACKGROUND The crosstalk between genomic alterations and metabolic dysregulation in bladder cancer is largely unknown. A deep understanding of the interactions between cancer drivers and cancer metabolic changes will provide novel opportunities for targeted therapeutic strategies. METHODS Three primary bladder cancer specimens with paired normal tissues or blood samples were subjected to whole-exome sequencing, DNA methylation array and whole-transcriptome sequencing by next-generation sequencing technology. We applied the methods to multi-omics data combining the Cancer Genome Atlas (TCGA) bladder cancer samples, including somatic mutation, DNA copy number, DNA methylation and gene expression profile for validation. RESULTS We identified 34 mutated cancer driver genes in bladder cancer. KDM6A was the most significantly mutated cancer driver gene. Metabolic pathways were enriched in both differentially methylated regions (DMRs) and differentially expressed genes. Twenty-nine DMRs in the TSS200 region were highly correlated with the upregulation of gene expression, and 24 DMRs in the genome were highly correlated with the downregulation of gene expression. A total of 201 genes had highly correlated DNA methylation and expression. Thirty-four genes, including the known metabolic genes CXXC5, PRR5, ABCB8 and BAHD1, were further validated in the TCGA cohort. Multi-omics alterations identified two new candidate driver genes, WIPI2 and GFM2, that warrant future studies. CONCLUSIONS This study provides a comprehensive and systematic analysis, focusing on identifying key regulatory factors that may lead to cancer metabolic heterogeneity. Further understanding and verification of the cancer genes driving metabolic reprogramming and their role in the progression of bladder cancer will help to identify new therapeutic targets.
Collapse
Affiliation(s)
- Chengcheng Wei
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Changqi Deng
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Rui Dong
- Department of Urology, Hanyang Hospital of Wuhan City, Wuhan, 430050, China
| | - Yaxin Hou
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Miao Wang
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Liang Wang
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Teng Hou
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Department of Urology, South China Hospital, Medical School, Shenzhen University, Shenzhen, 518116, People's Republic of China.
| | - Zhaohui Chen
- Department of Urology, South China Hospital, Medical School, Shenzhen University, Shenzhen, 518116, People's Republic of China.
| |
Collapse
|
15
|
Menyhárt O, Győrffy B. Dietary approaches for exploiting metabolic vulnerabilities in cancer. Biochim Biophys Acta Rev Cancer 2024; 1879:189062. [PMID: 38158024 DOI: 10.1016/j.bbcan.2023.189062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 12/20/2023] [Accepted: 12/20/2023] [Indexed: 01/03/2024]
Abstract
Renewed interest in tumor metabolism sparked an enthusiasm for dietary interventions to prevent and treat cancer. Changes in diet impact circulating nutrient levels in the plasma and the tumor microenvironment, and preclinical studies suggest that dietary approaches, including caloric and nutrient restrictions, can modulate tumor initiation, progression, and metastasis. Cancers are heterogeneous in their metabolic dependencies and preferred energy sources and can be addicted to glucose, fructose, amino acids, or lipids for survival and growth. This dependence is influenced by tumor type, anatomical location, tissue of origin, aberrant signaling, and the microenvironment. This review summarizes nutrient dependencies and the related signaling pathway activations that provide targets for nutritional interventions. We examine popular dietary approaches used as adjuvants to anticancer therapies, encompassing caloric restrictions, including time-restricted feeding, intermittent fasting, fasting-mimicking diets (FMDs), and nutrient restrictions, notably the ketogenic diet. Despite promising results, much of the knowledge on dietary restrictions comes from in vitro and animal studies, which may not accurately reflect real-life situations. Further research is needed to determine the optimal duration, timing, safety, and efficacy of dietary restrictions for different cancers and treatments. In addition, well-designed human trials are necessary to establish the link between specific metabolic vulnerabilities and targeted dietary interventions. However, low patient compliance in clinical trials remains a significant challenge.
Collapse
Affiliation(s)
- Otília Menyhárt
- Semmelweis University, Department of Bioinformatics, Tűzoltó u. 7-9, H-1094 Budapest, Hungary; Research Centre for Natural Sciences, Cancer Biomarker Research Group, Institute of Enzymology, Magyar tudósok krt. 2, H-1117 Budapest, Hungary; National Laboratory for Drug Research and Development, Magyar tudósok krt. 2, H-1117 Budapest, Hungary
| | - Balázs Győrffy
- Semmelweis University, Department of Bioinformatics, Tűzoltó u. 7-9, H-1094 Budapest, Hungary; Research Centre for Natural Sciences, Cancer Biomarker Research Group, Institute of Enzymology, Magyar tudósok krt. 2, H-1117 Budapest, Hungary; National Laboratory for Drug Research and Development, Magyar tudósok krt. 2, H-1117 Budapest, Hungary.
| |
Collapse
|
16
|
Wang RY, Yang JL, Xu N, Xu J, Yang SH, Liang DM, Li JZ, Zhu H. Lipid metabolism-related long noncoding RNA RP11-817I4.1 promotes fatty acid synthesis and tumor progression in hepatocellular carcinoma. World J Gastroenterol 2024; 30:919-942. [PMID: 38516243 PMCID: PMC10950635 DOI: 10.3748/wjg.v30.i8.919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/24/2023] [Accepted: 01/27/2024] [Indexed: 02/26/2024] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is one of the most common types of tumors. The influence of lipid metabolism disruption on the development of HCC has been demonstrated in published studies. AIM To establish an HCC prognostic model for lipid metabolism-related long non-coding RNAs (LMR-lncRNAs) and conduct in-depth research on the specific role of novel LMR-lncRNAs in HCC. METHODS Correlation and differential expression analyses of The Cancer Genome Atlas data were used to identify differentially expressed LMR-lncRNAs. Quantitative real-time polymerase chain reaction analysis was used to evaluate the expression of LMR-lncRNAs. Nile red staining was employed to observe intracellular lipid levels. The interaction between RP11-817I4.1, miR-3120-3p, and ATP citrate lyase (ACLY) was validated through the performance of dual-luciferase reporter gene and RIP assays. RESULTS Three LMR-lncRNAs (negative regulator of antiviral response, RNA transmembrane and coiled-coil domain family 1 antisense RNA 1, and RP11-817I4.1) were identified as predictive markers for HCC patients and were utilized in the construction of risk models. Additionally, proliferation, migration, and invasion were reduced by RP11-817I4.1 knockdown. An increase in lipid levels in HCC cells was significantly induced by RP11-817I4.1 through the miR-3120-3p/ACLY axis. CONCLUSION LMR-lncRNAs have the capacity to predict the clinical characteristics and prognoses of HCC patients, and the discovery of a novel LMR-lncRNAs, RP11-817I4.1, revealed its role in promoting lipid accumulation, thereby accelerating the onset and progression of HCC.
Collapse
Affiliation(s)
- Ren-Yong Wang
- Second Affiliated Hospital of Kunming Medical University, Kunming 650106, Yunnan Province, China
| | - Jia-Ling Yang
- School of Basic Medical Sciences, Nanjing Medical University, Nanjing 211166, Jiangsu Province, China
| | - Ning Xu
- Second Affiliated Hospital of Kunming Medical University, Kunming 650106, Yunnan Province, China
| | - Jia Xu
- Wuhan Blood Center, Wuhan 430030, Hubei Province, China
| | - Shao-Hua Yang
- Second Affiliated Hospital of Kunming Medical University, Kunming 650106, Yunnan Province, China
| | - Dao-Ming Liang
- Second Affiliated Hospital of Kunming Medical University, Kunming 650106, Yunnan Province, China
| | - Jin-Ze Li
- Department of Gastrointestinal Surgery, The Third People's Hospital of Hubei Province, Wuhan 430071, Hubei Province, China
| | - Hong Zhu
- Second Affiliated Hospital of Kunming Medical University, Kunming 650106, Yunnan Province, China
| |
Collapse
|
17
|
Dakilah I, Harb A, Abu-Gharbieh E, El-Huneidi W, Taneera J, Hamoudi R, Semreen MH, Bustanji Y. Potential of CDC25 phosphatases in cancer research and treatment: key to precision medicine. Front Pharmacol 2024; 15:1324001. [PMID: 38313315 PMCID: PMC10834672 DOI: 10.3389/fphar.2024.1324001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/04/2024] [Indexed: 02/06/2024] Open
Abstract
The global burden of cancer continues to rise, underscoring the urgency of developing more effective and precisely targeted therapies. This comprehensive review explores the confluence of precision medicine and CDC25 phosphatases in the context of cancer research. Precision medicine, alternatively referred to as customized medicine, aims to customize medical interventions by taking into account the genetic, genomic, and epigenetic characteristics of individual patients. The identification of particular genetic and molecular drivers driving cancer helps both diagnostic accuracy and treatment selection. Precision medicine utilizes sophisticated technology such as genome sequencing and bioinformatics to elucidate genetic differences that underlie the proliferation of cancer cells, hence facilitating the development of customized therapeutic interventions. CDC25 phosphatases, which play a crucial role in governing the progression of the cell cycle, have garnered significant attention as potential targets for cancer treatment. The dysregulation of CDC25 is a characteristic feature observed in various types of malignancies, hence classifying them as proto-oncogenes. The proteins in question, which operate as phosphatases, play a role in the activation of Cyclin-dependent kinases (CDKs), so promoting the advancement of the cell cycle. CDC25 inhibitors demonstrate potential as therapeutic drugs for cancer treatment by specifically blocking the activity of CDKs and modulating the cell cycle in malignant cells. In brief, precision medicine presents a potentially fruitful option for augmenting cancer research, diagnosis, and treatment, with an emphasis on individualized care predicated upon patients' genetic and molecular profiles. The review highlights the significance of CDC25 phosphatases in the advancement of cancer and identifies them as promising candidates for therapeutic intervention. This statement underscores the significance of doing thorough molecular profiling in order to uncover the complex molecular characteristics of cancer cells.
Collapse
Affiliation(s)
- Ibraheem Dakilah
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Amani Harb
- Department of Basic Sciences, Faculty of Arts and Sciences, Al-Ahliyya Amman University, Amman, Jordan
| | - Eman Abu-Gharbieh
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Waseem El-Huneidi
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Jalal Taneera
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Rifat Hamoudi
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
- Division of Surgery and Interventional Science, University College London, London, United Kingdom
| | - Mohammed H Semreen
- College of Pharmacy, University of Sharjah, Sharjah, United Arab Emirates
| | - Yasser Bustanji
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
- School of Pharmacy, The University of Jordan, Amman, Jordan
| |
Collapse
|
18
|
Sun Y, Liu L, Fu Y, Liu Y, Gao X, Xia X, Zhu D, Wang X, Zhou X. Metabolic reprogramming involves in transition of activated/resting CD4 + memory T cells and prognosis of gastric cancer. Front Immunol 2023; 14:1275461. [PMID: 38090588 PMCID: PMC10711070 DOI: 10.3389/fimmu.2023.1275461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/09/2023] [Indexed: 12/18/2023] Open
Abstract
Background Little is known on how metabolic reprogramming potentially prompts transition of activated and resting CD4+ memory T cells infiltration in tumor microenvironment of gastric cancer (GC). The study aimed to evaluate their interactions and develop a risk model for predicting prognosis in GC. Methods Expression profiles were obtained from TCGA and GEO databases. An immunotherapeutic IMvigor210 cohort was also enrolled. CIBERSORT algorithm was used to evaluate the infiltration of immune cells. The ssGSEA method was performed to assess levels of 114 metabolism pathways. Prognosis and correlation analysis were conducted to identify metabolism pathways and genes correlated with activated CD4+ memory T cells ratio (AR) and prognosis. An AR-related metabolism gene (ARMG) risk model was constructed and validated in different cohorts. Flow cytometry was applied to validate the effect of all-trans retinoic acid (ATRA) on CD4+ memory T cells. Results Since significantly inverse prognostic value and negative correlation of resting and activated CD4+ memory T cells, high AR level was associated with favorable overall survival (OS) in GC. Meanwhile, 15 metabolism pathways including retinoic acid metabolism pathway were significantly correlated with AR and prognosis. The ARMG risk model could classify GC patients with different outcomes, treatment responses, genomic and immune landscape. The prognostic value of the model was also confirmed in the additional validation, immunotherapy and pan-cancer cohorts. Functional analyses revealed that the ARMG model was positively correlated with pro-tumorigenic pathways. In vitro experiments showed that ATRA could inhibit levels of activated CD4+ memory T cells and AR. Conclusion Our study showed that metabolic reprogramming including retinoic acid metabolism could contribute to transition of activated and resting CD4+ memory T cells, and affect prognosis of GC patients. The ARMG risk model could serve as a new tool for GC patients by accurately predicting prognosis and response to treatment.
