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Shen C, Jiang K, Zhang W, Su B, Wang Z, Chen X, Zheng B, He T. LASSO regression and WGCNA-based telomerase-associated lncRNA signaling predicts clear cell renal cell carcinoma prognosis and immunotherapy response. Aging (Albany NY) 2024; 16:9386-9409. [PMID: 38819232 PMCID: PMC11210217 DOI: 10.18632/aging.205871] [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: 01/08/2024] [Accepted: 04/16/2024] [Indexed: 06/01/2024]
Abstract
OBJECTIVE To investigate whether telomerase-associated lncRNA expression affects the prognosis and anti-tumor immunity of patients with renal clear cell carcinoma (ccRCC). METHODS A series of analyses were performed to establish a prognostic risk model and validate its accuracy. Immune-related analyses were performed to assess further the association between immune status, tumor microenvironment, and prognostic risk models. RESULTS Eight telomerase-associated lncRNAs associated with prognosis were identified and applied to establish a prognostic risk model. Overall survival was higher in the low-risk group. CONCLUSION The established prognostic risk model has a good predictive ability for the prognosis of ccRCC patients and provides a new possible therapeutic target for ccRCC.
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MESH Headings
- Carcinoma, Renal Cell/genetics
- Carcinoma, Renal Cell/immunology
- Carcinoma, Renal Cell/mortality
- Carcinoma, Renal Cell/therapy
- Carcinoma, Renal Cell/metabolism
- RNA, Long Noncoding/genetics
- RNA, Long Noncoding/metabolism
- Humans
- Kidney Neoplasms/genetics
- Kidney Neoplasms/immunology
- Kidney Neoplasms/mortality
- Kidney Neoplasms/therapy
- Telomerase/genetics
- Telomerase/metabolism
- Prognosis
- Immunotherapy/methods
- Gene Expression Regulation, Neoplastic
- Tumor Microenvironment/immunology
- Tumor Microenvironment/genetics
- Biomarkers, Tumor/genetics
- Biomarkers, Tumor/metabolism
- Signal Transduction/genetics
- Male
- Female
- Gene Regulatory Networks
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Affiliation(s)
- Cheng Shen
- Department of Urology, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, China
- Medical Research Center, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, China
| | - Kaiyao Jiang
- Department of Urology, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, China
- Medical Research Center, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, China
| | - Wei Zhang
- Department of Urology, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, China
| | - Baohui Su
- Department of Urology, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, China
- Medical Research Center, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, China
| | - Zhenyu Wang
- Department of Urology, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, China
| | - Xinfeng Chen
- Department of Urology, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, China
| | - Bing Zheng
- Department of Urology, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, China
| | - Tao He
- Party Committe and Hospital Administration Office, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, China
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Yin R, Zhao H, Li L, Yang Q, Zeng M, Yang C, Bian J, Xie M. Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.15.589599. [PMID: 38659732 PMCID: PMC11042274 DOI: 10.1101/2024.04.15.589599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Colorectal cancer (CRC) is the third most diagnosed cancer and the second deadliest cancer worldwide representing a major public health problem. In recent years, increasing evidence has shown that microRNA (miRNA) can control the expression of targeted human messenger RNA (mRNA) by reducing their abundance or translation, acting as oncogenes or tumor suppressors in various cancers, including CRC. Due to the significant up-regulation of oncogenic miRNAs in CRC, elucidating the underlying mechanism and identifying dysregulated miRNA targets may provide a basis for improving current therapeutic interventions. In this paper, we proposed Gra-CRC-miRTar, a pre-trained nucleotide-to-graph neural network framework, for identifying potential miRNA targets in CRC. Different from previous studies, we constructed two pre-trained models to encode RNA sequences and transformed them into de Bruijn graphs. We employed different graph neural networks to learn the latent representations. The embeddings generated from de Bruijn graphs were then fed into a Multilayer Perceptron (MLP) to perform the prediction tasks. Our extensive experiments show that Gra-CRC-miRTar achieves better performance than other deep learning algorithms and existing predictors. In addition, our analyses also successfully revealed 172 out of 201 functional interactions through experimentally validated miRNA-mRNA pairs in CRC. Collectively, our effort provides an accurate and efficient framework to identify potential miRNA targets in CRC, which can also be used to reveal miRNA target interactions in other malignancies, facilitating the development of novel therapeutics.
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Affiliation(s)
- Rui Yin
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
- These authors contributed equally
| | - Hongru Zhao
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
- These authors contributed equally
| | - Lu Li
- Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, USA
| | - Qiang Yang
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Min Zeng
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Carl Yang
- Department of Computer Science, Emory University, Atlanta, GA, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Mingyi Xie
- Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, USA
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Hu X, Dong H, Qin W, Bin Y, Huang W, Kang M, Wang R. Machine learning-based identification of a consensus immune-derived gene signature to improve head and neck squamous cell carcinoma therapy and outcome. Front Pharmacol 2024; 15:1341346. [PMID: 38666027 PMCID: PMC11044683 DOI: 10.3389/fphar.2024.1341346] [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: 11/20/2023] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
Background Head and neck squamous cell carcinoma (HNSCC), an extremely aggressive tumor, is often associated with poor outcomes. The standard anatomy-based tumor-node-metastasis staging system does not satisfy the requirements for screening treatment-sensitive patients. Thus, an ideal biomarker leading to precise screening and treatment of HNSCC is urgently needed. Methods Ten machine learning algorithms-Lasso, Ridge, stepwise Cox, CoxBoost, elastic network (Enet), partial least squares regression for Cox (plsRcox), random survival forest (RSF), generalized boosted regression modelling (GBM), supervised principal components (SuperPC), and survival support vector machine (survival-SVM)-as well as 85 algorithm combinations were applied to construct and identify a consensus immune-derived gene signature (CIDGS). Results Based on the expression profiles of three cohorts comprising 719 patients with HNSCC, we identified 236 consensus prognostic genes, which were then filtered into a CIDGS, using the 10 machine learning algorithms and 85 algorithm combinations. The results of a study involving a training cohort, two testing cohorts, and a meta-cohort consistently demonstrated that CIDGS was capable of accurately predicting prognoses for HNSCC. Incorporation of several core clinical features and 51 previously reported signatures, enhanced the predictive capacity of the CIDGS to a level which was markedly superior to that of other signatures. Notably, patients with low CIDGS displayed fewer genomic alterations and higher immune cell infiltrate levels, as well as increased sensitivity to immunotherapy and other therapeutic agents, in addition to receiving better prognoses. The survival times of HNSCC patients with high CIDGS, in particular, were shorter. Moreover, CIDGS enabled accurate stratification of the response to immunotherapy and prognoses for bladder cancer. Niclosamide and ruxolitinib showed potential as therapeutic agents in HNSCC patients with high CIDGS. Conclusion CIDGS may be used for stratifying risks as well as for predicting the outcome of patients with HNSCC in a clinical setting.