Collapse
Affiliation(s)
- Yue Sun
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Li Liu
- Department of Gynecology, Shunde Women and Children’s Hospital (Maternity and Child Healthcare Hospital of Shunde Foshan), Guangdong Medical University, Foshan, Guangdong, China
| | - Yuanyuan Fu
- Department of Pharmacy, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yaoyao Liu
- Department of Translational Medicine, Beijing GenePlus Genomics Institute, Beijing, China
| | - Xuan Gao
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
- Department of Translational Medicine, Shenzhen GenePlus Clinical Laboratory, Shenzhen, China
| | - Xuefeng Xia
- Department of Translational Medicine, Beijing GenePlus Genomics Institute, Beijing, China
| | - Dajian Zhu
- Department of Gastroenterological Surgery, Shunde Women and Children’s Hospital (Maternity and Child Healthcare Hospital of Shunde Foshan), Guangdong Medical University, Foshan, China
| | - Xiaping Wang
- Department of Pathology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xin Zhou
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Oncology Center, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
| |
Collapse
|
19
|
Zou M, Li H, Su D, Xiong Y, Wei H, Wang S, Sun H, Wang T, Xi Q, Zuo Y, Yang L. Integrating somatic mutation profiles with structural deep clustering network for metabolic stratification in pancreatic cancer: a comprehensive analysis of prognostic and genomic landscapes. Brief Bioinform 2023; 25:bbad430. [PMID: 38040491 PMCID: PMC10783866 DOI: 10.1093/bib/bbad430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/29/2023] [Accepted: 11/05/2023] [Indexed: 12/03/2023] Open
Abstract
Pancreatic cancer is a globally recognized highly aggressive malignancy, posing a significant threat to human health and characterized by pronounced heterogeneity. In recent years, researchers have uncovered that the development and progression of cancer are often attributed to the accumulation of somatic mutations within cells. However, cancer somatic mutation data exhibit characteristics such as high dimensionality and sparsity, which pose new challenges in utilizing these data effectively. In this study, we propagated the discrete somatic mutation data of pancreatic cancer through a network propagation model based on protein-protein interaction networks. This resulted in smoothed somatic mutation profile data that incorporate protein network information. Based on this smoothed mutation profile data, we obtained the activity levels of different metabolic pathways in pancreatic cancer patients. Subsequently, using the activity levels of various metabolic pathways in cancer patients, we employed a deep clustering algorithm to establish biologically and clinically relevant metabolic subtypes of pancreatic cancer. Our study holds scientific significance in classifying pancreatic cancer based on somatic mutation data and may provide a crucial theoretical basis for the diagnosis and immunotherapy of pancreatic cancer patients.
Collapse
Affiliation(s)
- Min Zou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Honghao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Dongqing Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yuqiang Xiong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Haodong Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shiyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Hongmei Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Tao Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Qilemuge Xi
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yongchun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
- Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd. Hohhot 010010, China
- Inner Mongolia International Mongolian Hospital, Hohhot 010065, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| |
Collapse
|
20
|
Yamashita N, Withers H, Morimoto Y, Bhattacharya A, Haratake N, Daimon T, Fushimi A, Nakashoji A, Thorner AR, Isenhart E, Rosario S, Long MD, Kufe D. MUC1-C integrates aerobic glycolysis with suppression of oxidative phosphorylation in triple-negative breast cancer stem cells. iScience 2023; 26:108168. [PMID: 37915591 PMCID: PMC10616323 DOI: 10.1016/j.isci.2023.108168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/17/2023] [Accepted: 10/05/2023] [Indexed: 11/03/2023] Open
Abstract
Activation of the MUC1-C protein promotes lineage plasticity, epigenetic reprogramming, and the cancer stem cell (CSC) state. The present studies performed on enriched populations of triple-negative breast cancer (TNBC) CSCs demonstrate that MUC1-C is essential for integrating activation of glycolytic pathway genes with self-renewal and tumorigenicity. MUC1-C further integrates the glycolytic pathway with suppression of mitochondrial DNA (mtDNA) genes encoding components of mitochondrial Complexes I-V. The repression of mtDNA genes is explained by MUC1-C-mediated (i) downregulation of the mitochondrial transcription factor A (TFAM) required for mtDNA transcription and (ii) induction of the mitochondrial transcription termination factor 3 (mTERF3). In support of pathogenesis that suppresses mitochondrial ROS production, targeting MUC1-C increases (i) mtDNA gene transcription, (ii) superoxide levels, and (iii) loss of self-renewal capacity. These findings and scRNA-seq analysis of CSC subpopulations indicate that MUC1-C regulates self-renewal and redox balance by integrating activation of glycolysis with suppression of oxidative phosphorylation.
Collapse
Affiliation(s)
- Nami Yamashita
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Henry Withers
- Department of Biostatistics & Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | | | | | - Naoki Haratake
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Tatsuaki Daimon
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Atsushi Fushimi
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Ayako Nakashoji
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Aaron R. Thorner
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Emily Isenhart
- Department of Biostatistics & Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Spencer Rosario
- Department of Biostatistics & Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Mark D. Long
- Department of Biostatistics & Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Donald Kufe
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
21
|
Iqbal MA, Siddiqui S, Smith K, Singh P, Kumar B, Chouaib S, Chandrasekaran S. Metabolic stratification of human breast tumors reveal subtypes of clinical and therapeutic relevance. iScience 2023; 26:108059. [PMID: 37854701 PMCID: PMC10579441 DOI: 10.1016/j.isci.2023.108059] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 07/17/2023] [Accepted: 09/22/2023] [Indexed: 10/20/2023] Open
Abstract
Extensive metabolic heterogeneity in breast cancers has limited the deployment of metabolic therapies. To enable patient stratification, we studied the metabolic landscape in breast cancers (∼3000 patients combined) and identified three subtypes with increasing degrees of metabolic deregulation. Subtype M1 was found to be dependent on bile-acid biosynthesis, whereas M2 showed reliance on methionine pathway, and M3 engaged fatty-acid, nucleotide, and glucose metabolism. The extent of metabolic alterations correlated strongly with tumor aggressiveness and patient outcome. This pattern was reproducible in independent datasets and using in vivo tumor metabolite data. Using machine-learning, we identified robust and generalizable signatures of metabolic subtypes in tumors and cell lines. Experimental inhibition of metabolic pathways in cell lines representing metabolic subtypes revealed subtype-specific sensitivity, therapeutically relevant drugs, and promising combination therapies. Taken together, metabolic stratification of breast cancers can thus aid in predicting patient outcome and designing precision therapies.
Collapse
Affiliation(s)
- Mohammad A. Iqbal
- Thumbay Research Institute for Precision Medicine, Gulf Medical University, Ajman, United Arab Emirates
- College of Medicine, Gulf Medical University, Ajman, United Arab Emirates
| | | | - Kirk Smith
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Prithvi Singh
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia (A Central University), New Delhi, India
| | - Bhupender Kumar
- Department of Microbiology, Swami Shraddhanand College, University of Delhi, New Delhi, Delhi, India
| | - Salem Chouaib
- Thumbay Research Institute for Precision Medicine, Gulf Medical University, Ajman, United Arab Emirates
- College of Medicine, Gulf Medical University, Ajman, United Arab Emirates
- INSERM UMR 1186, Gustave Roussy, EPHE, Faculty of Medicine, University of Paris-Saclay, Villejuif, France
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA
| |
Collapse
|
22
|
Bailleux C, Chardin D, Guigonis JM, Ferrero JM, Chateau Y, Humbert O, Pourcher T, Gal J. Survival analysis of patient groups defined by unsupervised machine learning clustering methods based on patient metabolomic data. Comput Struct Biotechnol J 2023; 21:5136-5143. [PMID: 37920813 PMCID: PMC10618114 DOI: 10.1016/j.csbj.2023.10.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 10/16/2023] [Accepted: 10/16/2023] [Indexed: 11/04/2023] Open
Abstract
Purpose Meta-analyses failed to accurately identify patients with non-metastatic breast cancer who are likely to benefit from chemotherapy, and metabolomics could provide new answers. In our previous published work, patients were clustered using five different unsupervised machine learning (ML) methods resulting in the identification of three clusters with distinct clinical and simulated survival data. The objective of this study was to evaluate the survival outcomes, with extended follow-up, using the same 5 different methods of unsupervised machine learning. Experimental design Forty-nine patients, diagnosed between 2013 and 2016, with non-metastatic BC were included retrospectively. Median follow-up was extended to 85.8 months. 449 metabolites were extracted from tumor resection samples by combined Liquid chromatography-mass spectrometry (LC-MS). Survival analyses were reported grouping together Cluster 1 and 2 versus cluster 3. Bootstrap optimization was applied. Results PCA k-means, K-sparse and Spectral clustering were the most effective methods to predict 2-year progression-free survival with bootstrap optimization (PFSb); as bootstrap example, with PCA k-means method, PFSb were 94% for cluster 1&2 versus 82% for cluster 3 (p = 0.01). PCA k-means method performed best, with higher reproducibility (mean HR=2 (95%CI [1.4-2.7]); probability of p ≤ 0.05 85%). Cancer-specific survival (CSS) and overall survival (OS) analyses highlighted a discrepancy between the 5 ML unsupervised methods. Conclusion Our study is a proof-of-principle that it is possible to use unsupervised ML methods on metabolomic data to predict PFS survival outcomes, with the best performance for PCA k-means. A larger population study is needed to draw conclusions from CSS and OS analyses.
Collapse
Affiliation(s)
- Caroline Bailleux
- University Côte d′Azur, Centre Antoine Lacassagne, Medical Oncology Department, Nice F-06189, France
- University Côte d′Azur, Commissariat à l′Energie Atomique et aux énergies alternatives, Institut Frédéric Joliot, Service Hospitalier Frédéric Joliot, laboratory Transporters in Oncology and Radiotherapy in Oncology (TIRO), School of medicine, Nice F-06100, France
| | - David Chardin
- University Côte d′Azur, Commissariat à l′Energie Atomique et aux énergies alternatives, Institut Frédéric Joliot, Service Hospitalier Frédéric Joliot, laboratory Transporters in Oncology and Radiotherapy in Oncology (TIRO), School of medicine, Nice F-06100, France
- University Côte d′Azur, Centre Antoine Lacassagne, Nuclear medicine Department, Nice F-06189, France
| | - Jean-Marie Guigonis
- University Côte d′Azur, Commissariat à l′Energie Atomique et aux énergies alternatives, Institut Frédéric Joliot, Service Hospitalier Frédéric Joliot, laboratory Transporters in Oncology and Radiotherapy in Oncology (TIRO), School of medicine, Nice F-06100, France
| | - Jean-Marc Ferrero
- University Côte d′Azur, Centre Antoine Lacassagne, Medical Oncology Department, Nice F-06189, France
| | - Yann Chateau
- University Côte d′Azur, Centre Antoine Lacassagne, Epidemiology and Biostatistics Department, Nice F-06189, France
| | - Olivier Humbert
- University Côte d′Azur, Commissariat à l′Energie Atomique et aux énergies alternatives, Institut Frédéric Joliot, Service Hospitalier Frédéric Joliot, laboratory Transporters in Oncology and Radiotherapy in Oncology (TIRO), School of medicine, Nice F-06100, France
- University Côte d′Azur, Centre Antoine Lacassagne, Nuclear medicine Department, Nice F-06189, France
| | - Thierry Pourcher
- University Côte d′Azur, Commissariat à l′Energie Atomique et aux énergies alternatives, Institut Frédéric Joliot, Service Hospitalier Frédéric Joliot, laboratory Transporters in Oncology and Radiotherapy in Oncology (TIRO), School of medicine, Nice F-06100, France
| | - Jocelyn Gal
- University Côte d′Azur, Centre Antoine Lacassagne, Epidemiology and Biostatistics Department, Nice F-06189, France
| |
Collapse
|
23
|
Jannin A, Dessein AF, Do Cao C, Vantyghem MC, Chevalier B, Van Seuningen I, Jonckheere N, Coppin L. Metabolism of pancreatic neuroendocrine tumors: what can omics tell us? Front Endocrinol (Lausanne) 2023; 14:1248575. [PMID: 37908747 PMCID: PMC10613989 DOI: 10.3389/fendo.2023.1248575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/27/2023] [Indexed: 11/02/2023] Open
Abstract
Introduction Reprogramming of cellular metabolism is now a hallmark of tumorigenesis. In recent years, research on pancreatic neuroendocrine tumors (pNETs) has focused on genetic and epigenetic modifications and related signaling pathways, but few studies have been devoted to characterizing the metabolic profile of these tumors. In this review, we thoroughly investigate the metabolic pathways in pNETs by analyzing the transcriptomic and metabolomic data available in the literature. Methodology We retrieved and downloaded gene expression profiles from all publicly available gene set enrichments (GSE43797, GSE73338, and GSE117851) to compare the differences in expressed genes based on both the stage and MEN1 mutational status. In addition, we conducted a systematic review of metabolomic data in NETs. Results By combining transcriptomic and metabolomic approaches, we have identified a distinctive metabolism in pNETs compared with controls without pNETs. Our analysis showed dysregulations in the one-carbon, glutathione, and polyamine metabolisms, fatty acid biosynthesis, and branched-chain amino acid catabolism, which supply the tricarboxylic acid cycle. These targets are implicated in pNET cell proliferation and metastasis and could also have a prognostic impact. When analyzing the profiles of patients with or without metastasis, or with or without MEN1 mutation, we observed only a few differences due to the scarcity of published clinical data in the existing research. Consequently, further studies are now necessary to validate our data and investigate these potential targets as biomarkers or therapeutic solutions, with a specific focus on pNETs.
Collapse
Affiliation(s)
- Arnaud Jannin
- Univ. Lille, CNRS, Inserm, CHU Lille, UMR9020-U1277 - CANTHER - Cancer - Heterogeneity Plasticity and Resistance to Therapies, Lille, France
- CHU Lille, Department of Endocrinology, Diabetology, and Metabolism, Lille, France
| | - Anne-Frédérique Dessein
- Univ. Lille, CNRS, Inserm, CHU Lille, UMR9020-U1277 - CANTHER - Cancer - Heterogeneity Plasticity and Resistance to Therapies, Lille, France
| | - Christine Do Cao
- CHU Lille, Department of Endocrinology, Diabetology, and Metabolism, Lille, France
| | | | | | - Isabelle Van Seuningen
- Univ. Lille, CNRS, Inserm, CHU Lille, UMR9020-U1277 - CANTHER - Cancer - Heterogeneity Plasticity and Resistance to Therapies, Lille, France
| | - Nicolas Jonckheere
- Univ. Lille, CNRS, Inserm, CHU Lille, UMR9020-U1277 - CANTHER - Cancer - Heterogeneity Plasticity and Resistance to Therapies, Lille, France
| | - Lucie Coppin
- Univ. Lille, CNRS, Inserm, CHU Lille, UMR9020-U1277 - CANTHER - Cancer - Heterogeneity Plasticity and Resistance to Therapies, Lille, France
| |
Collapse
|
24
|
Wang B, Ge S, Wang Z, Wang W, Wang Y, Leng H, Ma X. Analysis and experimental validation of fatty acid metabolism-related genes prostacyclin synthase (PTGIS) in endometrial cancer. Aging (Albany NY) 2023; 15:10322-10346. [PMID: 37796199 PMCID: PMC10599728 DOI: 10.18632/aging.205080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/09/2023] [Indexed: 10/06/2023]
Abstract
The deregulation of fatty acid metabolism plays a pivotal role in cancer. Our objective is to construct a prognostic model for patients with endometrial carcinoma (EC) based on genes related to fatty acid metabolism-related genes (FAMGs). RNA sequencing and clinical data for EC were obtained from The Cancer Genome Atlas (TCGA). Lasso-Penalized Cox regression was employed to derive the risk formula for the model, the score = esum(corresponding coefficient × each gene's expression). Gene set enrichment analysis (GSEA) was utilized to examine the enrichment of KEGG and GO pathways within this model. Correlation analysis of immune function was conducted using Single-sample GSEA (ssGSEA). The "ESTIMATE" package in R was utilized to evaluate the tumor microenvironment. The support vector machine recursive feature elimination (SVM-RFE) and randomforest maps were employed to identify key genes. The effects of PTGIS on the malignant biological behavior of EC were assessed through CCK-8 assay, transwell invasion assay, cell cycle analysis, apoptosis assay, and tumor xenografts in nude mice. A novel prognostic signature comprising 10 FAMGs (INMT, ACACB, ACOT4, ACOXL, CYP4F3, FAAH, GPX1, HPGDS, PON3, PTGIS) was developed. This risk score serves as an independent prognostic marker validated for EC. According to ssGSEA analysis, the low- and high-risk groups exhibited distinct immune enrichments. The key gene PTGIS was screened by SVM-RFE and randomforest method. Furthermore, we validated the expression of PTGIS through qRT-PCR. In vitro and in vivo experiments also confirmed the effect of PTGIS on the malignant biological behavior of EC.
Collapse
Affiliation(s)
- Bo Wang
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang 110000, Liaoning, People’s Republic of China
| | - Shuwen Ge
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang 110000, Liaoning, People’s Republic of China
| | - Zihao Wang
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang 110000, Liaoning, People’s Republic of China
| | - Wantong Wang
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang 110000, Liaoning, People’s Republic of China
| | - Yuting Wang
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang 110000, Liaoning, People’s Republic of China
| | - Hongrui Leng
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang 110000, Liaoning, People’s Republic of China
| | - Xiaoxin Ma
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang 110000, Liaoning, People’s Republic of China
| |
Collapse
|
25
|
Tan J, Shu M, Liao J, Liang R, Liu S, Kuang M, Peng S, Xiao H, Zhou Q. Identification and validation of a plasma metabolomics-based model for risk stratification of intrahepatic cholangiocarcinoma. J Cancer Res Clin Oncol 2023; 149:12365-12377. [PMID: 37436513 DOI: 10.1007/s00432-023-05119-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/04/2023] [Indexed: 07/13/2023]
Abstract
BACKGROUND Liver resection is the mainstay of curative treatment for intrahepatic cholangiocarcinoma (ICC) while the postoperative prognosis varies greatly, with no recognized biomarker. We aimed to identify the plasma metabolomic biomarkers that could be used for preoperative risk stratification of ICC patients. METHODS 108 eligible ICC patients who underwent radical surgical resection between August 2012 and October 2020 were enrolled. Patients were randomly divided into a discovery cohort (n = 76) and a validation cohort (n = 32) by 7:3. Metabolomics profiling of preoperative plasma was performed and clinical data were collected. The least absolute shrinkage and selection operator (LASSO) regression, Cox regression, and receiver operating characteristic (ROC) analyses were used to screen and validate the survival-related metabolic biomarker panel and construct a LASSO-Cox prediction model. RESULTS 10 survival-related metabolic biomarkers were used for construction of a LASSO-Cox prediction model. In the discovery and validation cohorts, the LASSO-Cox prediction model achieved an AUC of 0.876 (95%CI: 0.777-0.974) and 0.860 (95%CI: 0.711-1.000) in evaluating 1-year OS of ICC patients, respectively. The OS of ICC patients in the high-risk group was significantly worse than that in the low-risk group (discovery cohort, p < 0.0001; validation cohort: p = 0.041). Also, the LASSO-Cox risk score (HR 2.43, 95%CI: 1.81-3.26, p < 0.0001) was a significant independent risk factor associated with OS. CONCLUSIONS The LASSO-Cox prediction model has potential as an important tool in evaluating the OS of ICC patients after surgical resection and can be used as prediction tools to implement the best treatment options that could result in better outcomes.
Collapse
Affiliation(s)
- Jiehui Tan
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
| | - Man Shu
- Department of Pathology, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
| | - Junbin Liao
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
| | - Ruiming Liang
- Department of Medical Statistics, Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
| | - Shiyi Liu
- Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
| | - Ming Kuang
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
- Zhongshan School of Medicine, Sun Yat-Sen University, No. 58, Zhongshan Road 2, 510080, Guangzhou, People's Republic of China
| | - Sui Peng
- Department of Medical Statistics, Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
- Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
| | - Han Xiao
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Road 2, 510080, Guangzhou, People's Republic of China.
| | - Qian Zhou
- Department of Medical Statistics, Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China.
- Zhongshan School of Medicine, Sun Yat-Sen University, No. 58, Zhongshan Road 2, 510080, Guangzhou, People's Republic of China.
| |
Collapse
|
26
|
Estrada-Pérez AR, García-Vázquez JB, Mendoza-Figueroa HL, Rosales-Hernández MC, Fernández-Pomares C, Correa-Basurto J. Untargeted LC-MS/MS Metabolomics Study of HO-AAVPA and VPA on Breast Cancer Cell Lines. Int J Mol Sci 2023; 24:14543. [PMID: 37833990 PMCID: PMC10572250 DOI: 10.3390/ijms241914543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 09/05/2023] [Accepted: 09/06/2023] [Indexed: 10/15/2023] Open
Abstract
Breast cancer (BC) is one of the biggest health problems worldwide, characterized by intricate metabolic and biochemical complexities stemming from pronounced variations across dysregulated molecular pathways. If BC is not diagnosed early, complications may lead to death. Thus, the pursuit of novel therapeutic avenues persists, notably focusing on epigenetic pathways such as histone deacetylases (HDACs). The compound N-(2-hydroxyphenyl)-2-propylpentanamide (HO-AAVPA), a derivative of valproic acid (VPA), has emerged as a promising candidate warranting pre-clinical investigation. HO-AAVPA is an HDAC inhibitor with antiproliferative effects on BC, but its molecular mechanism has yet to be deciphered. Furthermore, in the present study, we determined the metabolomic effects of HO-AAVPA and VPA on cells of luminal breast cancer (MCF-7) and triple-negative breast cancer (MDA-MB-231) subtypes. The LC-MS untargeted metabolomic study allowed for the simultaneous measurement of multiple metabolites and pathways, identifying that both compounds affect glycerophospholipid and sphingolipid metabolism in the MCF-7 and MDA-MB-231 cell lines, suggesting that other biological targets were different from HDACs. In addition, there are different dysregulate metabolites, possibly due to the physicochemical differences between HO-AAVPA and VPA.
Collapse
Affiliation(s)
- Alan Rubén Estrada-Pérez
- Laboratorio de Diseño y Desarrollo de Nuevos Fármacos e Innovación Biotecnológica, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de México 11340, Mexico
| | - Juan Benjamín García-Vázquez
- Laboratorio de Diseño y Desarrollo de Nuevos Fármacos e Innovación Biotecnológica, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de México 11340, Mexico
| | - Humberto L. Mendoza-Figueroa
- Laboratorio de Diseño y Desarrollo de Nuevos Fármacos e Innovación Biotecnológica, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de México 11340, Mexico
| | - Martha Cecilia Rosales-Hernández
- Laboratorio de Biofísica y Biocatálisis, Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de México 11340, Mexico
| | - Cynthia Fernández-Pomares
- Laboratorio de Diseño y Desarrollo de Nuevos Fármacos e Innovación Biotecnológica, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de México 11340, Mexico
| | - José Correa-Basurto
- Laboratorio de Diseño y Desarrollo de Nuevos Fármacos e Innovación Biotecnológica, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de México 11340, Mexico
| |
Collapse
|
27
|
Tian S, Li Y, Xu J, Zhang L, Zhang J, Lu J, Xu X, Luan X, Zhao J, Zhang W. COIMMR: a computational framework to reveal the contribution of herbal ingredients against human cancer via immune microenvironment and metabolic reprogramming. Brief Bioinform 2023; 24:bbad346. [PMID: 37816138 PMCID: PMC10564268 DOI: 10.1093/bib/bbad346] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/16/2023] [Accepted: 09/13/2023] [Indexed: 10/12/2023] Open
Abstract
Immune evasion and metabolism reprogramming have been regarded as two vital hallmarks of the mechanism of carcinogenesis. Thus, targeting the immune microenvironment and the reprogrammed metabolic processes will aid in developing novel anti-cancer drugs. In recent decades, herbal medicine has been widely utilized to treat cancer through the modulation of the immune microenvironment and reprogrammed metabolic processes. However, labor-based herbal ingredient screening is time consuming, laborious and costly. Luckily, some computational approaches have been proposed to screen candidates for drug discovery rapidly. Yet, it has been challenging to develop methods to screen drug candidates exclusively targeting specific pathways, especially for herbal ingredients which exert anti-cancer effects by multiple targets, multiple pathways and synergistic ways. Meanwhile, currently employed approaches cannot quantify the contribution of the specific pathway to the overall curative effect of herbal ingredients. Hence, to address this problem, this study proposes a new computational framework to infer the contribution of the immune microenvironment and metabolic reprogramming (COIMMR) in herbal ingredients against human cancer and specifically screen herbal ingredients targeting the immune microenvironment and metabolic reprogramming. Finally, COIMMR was applied to identify isoliquiritigenin that specifically regulates the T cells in stomach adenocarcinoma and cephaelin hydrochloride that specifically targets metabolic reprogramming in low-grade glioma. The in silico results were further verified using in vitro experiments. Taken together, our approach opens new possibilities for repositioning drugs targeting immune and metabolic dysfunction in human cancer and provides new insights for drug development in other diseases. COIMMR is available at https://github.com/LYN2323/COIMMR.