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Affiliation(s)
- Xueying Hu
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi, China
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, Guangxi, China
| | - Haiqun Dong
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi, China
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, Guangxi, China
| | - Wen Qin
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi, China
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, Guangxi, China
| | - Ying Bin
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi, China
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, Guangxi, China
| | - Wenhua Huang
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi, China
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, Guangxi, China
| | - Min Kang
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi, China
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, Guangxi, China
| | - Rensheng Wang
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi, China
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, Guangxi, China
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Weng S, Fu H, Xu S, Li J. Validating core therapeutic targets for osteoporosis treatment based on integrating network pharmacology and informatics. SLAS Technol 2024; 29:100122. [PMID: 38364892 DOI: 10.1016/j.slast.2024.100122] [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: 05/17/2023] [Revised: 01/24/2024] [Accepted: 02/13/2024] [Indexed: 02/18/2024]
Abstract
OBJECTIVE Our goal was to find metabolism-related lncRNAs that were associated with osteoporosis (OP) and construct a model for predicting OP progression using these lncRNAs. METHODS The GEO database was employed to obtain gene expression profiles. The WGCNA technique and differential expression analysis were used to identify hypoxia-related lncRNAs. A Lasso regression model was applied to select 25 hypoxia-related genes, from which a classification model was created. Its robust classification performance was confirmed with an area under the ROC curve close to 1, as verified on the validation set. Concurrently, we constructed a ceRNA network based on these genes to unveil potential regulatory processes. Biologically active compounds of STZYD were identified using the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP) database. BATMAN was used to identify its targets, and we obtained OP-related genes from Malacards and DisGeNET, followed by identifying intersection genes with metabolism-related genes. A pharmacological network was then constructed based on the intersecting genes. The pharmacological network was further integrated with the ceRNA network, resulting in the creation of a comprehensive network that encompasses herb-active components, pathways, lncRNAs, miRNAs, and targets. Expression levels of hypoxia-related lncRNAs in mononuclear cells isolated from peripheral blood of OP and normal patients were subsequently validated using quantitative real-time PCR (qRT-PCR). Protein levels of RUNX2 were determined through a western blot assay. RESULTS CBFB, GLO1, NFKB2 and PIK3CA were identified as central therapeutic targets, and ADD3-AS1, DTX2P1-UPK3BP1-PMS2P11, TTTY1B, ZNNT1 and LINC00623 were identified as core lncRNAs. CONCLUSIONS Our work uncovers a possible therapeutic mechanism for STZYD, providing a potential therapeutic target for OP. In addition, a prediction model of metabolism-related lncRNAs of OP progression was constructed to provide a reference for the diagnosis of OP patients.
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Affiliation(s)
- Shiyang Weng
- Department of Trauma Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201600, China
| | - Huichao Fu
- Department of Trauma Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201600, China
| | - Shengxiang Xu
- Department of Orthopedic Surgery, the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang 310009, China.
| | - Jieruo Li
- Department of Sport Medicine, Institute of Orthopedics Diseases and Center for Joint Surgery and Sports Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
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Shen C, Zheng B, Chen Z, Zhang W, Chen X, Xu S, Ji J, Fang X, Shi C. Identification of prognostic models for glycosylation-related subtypes and tumor microenvironment infiltration characteristics in clear cell renal cell cancer. Heliyon 2024; 10:e27710. [PMID: 38515689 PMCID: PMC10955297 DOI: 10.1016/j.heliyon.2024.e27710] [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: 10/13/2023] [Revised: 03/04/2024] [Accepted: 03/05/2024] [Indexed: 03/23/2024] Open
Abstract
Background One of the most fatal forms of cancer of the urinary system, renal cell carcinoma (RCC), significantly negatively impacts human health. Recent research reveals that abnormal glycosylation contributes to the growth and spread of tumors. However, there is no information on the function of genes related to glycosylation in RCC. Methods In this study, we created a technique that can be used to guide the choice of immunotherapy and chemotherapy regimens for RCC patients while predicting their survival prognosis. The Cancer Genome Atlas (TCGA) provided us with patient information, while the GeneCards database allowed us to collect genes involved in glycosylation. GSE29609 was used as external validation to assess the accuracy of prognostic models. The "ConsensusClusterPlus" program created molecular subtypes based on genes relevant to glycosylation discovered using differential expression analysis and univariate Cox analysis. We examined immune cell infiltration as measured by estimate, CIBERSORT, TIMER, and ssGSEA algorithms, Tumor Immune Dysfunction and Exclusion (TIDE) and exclusion of tumour stemness indices (TSIs) based on glycosylation-related molecular subtypes and risk profiles. Stratification, somatic mutation, nomogram creation, and chemotherapy response prediction were carried out based on risk factors. Results We built and verified 16 gene signatures associated with the prognosis of ccRCC patients, which are independent prognostic variables, and identified glycosylation-related genes by bioinformatics research. Cluster 2 is associated with lower human leukocyte antigen expression, worse overall survival, higher immunological checkpoints, and higher immune escape scores. In addition, cluster 2 had significantly better angiogenic activity, mesenchymal EMT, and stem ability scores. Higher immune checkpoint genes and human leukocyte antigens are associated with lower overall survival and a higher risk score. Higher estimated and immune scores, lesser tumor purity, lower mesenchymal EMT, and higher stem scores were all characteristics of the high-risk group. High amounts of tumor-infiltrating lymphocytes, a high mutation load, and a high copy number alteration frequency were present in the high-risk group.Discussion.According to our research, the 16-gene prognostic signature may be helpful in predicting prognosis and developing individualized treatments for patients with renal clear cell carcinoma, which may result in new personalized management options for these patients.
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Affiliation(s)
- Cheng Shen
- Department of Urology, Affiliated Hospital 2 of Nantong University, China
- Medical Research Center, Affiliated Hospital 2 of Nantong University, China
| | - Bing Zheng
- Department of Urology, Affiliated Hospital 2 of Nantong University, China
| | - Zhan Chen
- Department of Urology, Affiliated Hospital 2 of Nantong University, China
- Medical Research Center, Affiliated Hospital 2 of Nantong University, China
| | - Wei Zhang
- Department of Urology, Affiliated Hospital 2 of Nantong University, China
| | - Xinfeng Chen
- Department of Urology, Affiliated Hospital 2 of Nantong University, China
| | - Siyang Xu
- Clinical Medicine Specialty, Xinglin College of Nantong University, China
| | - Jianfeng Ji
- Department of Burn and plastic surgery, Affiliated Hospital 2 of Nantong University, China
| | - Xingxing Fang
- Nephrology Department, Affiliated Hospital 2 of Nantong University, China
| | - Chunmei Shi
- Department of Urology, Affiliated Hospital 2 of Nantong University, China
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Xu H, Chen S, Li J, Weng S, Ren Y, Zhang Y, Wang L, Liu L, Guo C, Xing Z, Luo P, Cheng Q, Han X, Liu Z. Cellular Ligand-Receptor Perturbations Unravel MEIS2 as a Key Factor for the Aggressive Progression and Prognosis in Stage II/III Colorectal Cancer. J Proteome Res 2024; 23:760-774. [PMID: 38153233 DOI: 10.1021/acs.jproteome.3c00626] [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] [Indexed: 12/29/2023]
Abstract
Approximately 10-15% of stage II and 25-30% of stage III colorectal cancer (CRC) patients experience recurrence within 5 years after surgery, and existing taxonomies are insufficient to meet the needs of clinical precision treatment. Thus, robust biomarkers and precise management were urgently required to stratify stage II and III CRC and identify potential patients who will benefit from postoperative adjuvant therapy. Alongside, interactions of ligand-receptor pairs point to an emerging direction in tumor signaling with far-reaching implications for CRC, while their impact on tumor subtyping has not been elucidated. Herein, based on multiple large-sample multicenter cohorts and perturbations of the ligand-receptor interaction network, four well-characterized ligand-receptor-driven subtypes (LRDS) were established and further validated. These molecular taxonomies perform with unique heterogeneity in terms of molecular characteristics, immune and mutational landscapes, and clinical features. Specifically, MEIS2, a key LRDS4 factor, performs significant associations with proliferation, invasion, migration, and dismal prognosis of stage II/III CRC, revealing promising directions for prognostic assessment and individualized treatment of CRC patients. Overall, our study sheds novel insights into the implications of intercellular communication on stage II/III CRC from a ligand-receptor interactome perspective and revealed MEIS2 as a key factor in the aggressive progression and prognosis for stage II/III CRC.
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Affiliation(s)
- Hui Xu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
| | - Shuang Chen
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Jing Li
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
| | - Siyuan Weng
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
| | - Yuqing Ren
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
| | - Libo Wang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Long Liu
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Chunguang Guo
- Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Zhe Xing
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Peng Luo
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
| | - Quan Cheng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P. R. China
| | - Xinwei Han
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
| | - Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
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Wu Q, Fu X, He X, Liu J, Li Y, Ou C. Experimental prognostic model integrating N6-methyladenosine-related programmed cell death genes in colorectal cancer. iScience 2024; 27:108720. [PMID: 38299031 PMCID: PMC10829884 DOI: 10.1016/j.isci.2023.108720] [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: 07/15/2023] [Revised: 10/30/2023] [Accepted: 12/11/2023] [Indexed: 02/02/2024] Open
Abstract
Colorectal cancer (CRC) intricacies, involving dysregulated cellular processes and programmed cell death (PCD), are explored in the context of N6-methyladenosine (m6A) RNA modification. Utilizing the TCGA-COADREAD/CRC cohort, 854 m6A-related PCD genes are identified, forming the basis for a robust 10-gene risk model (CDRS) established through LASSO Cox regression. qPCR experiments using CRC cell lines and fresh tissues was performed for validation. The CDRS served as an independent risk factor for CRC and showed significant associations with clinical features, molecular subtypes, and overall survival in multiple datasets. Moreover, CDRS surpasses other predictors, unveiling distinct genomic profiles, pathway activations, and associations with the tumor microenvironment. Notably, CDRS exhibits predictive potential for drug sensitivity, presenting a novel paradigm for CRC risk stratification and personalized treatment avenues.