Collapse
Affiliation(s)
- Saisai Tian
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Yanan Li
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Jia Xu
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
- College of Pharmacy, Henan University, Kaifeng 475000, China
| | - Lijun Zhang
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine
| | - Jinbo Zhang
- Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China Department of Pharmacy, Tianjin Rehabilitation Center of Joint Logistics Support Force, Tianjin, 300110, China
| | - Jinyuan Lu
- College of Pharmacy, Anhui University of Chinese Medicine, Anhui 230012, China
| | - Xike Xu
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Xin Luan
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine
| | - Jing Zhao
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine
| | - Weidong Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine
| |
Collapse
|
28
|
Li L, Wu N, Zhuang G, Geng L, Zeng Y, Wang X, Wang S, Ruan X, Zheng X, Liu J, Gao M. Heterogeneity and potential therapeutic insights for triple-negative breast cancer based on metabolic-associated molecular subtypes and genomic mutations. Front Pharmacol 2023; 14:1224828. [PMID: 37719859 PMCID: PMC10502304 DOI: 10.3389/fphar.2023.1224828] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 08/21/2023] [Indexed: 09/19/2023] Open
Abstract
Objective: Due to a lack of effective therapy, triple-negative breast cancer (TNBC) is extremely poor prognosis. Metabolic reprogramming is an important hallmark in tumorigenesis, cancer diagnosis, prognosis, and treatment. Categorizing metabolic patterns in TNBC is critical to combat heterogeneity and targeted therapeutics. Methods: 115 TNBC patients from TCGA were combined into a virtual cohort and verified by other verification sets, discovering differentially expressed genes (DEGs). To identify reliable metabolic features, we applied the same procedures to five independent datasets to verify the identified TNBC subtypes, which differed in terms of prognosis, metabolic characteristics, immune infiltration, clinical features, somatic mutation, and drug sensitivity. Results: In general, TNBC could be classified into two metabolically distinct subtypes. C1 had high immune checkpoint genes expression and immune and stromal scores, demonstrating sensitivity to the treatment of PD-1 inhibitors. On the other hand, C2 displayed a high variation in metabolism pathways involved in carbohydrate, lipid, and amino acid metabolism. More importantly, C2 was a lack of immune signatures, with late pathological stage, low immune infiltration and poor prognosis. Interestingly, C2 had a high mutation frequency in PIK3CA, KMT2D, and KMT2C and displayed significant activation of the PI3K and angiogenesis pathways. As a final output, we created a 100-gene classifier to reliably differentiate the TNBC subtypes and AKR1B10 was a potential biomarker for C2 subtypes. Conclusion: In conclusion, we identified two subtypes with distinct metabolic phenotypes, provided novel insights into TNBC heterogeneity, and provided a theoretical foundation for therapeutic strategies.
Collapse
Affiliation(s)
- Lijuan Li
- Department of Cancer Prevention Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Nan Wu
- Department of Cancer Prevention Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Gaojian Zhuang
- The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Qingyuan, China
| | - Lin Geng
- Department of Thyroid and Neck Tumor, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Yu Zeng
- Department of Thyroid and Neck Tumor, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Xuan Wang
- Department of Phase I Clinical Trial, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Shuang Wang
- Department of Cancer Prevention Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Xianhui Ruan
- Department of Thyroid and Neck Tumor, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Xiangqian Zheng
- Department of Thyroid and Neck Tumor, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Juntian Liu
- Department of Cancer Prevention Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Ming Gao
- Department of Thyroid and Neck Tumor, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Thyroid and Breast Surgery, Tianjin Union Medical Center, Tianjin Key Laboratory of General Surgery in construction, Tianjin Union Medical Center, Tianjin, China
| |
Collapse
|
29
|
Chen J, Wang Y, Jiang H. Features of metabolism associated molecular patterns in pancreatic ductal adenocarcinoma. Cancer Gene Ther 2023; 30:1296-1307. [PMID: 37414853 DOI: 10.1038/s41417-023-00639-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 05/24/2023] [Accepted: 06/21/2023] [Indexed: 07/08/2023]
Abstract
Exploring pancreatic ductal adenocarcinoma (PDAC) metabolic landscape would contribute to further understand PDAC from the metabolic perspective and provide more details for precise treatment design. This study aims to describe metabolic landscape of PDAC. Bioinformatics analysis was used to investigate the differences of genome, transcriptome, and proteome levels of metabolic patterns. Three subtypes (MC1, MC2, and MC3) were identified and characterized as distinct metabolic patterns. MC1, enriched in lipid metabolism and amino acid metabolism signatures, was associated with lower abundance of immune cells and stromal cells, and non-response to immunotherapy. MC2 displayed immune-activated characteristics, minor genome alterations and good response to immunotherapy. MC3 was characterized by high glucose metabolism, high pathological grade, immune-suppressed features, poor prognosis, and epithelial-mesenchymal transition phenotype. A ninety-three gene classifier preformed robust prediction and high accuracy (training set: 93.7%; validation set 1: 85.0%; validation set 2: 83.9%). Using random forest classifier, probabilities of three patterns could be predicted on pancreatic cancer cell lines, which could be used to find vulnerable targets in response to both genetic and drug perturbation. Our study revealed features of PDAC metabolic landscape, which could be expected to provide a reference for prognosis prediction and precise treatment design.
Collapse
Affiliation(s)
- Junfei Chen
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, 200032, Shanghai, China
| | - Yongjie Wang
- Department of Clinical Laboratory, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hua Jiang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, 200032, Shanghai, China.
| |
Collapse
|
30
|
Odongo R, Bellur O, Abdik E, Çakır T. Brain-wide transcriptome-based metabolic alterations in Parkinson's disease: human inter-region and human-experimental model correlations. Mol Omics 2023; 19:522-537. [PMID: 36928892 DOI: 10.1039/d2mo00343k] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Alterations in brain metabolism are closely associated with the molecular hallmarks of Parkinson's disease (PD). A clear understanding of the main metabolic perturbations in PD is therefore important. Here, we retrospectively analysed the expression of metabolic genes from 34 PD-control post-mortem human brain transcriptome data comparisons from literature, spanning multiple brain regions. We found high metabolic correlations between the Substantia nigra (SN)- and cerebral cortex-derived tissues. Moreover, three clusters of PD patient cohorts were identified based on perturbed metabolic processes in the SN - each characterised by perturbations in (a) bile acid metabolism (b) omega-3 fatty acid metabolism, and (c) lipoic acid and androgen metabolism - metabolic themes not comprehensively addressed in PD. These perturbations were supported by concurrence between transcriptome and proteome changes in the expression patterns for CBR1, ECI2, BDH2, CYP27A1, ALDH1B1, ALDH9A1, ADH5, ALDH7A1, L1CAM, and PLXNB3 genes, providing a valuable resource for drug targeting and diagnosis. Also, we analysed 58 PD-control transcriptome data comparisons from in vivo/in vitro disease models and identified experimental PD models with significant correlations to matched human brain regions. Collectively, our findings suggest metabolic alterations in several brain regions, heterogeneity in metabolic alterations between study cohorts for the SN tissues and the need to optimize current experimental models to advance research on metabolic aspects of PD.
Collapse
Affiliation(s)
- Regan Odongo
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey.
| | - Orhan Bellur
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey.
| | - Ecehan Abdik
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey.
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey.
| |
Collapse
|
31
|
Huang Y, Mohanty V, Dede M, Tsai K, Daher M, Li L, Rezvani K, Chen K. Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux. Nat Commun 2023; 14:4883. [PMID: 37573313 PMCID: PMC10423258 DOI: 10.1038/s41467-023-40457-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 07/26/2023] [Indexed: 08/14/2023] Open
Abstract
Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux (METAbolic Flux balance analysis) to infer metabolic fluxes from bulk or single-cell transcriptomic data. Large-scale experiments using cell-lines, the cancer genome atlas (TCGA), and scRNA-seq data obtained from diverse cancer and immunotherapeutic contexts, including CAR-NK cell therapy, have validated METAFlux's capability to characterize metabolic heterogeneity and metabolic interaction amongst cell types.
Collapse
Affiliation(s)
- Yuefan Huang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
- Department of Biostatistics & Data Science, School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, 77030, USA
| | - Vakul Mohanty
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Merve Dede
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Kyle Tsai
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - May Daher
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Li Li
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Katayoun Rezvani
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
| |
Collapse
|
32
|
Liu Y, Qu HQ, Chang X, Mentch FD, Qiu H, Wang X, Saeidian AH, Watson D, Glessner J, Hakonarson H. Genomic variants exclusively identified in children with birth defects and concurrent malignant tumors predispose to cancer development. Mol Cancer 2023; 22:126. [PMID: 37543594 PMCID: PMC10403830 DOI: 10.1186/s12943-023-01828-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/18/2023] [Indexed: 08/07/2023] Open
Abstract
Children with birth defects (BD) express distinct clinical features that often have various medical consequences, one of which is predisposition to the development of cancers. Identification of the underlying genetic mechanisms related to the development of cancer in BD patients would allow for preventive measures. We performed a whole genome sequencing (WGS) study on blood-derived DNA samples from 1566 individuals without chromosomal anomalies, including 454 BD probands with at least one type of malignant tumors, 767 cancer-free BD probands, and 345 healthy individuals. Exclusive recurrent variants were identified in BD-cancer and BD-only patients and mapped to their corresponding genomic regions. We observed statistically significant overlaps for protein-coding/ncRNA with exclusive variants in exons, introns, ncRNAs, and 3'UTR regions. Exclusive exonic variants, especially synonymous variants, tend to occur in prior exons locus in BD-cancer children. Intronic variants close to splicing site (< 500 bp from exon) have little overlaps in BD-cancer and BD-only patients. Exonic variants in non-coding RNA (ncRNA) tend to occur in different ncRNAs exons regardless of the overlaps. Notably, genes with 5' UTR variants are almost mutually exclusive between the two phenotypes. In conclusion, we conducted the first genomic study to explore the impact of recurrent variants exclusive to the two distinguished clinical phenotypes under study, BD with or without cancer, demonstrating enrichment of selective protein-coding/ncRNAs differentially expressed between these two phenotypes, suggesting that selective genetic factors may underlie the molecular processes of pediatric cancer development in BD children.
Collapse
Affiliation(s)
- Yichuan Liu
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, 3615 Civic Center Blvd Abramson Building, Philadelphia, PA, 19104, USA.
| | - Hui-Qi Qu
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, 3615 Civic Center Blvd Abramson Building, Philadelphia, PA, 19104, USA
| | - Xiao Chang
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, 3615 Civic Center Blvd Abramson Building, Philadelphia, PA, 19104, USA
| | - Frank D Mentch
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, 3615 Civic Center Blvd Abramson Building, Philadelphia, PA, 19104, USA
| | - Haijun Qiu
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, 3615 Civic Center Blvd Abramson Building, Philadelphia, PA, 19104, USA
| | - Xiang Wang
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, 3615 Civic Center Blvd Abramson Building, Philadelphia, PA, 19104, USA
| | - Amir Hossein Saeidian
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, 3615 Civic Center Blvd Abramson Building, Philadelphia, PA, 19104, USA
| | - Deborah Watson
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, 3615 Civic Center Blvd Abramson Building, Philadelphia, PA, 19104, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Joseph Glessner
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, 3615 Civic Center Blvd Abramson Building, Philadelphia, PA, 19104, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Hakon Hakonarson
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, 3615 Civic Center Blvd Abramson Building, Philadelphia, PA, 19104, USA.
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
- Division of Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland.
| |
Collapse
|
33
|
Vande Voorde J, Steven RT, Najumudeen AK, Ford CA, Dexter A, Gonzalez-Fernandez A, Nikula CJ, Xiang Y, Ford L, Maneta Stavrakaki S, Gilroy K, Zeiger LB, Pennel K, Hatthakarnkul P, Elia EA, Nasif A, Murta T, Manoli E, Mason S, Gillespie M, Lannagan TRM, Vlahov N, Ridgway RA, Nixon C, Raven A, Mills M, Athineos D, Kanellos G, Nourse C, Gay DM, Hughes M, Burton A, Yan B, Sellers K, Wu V, De Ridder K, Shokry E, Huerta Uribe A, Clark W, Clark G, Kirschner K, Thienpont B, Li VSW, Maddocks ODK, Barry ST, Goodwin RJA, Kinross J, Edwards J, Yuneva MO, Sumpton D, Takats Z, Campbell AD, Bunch J, Sansom OJ. Metabolic profiling stratifies colorectal cancer and reveals adenosylhomocysteinase as a therapeutic target. Nat Metab 2023; 5:1303-1318. [PMID: 37580540 PMCID: PMC10447251 DOI: 10.1038/s42255-023-00857-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 07/06/2023] [Indexed: 08/16/2023]
Abstract
The genomic landscape of colorectal cancer (CRC) is shaped by inactivating mutations in tumour suppressors such as APC, and oncogenic mutations such as mutant KRAS. Here we used genetically engineered mouse models, and multimodal mass spectrometry-based metabolomics to study the impact of common genetic drivers of CRC on the metabolic landscape of the intestine. We show that untargeted metabolic profiling can be applied to stratify intestinal tissues according to underlying genetic alterations, and use mass spectrometry imaging to identify tumour, stromal and normal adjacent tissues. By identifying ions that drive variation between normal and transformed tissues, we found dysregulation of the methionine cycle to be a hallmark of APC-deficient CRC. Loss of Apc in the mouse intestine was found to be sufficient to drive expression of one of its enzymes, adenosylhomocysteinase (AHCY), which was also found to be transcriptionally upregulated in human CRC. Targeting of AHCY function impaired growth of APC-deficient organoids in vitro, and prevented the characteristic hyperproliferative/crypt progenitor phenotype driven by acute deletion of Apc in vivo, even in the context of mutant Kras. Finally, pharmacological inhibition of AHCY reduced intestinal tumour burden in ApcMin/+ mice indicating its potential as a metabolic drug target in CRC.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Yuchen Xiang
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Lauren Ford
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Stefania Maneta Stavrakaki
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | | | - Lucas B Zeiger
- Cancer Research UK Beatson Institute, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - Kathryn Pennel
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | | | | | | | | | - Eftychios Manoli
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Sam Mason
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Michael Gillespie
- Cancer Research UK Beatson Institute, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | | | | | | | - Colin Nixon
- Cancer Research UK Beatson Institute, Glasgow, UK
| | | | - Megan Mills
- Cancer Research UK Beatson Institute, Glasgow, UK
| | | | | | - Craig Nourse
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - David M Gay
- Cancer Research UK Beatson Institute, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
- Københavns Universitet, BRIC, Copenhagen, Denmark
| | - Mark Hughes
- Cancer Research UK Beatson Institute, Glasgow, UK
| | - Amy Burton
- National Physical Laboratory, London, UK
| | - Bin Yan
- National Physical Laboratory, London, UK
| | - Katherine Sellers
- The Francis Crick Institute, London, UK
- Rheos Medicines, Cambridge, MA, USA
| | - Vincen Wu
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Kobe De Ridder
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
| | - Engy Shokry
- Cancer Research UK Beatson Institute, Glasgow, UK
| | | | | | - Graeme Clark
- Cancer Research UK Beatson Institute, Glasgow, UK
| | | | - Bernard Thienpont
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
| | | | | | - Simon T Barry
- Bioscience, Early Oncology, AstraZeneca, Cambridge, UK
| | - Richard J A Goodwin
- Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
- Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - James Kinross
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Joanne Edwards
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | | | | | - Zoltan Takats
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
- Biological Mass Spectrometry, Rosalind Franklin Institute, Didcot, UK
| | | | - Josephine Bunch
- National Physical Laboratory, London, UK
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
- Biological Mass Spectrometry, Rosalind Franklin Institute, Didcot, UK
| | - Owen J Sansom
- Cancer Research UK Beatson Institute, Glasgow, UK.
- School of Cancer Sciences, University of Glasgow, Glasgow, UK.
| |
Collapse
|
34
|
Lee MY, Tam WL. Multimodal metabolomics pinpoint new metabolic vulnerability in colorectal cancer. Nat Metab 2023; 5:1255-1257. [PMID: 37580541 DOI: 10.1038/s42255-023-00852-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Affiliation(s)
- May Yin Lee
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
| | - Wai Leong Tam
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Republic of Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore.
- NUS Centre for Cancer Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore.
| |
Collapse
|
35
|
Rosario SR, Dong B, Zhang Y, Hsiao HH, Isenhart E, Wang J, Siegel EM, Monjazeb AM, Owen DH, Dey P, Tabung FK, Spakowicz DJ, Murphy WJ, Edge S, Yendamuri S, Ibrahimi S, Kolesar JM, McDonald PH, Vadehra D, Churchman M, Liu S, Kalinski P, Mukherjee S. Metabolic Dysregulation Explains the Diverse Impacts of Obesity in Males and Females with Gastrointestinal Cancers. Int J Mol Sci 2023; 24:10847. [PMID: 37446025 PMCID: PMC10342094 DOI: 10.3390/ijms241310847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/09/2023] [Accepted: 06/24/2023] [Indexed: 07/15/2023] Open
Abstract
The prevalence of obesity, defined as the body mass index (BMI) ≥ 30 kg/m2, has reached epidemic levels. Obesity is associated with an increased risk of various cancers, including gastrointestinal ones. Recent evidence has suggested that obesity disproportionately impacts males and females with cancer, resulting in varied transcriptional and metabolic dysregulation. This study aimed to elucidate the differences in the metabolic milieu of adenocarcinomas of the gastrointestinal (GI) tract both related and unrelated to sex in obesity. To demonstrate these obesity and sex-related effects, we utilized three primary data sources: serum metabolomics from obese and non-obese patients assessed via the Biocrates MxP Quant 500 mass spectrometry-based kit, the ORIEN tumor RNA-sequencing data for all adenocarcinoma cases to assess the impacts of obesity, and publicly available TCGA transcriptional analysis to assess GI cancers and sex-related differences in GI cancers specifically. We applied and integrated our unique transcriptional metabolic pipeline in combination with our metabolomics data to reveal how obesity and sex can dictate differential metabolism in patients. Differentially expressed genes (DEG) analysis of ORIEN obese adenocarcinoma as compared to normal-weight adenocarcinoma patients resulted in large-scale transcriptional reprogramming (4029 DEGs, adj. p < 0.05 and |logFC| > 0.58). Gene Set Enrichment and metabolic pipeline analysis showed genes enriched for pathways relating to immunity (inflammation, and CD40 signaling, among others) and metabolism. Specifically, we found alterations to steroid metabolism and tryptophan/kynurenine metabolism in obese patients, both of which are highly associated with disease severity and immune cell dysfunction. These findings were further confirmed using the TCGA colorectal adenocarcinoma (CRC) and esophageal adenocarcinoma (ESCA) data, which showed similar patterns of increased tryptophan catabolism for kynurenine production in obese patients. These patients further showed disparate alterations between males and females when comparing obese to non-obese patient populations. Alterations to immune and metabolic pathways were validated in six patients (two obese and four normal weight) via CD8+/CD4+ peripheral blood mononuclear cell RNA-sequencing and paired serum metabolomics, which showed differential kynurenine and lipid metabolism, which corresponded with altered T-cell transcriptome in obese populations. Overall, obesity is associated with differential transcriptional and metabolic programs in various disease sites. Further, these alterations, such as kynurenine and tryptophan metabolism, which impact both metabolism and immune phenotype, vary with sex and obesity together. This study warrants further in-depth investigation into obesity and sex-related alterations in cancers that may better define biomarkers of response to immunotherapy.
Collapse
Affiliation(s)
- Spencer R. Rosario
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA; (S.R.R.); (Y.Z.); (H.-H.H.); (E.I.); (J.W.); (S.L.)
- Department of Pharmacology and Therapeutics, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA
| | - Bowen Dong
- Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA; (B.D.); (P.D.); (P.K.)
| | - Yali Zhang
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA; (S.R.R.); (Y.Z.); (H.-H.H.); (E.I.); (J.W.); (S.L.)
| | - Hua-Hsin Hsiao
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA; (S.R.R.); (Y.Z.); (H.-H.H.); (E.I.); (J.W.); (S.L.)
| | - Emily Isenhart
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA; (S.R.R.); (Y.Z.); (H.-H.H.); (E.I.); (J.W.); (S.L.)
| | - Jianmin Wang
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA; (S.R.R.); (Y.Z.); (H.-H.H.); (E.I.); (J.W.); (S.L.)
| | - Erin M. Siegel
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center, Tampa, FL 33612, USA;
| | - Arta M. Monjazeb
- Department of Radiation Oncology, University of California Davis, Sacramento, CA 95616, USA;
| | - Dwight H. Owen
- Department of Medical Oncology, Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA; (D.H.O.); (D.J.S.)
| | - Prasenjit Dey
- Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA; (B.D.); (P.D.); (P.K.)
| | - Fred K. Tabung
- Department of Epidemiology, Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA;
| | - Daniel J. Spakowicz
- Department of Medical Oncology, Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA; (D.H.O.); (D.J.S.)
| | - William J. Murphy
- Department of Immunology, University of California Davis, Sacramento, CA 95616, USA;
| | - Stephen Edge
- Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA;
| | - Sai Yendamuri
- Department of Thoracic Surgery, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA;
| | - Sami Ibrahimi
- Department of Medicine, Oklahoma University Health Stephenson Cancer Center, Oklahoma City, OK 73104, USA;
| | - Jill M. Kolesar
- Department of Pharmacy, University of Kentucky College of Pharmacy, Lexington, KY 40506, USA;
| | - Patsy H. McDonald
- Department of Cancer Biology, Moffitt Cancer Center, Tampa, FL 33612, USA;
| | - Deepak Vadehra
- Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA;
| | - Michelle Churchman
- Precision Therapy and Diagnostics, Aster Insights, Hudson, FL 34667, USA;
| | - Song Liu
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA; (S.R.R.); (Y.Z.); (H.-H.H.); (E.I.); (J.W.); (S.L.)
| | - Pawel Kalinski
- Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA; (B.D.); (P.D.); (P.K.)
| | - Sarbajit Mukherjee
- Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA; (B.D.); (P.D.); (P.K.)
- Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA;
| |
Collapse
|
36
|
Funke VLE, Walter C, Melcher V, Wei L, Sandmann S, Hotfilder M, Varghese J, Jäger N, Kool M, Jones DTW, Pfister SM, Milde T, Mynarek M, Rutkowski S, Seggewiss J, Jeising D, de Faria FW, Marquardt T, Albert TK, Schüller U, Kerl K. Group-specific cellular metabolism in Medulloblastoma. J Transl Med 2023; 21:363. [PMID: 37277823 DOI: 10.1186/s12967-023-04211-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/19/2023] [Indexed: 06/07/2023] Open
Abstract
BACKGROUND Cancer metabolism influences multiple aspects of tumorigenesis and causes diversity across malignancies. Although comprehensive research has extended our knowledge of molecular subgroups in medulloblastoma (MB), discrete analysis of metabolic heterogeneity is currently lacking. This study seeks to improve our understanding of metabolic phenotypes in MB and their impact on patients' outcomes. METHODS Data from four independent MB cohorts encompassing 1,288 patients were analysed. We explored metabolic characteristics of 902 patients (ICGC and MAGIC cohorts) on bulk RNA level. Moreover, data from 491 patients (ICGC cohort) were searched for DNA alterations in genes regulating cell metabolism. To determine the role of intratumoral metabolic differences, we examined single-cell RNA-sequencing (scRNA-seq) data from 34 additional patients. Findings on metabolic heterogeneity were correlated to clinical data. RESULTS Established MB groups exhibit substantial differences in metabolic gene expression. By employing unsupervised analyses, we identified three clusters of group 3 and 4 samples with distinct metabolic features in ICGC and MAGIC cohorts. Analysis of scRNA-seq data confirmed our results of intertumoral heterogeneity underlying the according differences in metabolic gene expression. On DNA level, we discovered clear associations between altered regulatory genes involved in MB development and lipid metabolism. Additionally, we determined the prognostic value of metabolic gene expression in MB and showed that expression of genes involved in metabolism of inositol phosphates and nucleotides correlates with patient survival. CONCLUSION Our research underlines the biological and clinical relevance of metabolic alterations in MB. Thus, distinct metabolic signatures presented here might be the first step towards future metabolism-targeted therapeutic options.
Collapse
Affiliation(s)
- Viktoria L E Funke
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Carolin Walter
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
- Institute of Medical Informatics, University of Münster, 48149, Münster, Germany
| | - Viktoria Melcher
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Lanying Wei
- Institute of Medical Informatics, University of Münster, 48149, Münster, Germany
| | - Sarah Sandmann
- Institute of Medical Informatics, University of Münster, 48149, Münster, Germany
| | - Marc Hotfilder
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Julian Varghese
- Institute of Medical Informatics, University of Münster, 48149, Münster, Germany
| | - Natalie Jäger
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Marcel Kool
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - David T W Jones
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Division of Pediatric Glioma Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stefan M Pfister
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany
- Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany
| | - Till Milde
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany
| | - Martin Mynarek
- Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
- Mildred Scheel Cancer Career Center HaTriCS4, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Rutkowski
- Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
| | - Jochen Seggewiss
- Institute of Human Genetics, University Hospital Münster, Münster, Germany
| | - Daniela Jeising
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Flavia W de Faria
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Thorsten Marquardt
- Department of General Pediatrics, Metabolic Diseases, University Children's Hospital Münster, 48149, Münster, Germany
| | - Thomas K Albert
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Ulrich Schüller
- Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
- Research Institute Children's Cancer Center, 20251, Hamburg, Germany
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
| | - Kornelius Kerl
- Department of Pediatric Hematology and Oncology, University Children's Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany.
| |
Collapse
|
37
|
McGray AJR, Chiello JL, Tsuji T, Long M, Maraszek K, Gaulin N, Rosario SR, Hess SM, Abrams SI, Kozbor D, Odunsi K, Zsiros E. BiTE secretion by adoptively transferred stem-like T cells improves FRα+ ovarian cancer control. J Immunother Cancer 2023; 11:e006863. [PMID: 37647218 PMCID: PMC10314690 DOI: 10.1136/jitc-2023-006863] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/26/2023] [Indexed: 09/01/2023] Open
Abstract
BACKGROUND Cancer immunotherapies can produce complete therapeutic responses, however, outcomes in ovarian cancer (OC) are modest. While adoptive T-cell transfer (ACT) has been evaluated in OC, durable effects are rare. Poor therapeutic efficacy is likely multifactorial, stemming from limited antigen recognition, insufficient tumor targeting due to a suppressive tumor microenvironment (TME), and limited intratumoral accumulation/persistence of infused T cells. Importantly, host T cells infiltrate tumors, and ACT approaches that leverage endogenous tumor-infiltrating T cells for antitumor immunity could effectively magnify therapeutic responses. METHODS Using retroviral transduction, we have generated T cells that secrete a folate receptor alpha (FRα)-directed bispecific T-cell engager (FR-B T cells), a tumor antigen commonly overexpressed in OC and other tumor types. The antitumor activity and therapeutic efficacy of FR-B T cells was assessed using FRα+ cancer cell lines, OC patient samples, and preclinical tumor models with accompanying mechanistic studies. Different cytokine stimulation of T cells (interleukin (IL)-2+IL-7 vs IL-2+IL-15) during FR-B T cell production and the resulting impact on therapeutic outcome following ACT was also assessed. RESULTS FR-B T cells efficiently lysed FRα+ cell lines, targeted FRα+ OC patient tumor cells, and were found to engage and activate patient T cells present in the TME through secretion of T cell engagers. Additionally, FR-B T cell therapy was effective in an immunocompetent in vivo OC model, with response duration dependent on both endogenous T cells and FR-B T cell persistence. IL-2/IL-15 preconditioning prior to ACT produced less differentiated FR-B T cells and enhanced therapeutic efficacy, with mechanistic studies revealing preferential accumulation of TCF-1+CD39-CD69- stem-like CD8+ FR B T cells in the peritoneal cavity over solid tumors. CONCLUSIONS These findings highlight the therapeutic potential of FR-B T cells in OC and suggest FR-B T cells can persist in extratumoral spaces while actively directing antitumor immunity. As the therapeutic activity of infused T cell therapies in solid tumor indications is often limited by poor intratumoral accumulation of transferred T cells, engager-secreting T cells that can effectively leverage endogenous immunity may have distinct mechanistic advantages for enhancing therapeutic responses rates.
Collapse
Affiliation(s)
- A J Robert McGray
- Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
- Department of Gynecologic Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Jessie L Chiello
- Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Takemasa Tsuji
- Center for Immunotherapy, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
- University of Chicago Medicine Comprehensive Cancer Center and Department of Obstetrics and Gynecology, Chicago, Illinois, USA
| | - Mark Long
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Kathryn Maraszek
- Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Nicole Gaulin
- Department of Gynecologic Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Spencer R Rosario
- Center for Immunotherapy, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Suzanne M Hess
- Department of Gynecologic Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Scott I Abrams
- Center for Immunotherapy, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Danuta Kozbor
- Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Kunle Odunsi
- Center for Immunotherapy, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
- University of Chicago Medicine Comprehensive Cancer Center and Department of Obstetrics and Gynecology, Chicago, Illinois, USA
| | - Emese Zsiros
- Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
- Department of Gynecologic Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| |
Collapse
|
38
|
Chen D, Cao Y, Tang H, Zang L, Yao N, Zhu Y, Jiang Y, Zhai S, Liu Y, Shi M, Zhao S, Wang W, Wen C, Peng C, Chen H, Deng X, Jiang L, Shen B. Comprehensive machine learning-generated classifier identifies pro-metastatic characteristics and predicts individual treatment in pancreatic cancer: A multicenter cohort study based on super-enhancer profiling. Theranostics 2023; 13:3290-3309. [PMID: 37351165 PMCID: PMC10283048 DOI: 10.7150/thno.84978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 05/13/2023] [Indexed: 06/24/2023] Open
Abstract
Rationale: Accumulating evidence illustrated that the reprogramming of the super-enhancers (SEs) landscape could promote the acquisition of metastatic features in pancreatic cancer (PC). Given the anatomy-based TNM staging is limited by the heterogeneous clinical outcomes in treatment, it is of great clinical significance to tailor individual stratification and to develop alternative therapeutic strategies for metastatic PC patients based on SEs. Methods: In our study, ChIP-Seq analysis for H3K27ac was performed in primary pancreatic tumors (PTs) and hepatic metastases (HMs). Bootstrapping and univariate Cox analysis were implemented to screen prognostic HM-acquired, SE-associated genes (HM-SE genes). Then, based on 1705 PC patients from 14 multicenter cohorts, 188 machine-learning (ML) algorithm integrations were utilized to develop a comprehensive super-enhancer-related metastatic (SEMet) classifier. Results: We established a novel SEMet classifier based on 38 prognostic HM-SE genes. Compared to other clinical traits and 33 published signatures, the SEMet classifier possessed robust and powerful performance in predicting prognosis. In addition, patients in the SEMetlow subgroup owned dismal survival rates, more frequent genomic alterations, and more activated cancer immunity cycle as well as better benefits in immunotherapy. Remarkably, there existed a tight correlation between the SEMetlow subgroup and metastatic phenotypes of PC. Among 18 SEMet genes, we demonstrated that E2F7 may promote PC metastasis through the upregulation of TGM2 and DKK1. Finally, after in silico screening of potential compounds targeted SEMet classifier, results revealed that flumethasone could enhance the sensitivity of metastatic PC to routine gemcitabine chemotherapy. Conclusion: Overall, our study provided new insights into personalized treatment approaches in the clinical management of metastatic PC patients.
Collapse
Affiliation(s)
- Dongjie Chen
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Yizhi Cao
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Haoyu Tang
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Longjun Zang
- Department of General Surgery, Taiyuan Central Hospital, Shanxi, P.R. China
| | - Na Yao
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Youwei Zhu
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Yongsheng Jiang
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Shuyu Zhai
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Yihao Liu
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Minmin Shi
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Shulin Zhao
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Weishen Wang
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Chenlei Wen
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Chenghong Peng
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Hao Chen
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Xiaxing Deng
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Lingxi Jiang
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Baiyong Shen
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| |
Collapse
|
39
|
Dai J, Li Q, Quan J, Webb G, Liu J, Gao K. Construction of a lipid metabolism-related and immune-associated prognostic score for gastric cancer. BMC Med Genomics 2023; 16:93. [PMID: 37138287 PMCID: PMC10158005 DOI: 10.1186/s12920-023-01515-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 04/12/2023] [Indexed: 05/05/2023] Open
Abstract
BACKGROUND The interaction between tumor cells and immune or non-immune stromal cells creates a unique tumor microenvironment, which plays an important role in the growth, invasion and metastasis of gastric cancer (GC). METHODS The candidate genes were selected to construct risk-score by univariate and multivariate Cox regression analysis. Nomograms were constructed by combining clinical pathological factors, and the model performance was evaluated by receiver operating characteristic curve, decision curve analysis, net reclassification improvement and integrated discrimination improvement. The functional enrichment between high-risk group (HRisk) and low-risk group (LRisk) was explored through GO, KEGG, GSVA and ssGSEA. CIBERSORT, quanTIseq and xCell were used to explore the immune cell infiltration between HRisk and LRisk. The relevant EMT scores, macrophage infiltration scores and various metabolic scores were calculated through the "IOBR" package and analyzed visually. RESULTS Through univariate and multivariate Cox regression analysis, we obtained the risk-score of fittings six lipid metabolism related genes (LMAGs). Through survival analysis, we found that risk-score has significant prognostic significance and can accurately reflect the metabolic level of patients. The AUCs of the nomogram model incorporating risk-score 1, 3 and 5 years were 0.725, 0.729 and 0.749 respectively. In addition, it was found that the inclusion of risk-score could significantly improve the prediction performance of the model. It was found that the arachidonic acid metabolism and prostaglandin synthesis were up-regulated in HRisk, and more tumor metastasis related markers and immune related pathways were also enriched. Further study found that HRisk had higher immune score and M2 macrophage infiltration. More importantly, the immune checkpoints of tumor associated macrophages involved in tumor antigen recognition disorders increased significantly. We also found that ST6GALNAC3 can promote arachidonic acid metabolism and up-regulate prostaglandin synthesis, increase M2 macrophage infiltration, induce epithelial mesenchymal transformation, and affect the prognosis of patients. CONCLUSIONS Our research found a novel and powerful LMAGs signature. Six-LMAGs features can effectively evaluate the prognosis of GC patients and reflect the metabolic and immune status. ST6GALNAC3 may be a potential prognostic marker to improve the survival rate and prognostic accuracy of GC patients, and may even be a potential biomarker of GC patients, indicating the response to immunotherapy.