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Affiliation(s)
- Qihui Wu
- Department of Gynecology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Xiaodan Fu
- Department of Pathology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Xiaoyun He
- Departments of Ultrasound Imaging, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Jiaxin Liu
- Department of Pathology, School of Basic Medical Sciences, Central South University, Changsha 410078, China
| | - Yimin Li
- Department of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China
| | - Chunlin Ou
- Department of Pathology, Xiangya Hospital, Central South University, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha 410008, China
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Huang B, Xin C, Yan H, Yu Z. A Machine Learning Method for a Blood Diagnostic Model of Pancreatic Cancer Based on microRNA Signatures. Crit Rev Immunol 2024; 44:13-23. [PMID: 38421702 DOI: 10.1615/critrevimmunol.2023051250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
This study aimed to construct a blood diagnostic model for pancreatic cancer (PC) using miRNA signatures by a combination of machine learning and biological experimental verification. Gene expression profiles of patients with PC and transcriptome normalization data were obtained from the Gene Expression Omnibus (GEO) database. Using random forest algorithm, lasso regression algorithm, and multivariate cox regression analyses, the classifier of differentially expressed miRNAs was identified based on algorithms and functional properties. Next, the ROC curve analysis was used to evaluate the predictive performance of the diagnostic model. Finally, we analyzed the expression of two specific miRNAs in Capan-1, PANC-1, and MIA PaCa-2 pancreatic cells using qRT-PCR. Integrated microarray analysis revealed that 33 common miRNAs exhibited significant differences in expression profiles between tumor and normal groups (P value < 0.05 and |logFC| > 0.3). Pathway analysis showed that differentially expressed miRNAs were related to P00059 p53 pathway, hsa04062 chemokine signaling pathway, and cancer-related pathways including PC. In ENCORI database, the hsa-miR-4486 and hsa-miR-6075 were identified by random forest algorithm and lasso regression algorithm and introduced as major miRNA markers in PC diagnosis. Further, the receiver operating characteristic curve analysis achieved the area under curve score > 80%, showing good sensitivity and specificity of the two-miRNA signature model in PC diagnosis. Additionally, hsa-miR-4486 and hsa-miR-6075 genes expressions in three pancreatic cells were all up-regulated by qRT-PCR. In summary, these findings suggest that the two miRNAs, hsa-miR-4486 and hsa-miR-6075, could serve as valuable prognostic markers for PC.
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Affiliation(s)
- Bin Huang
- The Affiliated People's Hospital of Ningbo University
| | - Chang Xin
- Department of Hepatopancreatobiliary Surgery, The Affiliated People's Hospital of Ningbo University, Ningbo, Zhejiang, China
| | - Huanjun Yan
- Department of Hepatopancreatobiliary Surgery, The Affiliated People's Hospital of Ningbo University, Ningbo, Zhejiang, China
| | - Zhewei Yu
- Department of Hepatopancreatobiliary Surgery, The Affiliated People's Hospital of Ningbo University, Ningbo, Zhejiang, China
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Qin Y, Wen C, Wu H. CXCL10-based gene cluster model serves as a potential diagnostic biomarker for premature ovarian failure. PeerJ 2023; 11:e16659. [PMID: 38107572 PMCID: PMC10725173 DOI: 10.7717/peerj.16659] [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: 09/21/2023] [Accepted: 11/21/2023] [Indexed: 12/19/2023] Open
Abstract
Objective Premature ovarian failure (POF) is a disease with high clinical heterogeneity. Subsequently, its diagnosis is challenging. CXCL10 which is a small signaling protein involved in immune response and inflammation may have diagnostic potential in detection of premature ovarian insufficiency. Therefore, this study aimed to investigate CXCL10 based diagnostic biomarkers for POF. Methods Transcriptome data for POF was obtained from the Gene Expression Omnibus (GEO) database (GSE39501). Principal component analysis (PCA) assessed CXCL10 expression in patients with POF. The receiver operating characteristic (ROC) curve, analyzed using PlotROC, demonstrated the diagnostic potential of CXCL10 and CXCL10-based models for POF. Differentially expressed genes (DEGs) in the control group of POF were identified using DEbylimma. PlotVenn was used to determine the overlap between the POF-control group and the high-/low-expression CXCL10 groups. QuadrantPlot was employed to detect CXCL10-dysregulated genes in POF. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were conducted on DEGs using RunMulti Group cluster Profiler. A POF model was induced with cisplatin (DDP) using KGN cells. RT-qPCR and Western blot were used to measure the expression of CXCL10, apoptosis-related proteins, and peroxisome proliferator-activated receptor (PPAR) signaling pathway-related proteins in this model, following siRNA-mediated silencing of CXCL10. Flow cytometry was employed to assess the apoptosis of KGN cells after CXCL10 downregulation. Results The expression of CXCL10 is dysregulated in POF, and it shows promising diagnostic potential for POF, as evidenced by an area under the curve value of 1. In POF, we found 3,362 up-regulated and 3,969 down-regulated DEGs compared to healthy controls, while the high- and low-expression groups of POF (comprising samples above and below the median CXCL10 expression) exhibited 1,304 up-regulated and 1,315 down-regulated DEGs. Among these, 786 DEGs consistently displayed dysregulation in POF due to CXCL10 influence. Enrichment analysis indicated that the PPAR signaling pathway was activated by CXCL10 in POF. The CXCL10-based model (including CXCL10, Itga2, and Raf1) holds potential as a diagnostic biomarker for POF. Additionally, in the DDP-induced KGN cell model, interfering with CXCL10 expression promoted the secretion of estradiol, and reduced apoptosis. Furthermore, CXCL10 silencing led to decreased expression levels of PPARβ and long-chain acyl-CoA synthetase 1 compared to the Si-NC group. These results suggest that CXCL10 influences the progression of POF through the PPAR signaling pathway. Conclusion The CXCL10-based model, demonstrating perfect diagnostic accuracy for POF and comprising CXCL10, Itga2, and Raf1, holds potential as a valuable diagnostic biomarker. Thus, the expression levels of these genes may collectively provide valuable diagnostic information for POF.