Collapse
Affiliation(s)
- Jing Dai
- Department of Gastrointestinal Surgery, The Third Xiangya Hospital of Central South University, Changsha, 410013, Hunan, People's Republic of China
| | - Qiqing Li
- Department of Gastrointestinal Surgery, The Third Xiangya Hospital of Central South University, Changsha, 410013, Hunan, People's Republic of China
| | - Jun Quan
- Department of Gastrointestinal Surgery, The Third Xiangya Hospital of Central South University, Changsha, 410013, Hunan, People's Republic of China
| | - Gunther Webb
- Department of Gastrointestinal Surgery, The Third Xiangya Hospital of Central South University, Changsha, 410013, Hunan, People's Republic of China
| | - Juan Liu
- Department of Dermatology, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, People's Republic of China
| | - Kai Gao
- Department of Gastrointestinal Surgery, The Third Xiangya Hospital of Central South University, Changsha, 410013, Hunan, People's Republic of China.
| |
Collapse
|
40
|
Wang Y, Gao X, Wang J. Functional Proteomic Profiling Analysis in Four Major Types of Gastrointestinal Cancers. Biomolecules 2023; 13:biom13040701. [PMID: 37189448 DOI: 10.3390/biom13040701] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/05/2023] [Accepted: 04/18/2023] [Indexed: 05/17/2023] Open
Abstract
Gastrointestinal (GI) cancer accounts for one in four cancer cases and one in three cancer-related deaths globally. A deeper understanding of cancer development mechanisms can be applied to cancer medicine. Comprehensive sequencing applications have revealed the genomic landscapes of the common types of human cancer, and proteomics technology has identified protein targets and signalling pathways related to cancer growth and progression. This study aimed to explore the functional proteomic profiles of four major types of GI tract cancer based on The Cancer Proteome Atlas (TCPA). We provided an overview of functional proteomic heterogeneity by performing several approaches, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), t-stochastic neighbour embedding (t-SNE) analysis, and hierarchical clustering analysis in oesophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ) tumours, to gain a system-wide understanding of the four types of GI cancer. The feature selection approach, mutual information feature selection (MIFS) method, was conducted to screen candidate protein signature subsets to better distinguish different cancer types. The potential clinical implications of candidate proteins in terms of tumour progression and prognosis were also evaluated based on TCPA and The Cancer Genome Atlas (TCGA) databases. The results suggested that functional proteomic profiling can identify different patterns among the four types of GI cancers and provide candidate proteins for clinical diagnosis and prognosis evaluation. We also highlighted the application of feature selection approaches in high-dimensional biological data analysis. Overall, this study could improve the understanding of the complexity of cancer phenotypes and genotypes and thus be applied to cancer medicine.
Collapse
Affiliation(s)
- Yangyang Wang
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China
| | - Xiaoguang Gao
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China
| | - Jihan Wang
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an 710072, China
| |
Collapse
|
41
|
Liu J, Zhu J, Zhang Q. Comprehensive analysis of glycolysis mediated pattern clusters and immune infiltration characterization of tumor microenvironment in triple-negative breast cancer. Heliyon 2023; 9:e15175. [PMID: 37089355 PMCID: PMC10119610 DOI: 10.1016/j.heliyon.2023.e15175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 03/24/2023] [Accepted: 03/29/2023] [Indexed: 04/03/2023] Open
Abstract
Background The involvement of glycolysis in carcinogenesis and the tumor microenvironment is being increasingly supported by the available data. The aim of this work was to develop a triple-negative breast cancer predictive model based on glycolysis. Methods Glycolysis mediated pattern clusters were created using the R "ConsensusClusterPlus" package. The variations in the tumor microenvironment between the pattern clusters were examined using the R "GSVA", "ESTIMATE", and "CIBERSORT" package. The risk score and nomogram were established to assess clinical outcomes of patients. Results Substantial differences were observed in the immunological landscape between the glycolysis-mediated pattern clusters. When it came to predicting survival and immunotherapy response, the developed risk score showed promising predictive power. Nomogram was designed to be used in therapeutic settings due to its remarkable predictive accuracy. Conclusions The immune microenvironment varied among cases of triple-negative breast cancer. The nomogram and the risk score based on glycolysis could both be used to help create more effective treatments.
Collapse
|
42
|
Tian S, Zhang J, Yuan S, Wang Q, Lv C, Wang J, Fang J, Fu L, Yang J, Zu X, Zhao J, Zhang W. Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM. Brief Bioinform 2023; 24:7017365. [PMID: 36719094 DOI: 10.1093/bib/bbad027] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/18/2022] [Accepted: 01/10/2023] [Indexed: 02/01/2023] Open
Abstract
With the emergence of high-throughput technologies, computational screening based on gene expression profiles has become one of the most effective methods for drug discovery. More importantly, profile-based approaches remarkably enhance novel drug-disease pair discovery without relying on drug- or disease-specific prior knowledge, which has been widely used in modern medicine. However, profile-based systematic screening of active ingredients of traditional Chinese medicine (TCM) has been scarcely performed due to inadequate pharmacotranscriptomic data. Here, we develop the largest-to-date online TCM active ingredients-based pharmacotranscriptomic platform integrated traditional Chinese medicine (ITCM) for the effective screening of active ingredients. First, we performed unified high-throughput experiments and constructed the largest data repository of 496 representative active ingredients, which was five times larger than the previous one built by our team. The transcriptome-based multi-scale analysis was also performed to elucidate their mechanism. Then, we developed six state-of-art signature search methods to screen active ingredients and determine the optimal signature size for all methods. Moreover, we integrated them into a screening strategy, TCM-Query, to identify the potential active ingredients for the special disease. In addition, we also comprehensively collected the TCM-related resource by literature mining. Finally, we applied ITCM to an active ingredient bavachinin, and two diseases, including prostate cancer and COVID-19, to demonstrate the power of drug discovery. ITCM was aimed to comprehensively explore the active ingredients of TCM and boost studies of pharmacological action and drug discovery. ITCM is available at http://itcm.biotcm.net.
Collapse
Affiliation(s)
- Saisai Tian
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Jinbo Zhang
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
- Department of Pharmacy, Tianjin Rehabilitation Center of Joint Logistics Support Force, Tianjin, 300110, China
| | - Shunling Yuan
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Qun Wang
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chao Lv
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jinxing Wang
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Lu Fu
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Jian Yang
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Xianpeng Zu
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Jing Zhao
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Weidong Zhang
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| |
Collapse
|
43
|
Srivastava A, Vinod PK. Identification and Characterization of Metabolic Subtypes of Endometrial Cancer Using a Systems-Level Approach. Metabolites 2023; 13:metabo13030409. [PMID: 36984849 PMCID: PMC10054278 DOI: 10.3390/metabo13030409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 03/03/2023] [Accepted: 03/05/2023] [Indexed: 03/12/2023] Open
Abstract
Endometrial cancer (EC) is the most common gynecological cancer worldwide. Understanding metabolic adaptation and its heterogeneity in tumor tissues may provide new insights and help in cancer diagnosis, prognosis, and treatment. In this study, we investigated metabolic alterations of EC to understand the variations in metabolism within tumor samples. Integration of transcriptomics data of EC (RNA-Seq) and the human genome-scale metabolic network was performed to identify the metabolic subtypes of EC and uncover the underlying dysregulated metabolic pathways and reporter metabolites in each subtype. The relationship between metabolic subtypes and clinical variables was explored. Further, we correlated the metabolic changes occurring at the transcriptome level with the genomic alterations. Based on metabolic profile, EC patients were stratified into two subtypes (metabolic subtype-1 and subtype-2) that significantly correlated to patient survival, tumor stages, mutation, and copy number variations. We observed the co-activation of the pentose phosphate pathway, one-carbon metabolism, and genes involved in controlling estrogen levels in metabolic subtype-2, which is linked to poor survival. PNMT and ERBB2 are also upregulated in metabolic subtype-2 samples and present on the same chromosome locus 17q12, which is amplified. PTEN and TP53 mutations show mutually exclusive behavior between subtypes and display a difference in survival. This work identifies metabolic subtypes with distinct characteristics at the transcriptome and genome levels, highlighting the metabolic heterogeneity within EC.
Collapse
|
44
|
Chouhan S, Sawant M, Weimholt C, Luo J, Sprung RW, Terrado M, Mueller DM, Earp HS, Mahajan NP. TNK2/ACK1-mediated phosphorylation of ATP5F1A (ATP synthase F1 subunit alpha) selectively augments survival of prostate cancer while engendering mitochondrial vulnerability. Autophagy 2023; 19:1000-1025. [PMID: 35895804 PMCID: PMC9980697 DOI: 10.1080/15548627.2022.2103961] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 07/08/2022] [Accepted: 07/11/2022] [Indexed: 11/02/2022] Open
Abstract
The challenge of rapid macromolecular synthesis enforces the energy-hungry cancer cell mitochondria to switch their metabolic phenotypes, accomplished by activation of oncogenic tyrosine kinases. Precisely how kinase activity is directly exploited by cancer cell mitochondria to meet high-energy demand, remains to be deciphered. Here we show that a non-receptor tyrosine kinase, TNK2/ACK1 (tyrosine kinase non receptor 2), phosphorylated ATP5F1A (ATP synthase F1 subunit alpha) at Tyr243 and Tyr246 (Tyr200 and 203 in the mature protein, respectively) that not only increased the stability of complex V, but also increased mitochondrial energy output in cancer cells. Further, phospho-ATP5F1A (p-Y-ATP5F1A) prevented its binding to its physiological inhibitor, ATP5IF1 (ATP synthase inhibitory factor subunit 1), causing sustained mitochondrial activity to promote cancer cell growth. TNK2 inhibitor, (R)-9b reversed this process and induced mitophagy-based autophagy to mitigate prostate tumor growth while sparing normal prostate cells. Further, depletion of p-Y-ATP5F1A was needed for (R)-9b-mediated mitophagic response and tumor growth. Moreover, Tnk2 transgenic mice displayed increased p-Y-ATP5F1A and loss of mitophagy and exhibited formation of prostatic intraepithelial neoplasia (PINs). Consistent with these data, a marked increase in p-Y-ATP5F1A was seen as prostate cancer progressed to the malignant stage. Overall, this study uncovered the molecular intricacy of tyrosine kinase-mediated mitochondrial energy regulation as a distinct cancer cell mitochondrial vulnerability and provided evidence that TNK2 inhibitors can act as "mitocans" to induce cancer-specific mitophagy.Abbreviations: ATP5F1A: ATP synthase F1 subunit alpha; ATP5IF1: ATP synthase inhibitory factor subunit 1; CRPC: castration-resistant prostate cancer; DNM1L: dynamin 1 like; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; Mdivi-1: mitochondrial division inhibitor 1; Mut-ATP5F1A: Y243,246A mutant of ATP5F1A; OXPHOS: oxidative phosphorylation; PC: prostate cancer; PINK1: PTEN induced kinase 1; p-Y-ATP5F1A: phosphorylated tyrosine 243 and 246 on ATP5F1A; TNK2/ACK1: tyrosine kinase non receptor 2; Ub: ubiquitin; WT: wild type.
Collapse
Affiliation(s)
- Surbhi Chouhan
- Department of Surgery, Cancer Research Building, St. Louis, MO, USA
- Division of Urologic Surgery Washington University, St. Louis, MO, USA
| | - Mithila Sawant
- Department of Surgery, Cancer Research Building, St. Louis, MO, USA
- Division of Urologic Surgery Washington University, St. Louis, MO, USA
| | - Cody Weimholt
- Department of Pathology & Immunology Washington University, St. Louis, MO, USA
| | - Jingqin Luo
- Division of Public Health Sciences, Washington University, St. Louis, MO, USA
| | - Robert W. Sprung
- Department of Surgery, Cancer Research Building, St. Louis, MO, USA
| | - Mailyn Terrado
- Center for Genetic Diseases, Chicago Medical School, Rosalind Franklin University, North Chicago, IL, USA
| | - David M. Mueller
- Center for Genetic Diseases, Chicago Medical School, Rosalind Franklin University, North Chicago, IL, USA
| | - H. Shelton Earp
- Lineberger Comprehensive Cancer Center, Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - Nupam P. Mahajan
- Department of Surgery, Cancer Research Building, St. Louis, MO, USA
- Division of Urologic Surgery Washington University, St. Louis, MO, USA
- Siteman Cancer Center Washington University, St. Louis, MO, USA
| |
Collapse
|
45
|
Zhang N, Zhang H, Liu Z, Dai Z, Wu W, Zhou R, Li S, Wang Z, Liang X, Wen J, Zhang X, Zhang B, Ouyang S, Zhang J, Luo P, Li X, Cheng Q. An artificial intelligence network-guided signature for predicting outcome and immunotherapy response in lung adenocarcinoma patients based on 26 machine learning algorithms. Cell Prolif 2023; 56:e13409. [PMID: 36822595 PMCID: PMC10068958 DOI: 10.1111/cpr.13409] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/27/2022] [Accepted: 01/12/2023] [Indexed: 02/25/2023] Open
Abstract
The immune cells play an increasingly vital role in influencing the proliferation, progression, and metastasis of lung adenocarcinoma (LUAD) cells. However, the potential of immune cells' specific genes-based model remains largely unknown. In the current study, by analysing single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing data, the tumour-infiltrating immune cell (TIIC) associated signature was developed based on a total of 26 machine learning (ML) algorithms. As a result, the TIIC signature score could predict survival outcomes of LUAD patients across five independent datasets. The TIIC signature score showed superior performance to 168 previously established signatures in LUAD. Moreover, the TIIC signature score developed by the immunofluorescence staining of the tissue array of LUAD patients showed a prognostic value. Our research revealed a solid connection between TIIC signature score and tumour immunity as well as metabolism. Additionally, it has been discovered that the TIIC signature score can forecast genomic change, chemotherapeutic drug susceptibility, and-most significantly-immunotherapeutic response. As a newly demonstrated biomarker, the TIIC signature score facilitated the selection of the LUAD population who would benefit from future clinical stratification.