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Affiliation(s)
- Ying Qin
- Department of Obstetrics and Gynecology, Guangzhou Women and Children’s Medical Center, Guangzhou, China
- Reproductive Medicine Center, Guangzhou Women and Children’s Medical Center, Guangzhou, China
| | - Canliang Wen
- Department of Obstetrics and Gynecology, Guangzhou Women and Children’s Medical Center, Guangzhou, China
| | - Huijiao Wu
- Reproductive Medicine Center, Guangzhou Women and Children’s Medical Center, Guangzhou, China
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10
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Wang H, Wang W, Wang Z, Li X. Transcriptomic correlates of cell cycle checkpoints with distinct prognosis, molecular characteristics, immunological regulation, and therapeutic response in colorectal adenocarcinoma. Front Immunol 2023; 14:1291859. [PMID: 38143740 PMCID: PMC10749195 DOI: 10.3389/fimmu.2023.1291859] [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: 09/10/2023] [Accepted: 11/22/2023] [Indexed: 12/26/2023] Open
Abstract
Backgrounds Colorectal adenocarcinoma (COAD), accounting for the most common subtype of colorectal cancer (CRC), is a kind of malignant digestive tumor. Some cell cycle checkpoints (CCCs) have been found to contribute to CRC progression, whereas the functional roles of a lot of CCCs, especially the integrated role of checkpoint mechanism in the cell cycle, remain unclear. Materials and methods The Genomic Data Commons (GDC) The Cancer Genome Atlas (TCGA) COAD cohort was retrieved as the training dataset, and GSE24551 and GSE29623 were downloaded from Gene Expression Omnibus (GEO) as the validation datasets. A total of 209 CCC-related genes were derived from the Gene Ontology Consortium and were subsequently enrolled in the univariate, multivariate, and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, finally defining a CCC signature. Cell proliferation and Transwell assay analyses were utilized to evaluate the functional roles of signature-related CCCs. The underlying CCC signature, molecular characteristics, immune-related features, and therapeutic response were finally estimated. The Genomics of Drug Sensitivity in Cancer (GDSC) database was employed for the evaluation of chemotherapeutic responses. Results The aberrant gene expression of CCCs greatly contributed to COAD development and progression. Univariate Cox regression analysis identified 27 CCC-related genes significantly affecting the overall survival (OS) of COAD patients; subsequently, LASSO analysis determined a novel CCC signature. Noticeably, CDK5RAP2, MAD1L1, NBN, RGCC, and ZNF207 were first identified to be correlated with the prognosis of COAD, and it was proven that all of them were significantly correlated with the proliferation and invasion of HCT116 and SW480 cells. In TCGA COAD cohort, CCC signature robustly stratified COAD patients into high and low CCC score groups (median OS: 57.24 months vs. unreached, p< 0.0001), simultaneously, with the good AUC values for OS prediction at 1, 2, and 3 years were 0.74, 0.78, and 0.77. Furthermore, the prognostic capacity of the CCC signature was verified in the GSE24551 and GSE29623 datasets, and the CCC signature was independent of clinical features. Moreover, a higher CCC score always indicated worse OS, regardless of clinical features, histological subtypes, or molecular subgroups. Intriguingly, functional enrichment analysis confirmed the CCC score was markedly associated with extracellular, matrix and immune (chemokine)-related signaling, cell cycle-related signaling, and metabolisms. Impressively, a higher CCC score was positively correlated with a majority of chemokines, receptors, immunostimulators, and anticancer immunity, indicating a relatively immune-promoting microenvironment. In addition, GSE173839, GSE25066, GSE41998, and GSE194040 dataset analyses of the underlying CCC signature suggested that durvalumab with olaparib and paclitaxel, taxane-anthracycline chemotherapy, neoadjuvant cyclophosphamide/doxorubicin with ixabepilone or paclitaxel, and immunotherapeutic strategies might be suitable for COAD patients with higher CCC score. Eventually, the GDSC database analysis showed that lower CCC scores were likely to be more sensitive to 5-fluorouracil, bosutinib, gemcitabine, gefitinib, methotrexate, mitomycin C, and temozolomide, while patients with higher CCC score seemed to have a higher level of sensitivity to bortezomib and elesclomol. Conclusion The novel CCC signature exhibited a good ability for prognosis prediction for COAD patients, and the CCC score was found to be highly correlated with molecular features, immune-related characteristics, and therapeutic responses, which would greatly promote clinical management and precision medicine for COAD.
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Affiliation(s)
- Heng Wang
- Department of Colorectal Surgery, Shanghai Yangpu Hospital of Traditional Chinese Medicine, Shanghai, China
| | - Wei Wang
- Department of Colorectal Surgery, The First Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Zhen Wang
- Department of Colorectal Surgery, The First Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Xu Li
- Department of Colorectal Surgery, The First Affiliated Hospital of Naval Medical University, Shanghai, China
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Wang X, Liu Y, Zhou M, Yu L, Si Z. m6A modified BACE1-AS contributes to liver metastasis and stemness-like properties in colorectal cancer through TUFT1 dependent activation of Wnt signaling. J Exp Clin Cancer Res 2023; 42:306. [PMID: 37986103 PMCID: PMC10661562 DOI: 10.1186/s13046-023-02881-0] [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: 07/11/2023] [Accepted: 11/01/2023] [Indexed: 11/22/2023] Open
Abstract
BACKGROUND Liver metastasis is one of the most important reasons for high mortality of colorectal cancer (CRC). Growing evidence illustrates that lncRNAs play a critical role in CRC liver metastasis. Here we described a novel function and mechanisms of BACE1-AS promoting CRC liver metastasis. METHODS qRT-PCR and in situ hybridization were performed to examine the BACE1-AS level in CRC. IGF2BP2 binding to m6A motifs in BACE1-AS was determined by RIP assay and S1m-tagged immunoprecipitation. Transwell assay and liver metastasis mice model experiments were performed to examine the metastasis capabilities of BACE1-AS knockout cells. Stemness-like properties was examined by tumor sphere assay and the expression of stemness biomarkers. Microarray data were acquired to analyze the signaling pathways involved in BACE1-AS promoting CRC metastasis. RESULTS BACE1-AS is the most up-regulated in metastatic CRC associated with unfavorable prognosis. Sequence blast revealed two m6A motifs in BACE1-AS. IGF2BP2 binding to these two m6A motifs is required for BACE1-AS boost in metastatic CRC. m6A modified BACE1-AS drives CRC cells migration and invasion and liver metastasis both in vitro and in vivo. Moreover, BACE1-AS maintains the stemness-like properties of CRC cells. Mechanically, BACE1-AS promoted TUFT1 expression by ceRNA network through miR-214-3p. CRC patients with such ceRNA network suffer poorer prognosis than ceRNA-negative patients. Depletion of TUFT1 mimics BACE1-AS loss. BACE1-AS activated Wnt signaling pathway in a TUFT1 dependent manner. BACE1-AS/miR-214-3p/TUFT1/Wnt signaling regulatory axis is essential for CRC liver metastasis. Pharmacologic inhibition of Wnt signaling pathway repressed liver metastasis and stemness-like features in BACE1-AS over-expressed CRC cells. CONCLUSION Our study demonstrated BACE1-AS as a novel target of IGF2BP2 through m6A modification. m6A modified BACE1-AS promotes CRC liver metastasis through TUFT1 dependent activation of Wnt signaling pathway. Thus, targeting BACE1-AS and its downstream Wnt signaling pathways may provide a new opportunity for metastatic CRC intervention and treatment.
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Affiliation(s)
- Xidi Wang
- Central Laboratory of the Medical Research Center, The First Affiliated Hospital of Ningbo University, 247 Renmin Road, Jiangbei District, Ningbo, 315020, P. R. China.
- Health Science Center, Ningbo University, 818 Fenghua Road, Jiangbei District, Ningbo, 315211, P. R. China.
| | - Yu Liu
- Health Science Center, Ningbo University, 818 Fenghua Road, Jiangbei District, Ningbo, 315211, P. R. China
| | - Miao Zhou
- Central Laboratory of the Medical Research Center, The First Affiliated Hospital of Ningbo University, 247 Renmin Road, Jiangbei District, Ningbo, 315020, P. R. China
| | - Lei Yu
- Department of Colorectal Cancer Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, P. R. China
| | - Zizhen Si
- Health Science Center, Ningbo University, 818 Fenghua Road, Jiangbei District, Ningbo, 315211, P. R. China.
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Yuan K, Hu D, Mo X, Zeng R, Wu B, Zhang Z, Hu R, Wang C. Novel diagnostic biomarkers of oxidative stress, immune- infiltration characteristics and experimental validation of SERPINE1 in colon cancer. Discov Oncol 2023; 14:206. [PMID: 37980291 PMCID: PMC10657345 DOI: 10.1007/s12672-023-00833-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 11/15/2023] [Indexed: 11/20/2023] Open
Abstract
BACKGROUND Colon cancer (CC) is a prevalent malignant tumor that affects the colon in the gastrointestinal tract. Its aggressive nature, strong invasiveness, and rapid progression make it a significant health concern. In addition, oxidative stress can lead to the production of reactive oxygen species (ROS) that surpass the body's antioxidant defense capacity, causing damage to proteins, lipids, and DNA, potentially promoting tumor development. However, the relationship between CC and oxidative stress requires further investigation. METHODS We collected gene expression data and clinical data from 473 CC patients from The Cancer Genome Atlas (TCGA) dataset. Additionally, we obtained 433 oxidative stress genes from Genecards ( https://www.genecards.org/ ). Using univariate, multivariate, and LASSO Cox regression analyses, we developed predictive models for oxidative stress-related genes in CC patients. To validate the models, we utilized data from the Gene Expression Omnibus (GEO) database. We assessed the accuracy of the models through various techniques, including the creation of a nomogram, receiver operating characteristic curve (ROC) analysis, and principal component analysis (PCA). The Cytoscape program was utilized to identify hub genes among differentially expressed genes (DEGs) in tumor patients using the TCGA dataset. Subsequently, we conducted survival analysis, clinical relevance analysis, and immune cell relevance analysis for the intersected genes obtained by combining the hub genes with the genes from the predictive models. Moreover, we investigated the mRNA expression and potential functions of these intersected genes using a range of experimental approaches. RESULTS In both the TCGA and GSE17538 datasets, patients classified as high-risk had significantly shorter overall survival compared to those in the low-risk group (TCGA: p < 0.001; GSE17538: p = 0.010). As a result, we decided to further investigate the role of SERPINE1. Our survival analysis revealed that patients with high expression of SERPINE1 had a significantly lower probability of survival compared to those with low expression (p < 0.05). Additionally, our clinical correlation analysis showed a significant relationship between SERPINE1 expression and T, N, and M stages, as well as tumor grade. Furthermore, our immune infiltration correlation analysis demonstrated notable differences in multiple immune cells between the high- and low-expression groups of SERPINE1. To validate our findings, we conducted experimental tests and observed that knocking down SERPINE1 in colon cancer cells resulted in significant reductions in cell viability and proliferation. Interestingly, we also noticed an increase in oxidative stress parameters, such as ROS and MDA levels, while the levels of reduced GSH decreased upon SERPINE1 knockdown. These findings suggest that the antineoplastic effect of silencing SERPINE1 may be associated with the induction of oxidative stress. CONCLUSION In conclusion, this study introduces a new approach for the early diagnosis and treatment of CC, and further exploration of SERPINE1 could potentially lead to a significant advancement.