Collapse
Affiliation(s)
- Nan Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Hao Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ziyu Dai
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Wantao Wu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Ran Zhou
- Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Shuyu Li
- Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Zeyu Wang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Xisong Liang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jie Wen
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Xun Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Bo Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Sirui Ouyang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jian Zhang
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Peng Luo
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xizhe Li
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Thoracic Surgery, Xiangya Hospital, Central South University, Changsha, China.,Hunan Engineering Research Center for Pulmonary Nodules Precise Diagnosis & Treatment, Changsha, China
| | - Quan Cheng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| |
Collapse
|
46
|
Zhang H, Chi M, Su D, Xiong Y, Wei H, Yu Y, Zuo Y, Yang L. A random forest-based metabolic risk model to assess the prognosis and metabolism-related drug targets in ovarian cancer. Comput Biol Med 2023; 153:106432. [PMID: 36608460 DOI: 10.1016/j.compbiomed.2022.106432] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/13/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
As one of the most common gynecologic malignant tumors, ovarian cancer is usually diagnosed at an advanced and incurable stage because of its early asymptomatic onset. Increasing research into tumor biology has demonstrated that abnormal cellular metabolism precedes tumorigenesis, therefore it has become an area of active research in academia. Cellular metabolism is of great significance in cancer diagnostic and prognostic studies. In this study, we integrated The Cancer Genome Atlas dataset with multiple Gene Expression Omnibus ovarian cancer datasets, identified 17 metabolic pathways with prognostic values using the random forest algorithm, constructed a metabolic risk scoring model based on metabolic pathway enrichment scores, and classified patients with ovarian cancer into two subtypes. Then, we systematically investigated the differences between different subtypes in terms of prognosis, differential gene expression, immune signature enrichment, Hallmark signature enrichment, and somatic mutations. As well, we successfully predicted differences in sensitivity to immunotherapy and chemotherapy drugs in patients with different metabolic risk subtypes. Moreover, we identified 5 drug targets associated with high metabolic risk and low metabolic risk ovarian cancer phenotypes through the weighted correlation network analysis and investigated their roles in the genesis of ovarian cancer. Finally, we developed an XGBoost classifier for predicting metabolic risk types in patients with ovarian cancer, producing a good predictive effect. In light of the above study, the research findings will provide valuable information for prognostic prediction and personalized medical treatment of patients with ovarian cancer.
Collapse
Affiliation(s)
- Haoxin Zhang
- Department of Gastrointestinal Oncology, Harbin Medical University Cancer Hospital, Harbin, 150081, China
| | - Meng Chi
- Department of Anesthesiology, Harbin Medical University Cancer Hospital, Harbin, 150081, China
| | - Dongqing Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yuqiang Xiong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Haodong Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yao Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yongchun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China; Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd, Hohhot, 010010, China.
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| |
Collapse
|
47
|
Extensive metabolic consequences of human glycosyltransferase gene knockouts in prostate cancer. Br J Cancer 2023; 128:285-296. [PMID: 36347965 PMCID: PMC9902621 DOI: 10.1038/s41416-022-02040-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 10/17/2022] [Accepted: 10/19/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Naturally occurring germline gene deletions (KO) represent a unique setting to interrogate gene functions. Complete deletions and differential expression of the human glycosyltransferase UGT2B17 and UGT2B28 genes are linked to prostate cancer (PCa) risk and progression, leukaemia, autoimmune and other diseases. METHODS The systemic metabolic consequences of UGT deficiencies were examined using untargeted and targeted mass spectrometry-based metabolomics profiling of carefully matched, treatment-naive PCa cases. RESULTS Each UGT KO differentially affected over 5% of the 1545 measured metabolites, with divergent metabolic perturbations influencing the same pathways. Several of the perturbed metabolites are known to promote PCa growth, invasion and metastasis, including steroids, ceramides and kynurenine. In UGT2B17 KO, reduced levels of inactive steroid-glucuronides were compensated by sulfated derivatives that constitute circulating steroid reservoirs. UGT2B28 KO presented remarkably lower levels of oxylipins paralleled by reduced inflammatory mediators, but higher ceramides unveiled as substrates of the enzyme in PCa cells. CONCLUSION The distinctive and broad metabolic rewiring caused by UGT KO reinforces the need to examine their unique and divergent functions in PCa biology.
Collapse
|
48
|
Zhao M, Li W. Metabolism-associated molecular classification of uterine corpus endometrial carcinoma. Front Genet 2023; 14:955466. [PMID: 36726804 PMCID: PMC9885131 DOI: 10.3389/fgene.2023.955466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 01/02/2023] [Indexed: 01/18/2023] Open
Abstract
Uterine corpus endometrial carcinoma (UCEC) is one of the most common gynecologic malignancies. Currently, for UCEC cancer, molecular classification based on metabolic gene characteristics is rarely established. Here, we describe the molecular subtype features of UCEC by classifying metabolism-related gene profiles. Therefore, integrative analysis was performed on UCEC patients from the TCGA public database. Consensus clustering of RNA expression data on 2,752 previously reported metabolic genes identified two metabolic subtypes, namely, C1 and C2 subtypes. Two metabolic subtypes for prognostic characteristics, immune infiltration, genetic alteration, and responses to immunotherapy existed with distinct differences. Then, differentially expressed genes (DEGs) among the two metabolic subtypes were also clustered into two subclusters, and the aforementioned features were similar to the metabolic subtypes, supporting that the metabolism-relevant molecular classification is reliable. The results showed that the C1 subtype has high metabolic activity, high immunogenicity, high gene mutation, and a good prognosis. The C2 subtype has some features with low metabolic activity, low immunogenicity, high copy number variation (CNV) alteration, and poor prognosis. Finally, a model was identified, with three gene metabolism-related signatures, which can predict the prognosis. These findings of this study demonstrate a new classification in UCEC based on the metabolic pattern, thereby providing valuable information for understanding UCEC's molecular characteristics.
Collapse
|
49
|
Huang X, Zhang F, Lin J, Lin S, Shen G, Chen X, Chen W. Systematically analyzed molecular characteristics of lung adenocarcinoma using metabolism-related genes classification. Genet Mol Biol 2023; 45:e20220121. [PMID: 36622242 PMCID: PMC9830935 DOI: 10.1590/1678-4685-gmb-2022-0121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 11/06/2022] [Indexed: 01/10/2023] Open
Abstract
High heterogeneity of lung adenocarcinoma (LUAD) is a major clinical challenge. This study aims to characterize the molecular features of LUAD through classification based on metabolism-related genes. A total of 500 LUAD samples from The Cancer Genome Atlas (TCGA) and 612 from Gene Expression Omnibus (GEO) were integrated with 2,753 metabolism-related genes to determine the molecular classification. Systematic bioinformatics analysis was used to conduct correlation analysis between metabolism-related classification and molecular characteristics of LUAD. LUAD patients were divided into three molecular clusters (C1-C3). Survival analysis revealed that C1 and C2 showed good and poor prognoses, respectively. Associational analysis of classification and molecular characteristics revealed that C1 was associated with low pathological stage, metabolic pathways, high metabolic process, active immune process and checkpoint, sensitive drug response, as well as a low genetic mutation. Nevertheless, C2 was associated with high pathological stage, carcinogenic pathways, low metabolic process, inactive immune signatures, resistant drug response, and frequent genetic mutation. Eventually, a classifier with 60 metabolic genes was constructed, confirming the robustness of molecular classification on LUAD. Our findings promote the understanding of LUAD molecular characteristics, and the research data may be used for providing information be helpful for clinical diagnosis and treatment.
Collapse
Affiliation(s)
- Xiaoming Huang
- The Affiliated Hospital of Southern Medical University, People’s Hospital of Longhua, Department of respiratory medicine, Shenzhen, China
| | - Feng Zhang
- The First Affiliated Hospital of Jinan University, Department of Intensive Care Unit, Guangzhou, China
| | - Junqi Lin
- The Affiliated Hospital of Southern Medical University, People’s Hospital of Longhua, Department of respiratory medicine, Shenzhen, China
| | - Shaoming Lin
- The Affiliated Hospital of Southern Medical University, People’s Hospital of Longhua, Department of respiratory medicine, Shenzhen, China
| | - Guanle Shen
- The Affiliated Hospital of Southern Medical University, People’s Hospital of Longhua, Department of respiratory medicine, Shenzhen, China
| | - Xiaozhu Chen
- The Affiliated Hospital of Southern Medical University, People’s Hospital of Longhua, Department of Medical Ultrasound Department, Shenzhen, China
| | - Wenbiao Chen
- The Affiliated Hospital of Southern Medical University, People’s Hospital of Longhua, Department of respiratory medicine, Shenzhen, China
| |
Collapse
|
50
|
Rosario SR, Jacobi JJ, Long MD, Affronti HC, Rowsam AM, Smiraglia DJ. JAZF1: A Metabolic Regulator of Sensitivity to a Polyamine-Targeted Therapy. Mol Cancer Res 2023; 21:24-35. [PMID: 36166196 PMCID: PMC9808368 DOI: 10.1158/1541-7786.mcr-22-0316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 08/05/2022] [Accepted: 09/22/2022] [Indexed: 02/03/2023]
Abstract
Identifying and leveraging unique points of metabolic dysregulation in different disease settings is vital for safe and effective incorporation of metabolism-targeted therapies in the clinic. In addition, it has been shown identification of master metabolic transcriptional regulators (MMTR) of individual metabolic pathways, and how they relate to the disease in question, may offer the key to understanding therapeutic response. In prostate cancer, we have previously demonstrated polyamine biosynthesis and the methionine cycle were targetable metabolic vulnerabilities. However, the MMTRs of these pathways, and how they affect treatment, have yet to be explored. We sought to characterize differential sensitivity of prostate cancer to polyamine- and methionine-targeted therapies by identifying novel MMTRs. We began by developing a gene signature from patient samples, which can predict response to metabolic therapy, and further uncovered a MMTR, JAZF1. We characterized the effects of JAZF1 overexpression on prostate cancer cells, basally and in the context of treatment, by assessing mRNA levels, proliferation, colony formation capability, and key metabolic processes. Lastly, we confirmed the relevance of our findings in large publicly available cohorts of prostate cancer patient samples. We demonstrated differential sensitivity to polyamine and methionine therapies and identified JAZF1 as a MMTR of this response. IMPLICATIONS We have shown JAZF1 can alter sensitivity of cells and its expression can segregate patient populations into those that do, or do not highly express polyamine genes, leading to better prediction of response to a polyamine targeting therapy.
Collapse
Affiliation(s)
- Spencer R. Rosario
- Department of Pharmacology and Therapeutics, Roswell Park Comprehensive Cancer Center, Buffalo, New York
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, New York
| | - Justine J. Jacobi
- Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Buffalo, New York
| | - Mark D. Long
- Department of Pharmacology and Therapeutics, Roswell Park Comprehensive Cancer Center, Buffalo, New York
| | - Hayley C. Affronti
- Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Buffalo, New York
| | - Aryn M. Rowsam
- Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Buffalo, New York
| | - Dominic J. Smiraglia
- Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Buffalo, New York
| |
Collapse
|