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Affiliation(s)
- Kaisheng Yuan
- Department of Metabolic and Bariatric Surgery, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, 510000, Guangdong, China
- The Guangdong-Hong Kong-Macao Joint University Laboratory of Metabolic and Molecular Medicine, Jinan University, Guangzhou, 510000, Guangdong, China
| | - Di Hu
- Department of Neurology and Stroke Centre, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, 510000, Guangdong, China
| | - Xiaocong Mo
- Department of Oncology, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, 510000, Guangdong, China
| | - Ruiqi Zeng
- Department of Urology, the Second People's Hospital of Yibin City, Yibin, 644000, Sichuan, China
| | - Bing Wu
- Department of Metabolic and Bariatric Surgery, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, 510000, Guangdong, China
- The Guangdong-Hong Kong-Macao Joint University Laboratory of Metabolic and Molecular Medicine, Jinan University, Guangzhou, 510000, Guangdong, China
| | - Zunhao Zhang
- Department of Metabolic and Bariatric Surgery, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, 510000, Guangdong, China
| | - Ruixiang Hu
- Department of Metabolic and Bariatric Surgery, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, 510000, Guangdong, China.
- The Guangdong-Hong Kong-Macao Joint University Laboratory of Metabolic and Molecular Medicine, Jinan University, Guangzhou, 510000, Guangdong, China.
| | - Cunchuan Wang
- Department of Metabolic and Bariatric Surgery, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, 510000, Guangdong, China.
- The Guangdong-Hong Kong-Macao Joint University Laboratory of Metabolic and Molecular Medicine, Jinan University, Guangzhou, 510000, Guangdong, China.
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Dou R, Han L, Yang C, Fang Y, Zheng J, Liang C, Song J, Wei C, Huang G, Zhong P, Liu K, Peng Q, Peng C, Xiong B, Wang S. Upregulation of LINC00501 by H3K27 acetylation facilitates gastric cancer metastasis through activating epithelial-mesenchymal transition and angiogenesis. Clin Transl Med 2023; 13:e1432. [PMID: 37867401 PMCID: PMC10591115 DOI: 10.1002/ctm2.1432] [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: 03/13/2023] [Revised: 09/11/2023] [Accepted: 09/30/2023] [Indexed: 10/24/2023] Open
Abstract
BACKGROUND The molecular mechanism of the significant role of long noncoding RNAs (lncRNAs) in the progression and metastasis of gastric cancer (GC) remains largely elusive. Our objective is to detect overexpressed lncRNA in GC and investigate its role in promoting epithelial-mesenchymal transition and tumour microenvironment remodel. METHODS LncRNA differential expression profile in GC was analysed using RNA microarrays. The level of LINC00501 was evaluated in both GC patient tissues and GC cell lines by quantitative reverse transcription PCR and large-scale (n = 304) tissue microarray. To explore the biological role and regulatory driver of LINC00501 in GC, various experimental techniques including Chromatin isolation by RNA purification (ChIRP), RNA immunoprecipitation (RIP), chromatin immunoprecipitation (ChIP) assay, dual luciferase assays were performed. RESULTS Clinically, it was observed that LINC00501 level was abnormal overexpression in GC tissue and was associated with GC progression and distant metastasis. Gain and loss molecular biological experiments suggested that LINC00501, promoted EMT process and angiogenesis of GC. Mechanically, the enrichment of H3K27 acetylation in LINC00501 promoter region contributed to the increase of LINC00501 in GC. LINC00501 transactivated transcription of SLUG, by recruiting hnRNPR to its promoter. The growth of GC was inhibited both in vitro and in vivo by suppressing the level of LINC00501 using pharmacological intervention from the histone acetyltransferase (HAT) inhibitor -C646. CONCLUSIONS This study suggests that LINC00501 promotes GC progression via hnRNPR/SLUG pathway, which indicates a promising biomarker and target for GC.
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Affiliation(s)
- Rongzhang Dou
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Department of Gastric and Colorectal Surgical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Hubei Key Laboratory of Tumor Biological Behaviors, Wuhan, Hubei, China
- Hubei Cancer Clinical Study Center, Wuhan, Hubei, China
| | - Lei Han
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Department of Gastric and Colorectal Surgical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Hubei Key Laboratory of Tumor Biological Behaviors, Wuhan, Hubei, China
- Hubei Cancer Clinical Study Center, Wuhan, Hubei, China
| | - Chaogang Yang
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Department of Gastric and Colorectal Surgical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Hubei Key Laboratory of Tumor Biological Behaviors, Wuhan, Hubei, China
- Hubei Cancer Clinical Study Center, Wuhan, Hubei, China
| | - Yan Fang
- Department of Obstetrics and Gynecology, Guangzhou Women and Children's Medical Center, Guangzhou, Guangdong, China
| | - Jinsen Zheng
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Department of Gastric and Colorectal Surgical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Hubei Key Laboratory of Tumor Biological Behaviors, Wuhan, Hubei, China
- Hubei Cancer Clinical Study Center, Wuhan, Hubei, China
| | - Chenxi Liang
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Department of Gastric and Colorectal Surgical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Hubei Key Laboratory of Tumor Biological Behaviors, Wuhan, Hubei, China
- Hubei Cancer Clinical Study Center, Wuhan, Hubei, China
| | - Jialin Song
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Department of Gastric and Colorectal Surgical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Hubei Key Laboratory of Tumor Biological Behaviors, Wuhan, Hubei, China
- Hubei Cancer Clinical Study Center, Wuhan, Hubei, China
| | - Chen Wei
- Department of Internal Medicine, Affiliated Tumor Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Guoquan Huang
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Department of Gastric and Colorectal Surgical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Hubei Key Laboratory of Tumor Biological Behaviors, Wuhan, Hubei, China
- Hubei Cancer Clinical Study Center, Wuhan, Hubei, China
| | - Panyi Zhong
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Department of Gastric and Colorectal Surgical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Hubei Key Laboratory of Tumor Biological Behaviors, Wuhan, Hubei, China
- Hubei Cancer Clinical Study Center, Wuhan, Hubei, China
| | - Keshu Liu
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Department of Gastric and Colorectal Surgical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Hubei Key Laboratory of Tumor Biological Behaviors, Wuhan, Hubei, China
- Hubei Cancer Clinical Study Center, Wuhan, Hubei, China
| | - Qian Peng
- Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chunwei Peng
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Department of Gastric and Colorectal Surgical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Hubei Key Laboratory of Tumor Biological Behaviors, Wuhan, Hubei, China
- Hubei Cancer Clinical Study Center, Wuhan, Hubei, China
| | - Bin Xiong
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Department of Gastric and Colorectal Surgical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Hubei Key Laboratory of Tumor Biological Behaviors, Wuhan, Hubei, China
- Hubei Cancer Clinical Study Center, Wuhan, Hubei, China
| | - Shuyi Wang
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Department of Gastric and Colorectal Surgical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
- Hubei Key Laboratory of Tumor Biological Behaviors, Wuhan, Hubei, China
- Hubei Cancer Clinical Study Center, Wuhan, Hubei, China
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Woischke C, Michl M, Neumann J. [Molecular pathology of colorectal cancer]. PATHOLOGIE (HEIDELBERG, GERMANY) 2023; 44:279-286. [PMID: 37277480 DOI: 10.1007/s00292-023-01201-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/20/2023] [Indexed: 06/07/2023]
Abstract
In recent years, the treatment of colorectal carcinoma has experienced increasing individualization. In addition to RAS and BRAF mutational status that is firmly established in routine diagnostics, new therapeutic options evolved based on MSI and HER2 status as well as primary tumour localization. Offering the best targeted options in therapy requires new evidence-based decision-making algorithms regarding timing and scope of molecular pathological diagnostics in order for patients to receive an optimized therapy according to current treatment guidelines. New targeted therapies, some of which are about to be approved and for which pathology has to provide new molecular pathological biomarkers, will also play an increasingly important role in the future.
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Affiliation(s)
- Christine Woischke
- Pathologisches Institut, Medizinische Fakultät, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland
| | - Marlies Michl
- Medizinische Klinik und Poliklinik III, Klinikum der Universität München, Ludwig-Maximilians-Universität München, München, Deutschland
- Facharztpraxis für Innere Medizin, Hämatologie und Onkologie mit Tagesklinik, Praxis Dr. Michl, München, Deutschland
| | - Jens Neumann
- Pathologisches Institut, Medizinische Fakultät, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland.
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Zhou J, Foroughi Pour A, Deirawan H, Daaboul F, Aung TN, Beydoun R, Ahmed FS, Chuang JH. Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality. EBioMedicine 2023; 94:104726. [PMID: 37499603 PMCID: PMC10388166 DOI: 10.1016/j.ebiom.2023.104726] [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: 01/12/2023] [Revised: 06/19/2023] [Accepted: 07/10/2023] [Indexed: 07/29/2023] Open
Abstract
BACKGROUND Colorectal cancers are the fourth most diagnosed cancer and the second leading cancer in number of deaths. Many clinical variables, pathological features, and genomic signatures are associated with patient risk, but reliable patient stratification in the clinic remains a challenging task. Here we assess how image, clinical, and genomic features can be combined to predict risk. METHODS We developed and evaluated integrative deep learning models combining formalin-fixed, paraffin-embedded (FFPE) whole slide images (WSIs), clinical variables, and mutation signatures to stratify colon adenocarcinoma (COAD) patients based on their risk of mortality. Our models were trained using a dataset of 108 patients from The Cancer Genome Atlas (TCGA), and were externally validated on newly generated dataset from Wayne State University (WSU) of 123 COAD patients and rectal adenocarcinoma (READ) patients in TCGA (N = 52). FINDINGS We first observe that deep learning models trained on FFPE WSIs of TCGA-COAD separate high-risk (OS < 3 years, N = 38) and low-risk (OS > 5 years, N = 25) patients (AUC = 0.81 ± 0.08, 5 year survival p < 0.0001, 5 year relative risk = 1.83 ± 0.04) though such models are less effective at predicting overall survival (OS) for moderate-risk (3 years < OS < 5 years, N = 45) patients (5 year survival p-value = 0.5, 5 year relative risk = 1.05 ± 0.09). We find that our integrative models combining WSIs, clinical variables, and mutation signatures can improve patient stratification for moderate-risk patients (5 year survival p < 0.0001, 5 year relative risk = 1.87 ± 0.07). Our integrative model combining image and clinical variables is also effective on an independent pathology dataset (WSU-COAD, N = 123) generated by our team (5 year survival p < 0.0001, 5 year relative risk = 1.52 ± 0.08), and the TCGA-READ data (5 year survival p < 0.0001, 5 year relative risk = 1.18 ± 0.17). Our multicenter integrative image and clinical model trained on combined TCGA-COAD and WSU-COAD is effective in predicting risk on TCGA-READ (5 year survival p < 0.0001, 5 year relative risk = 1.82 ± 0.13) data. Pathologist review of image-based heatmaps suggests that nuclear size pleomorphism, intense cellularity, and abnormal structures are associated with high-risk, while low-risk regions have more regular and small cells. Quantitative analysis shows high cellularity, high ratios of tumor cells, large tumor nuclei, and low immune infiltration are indicators of high-risk tiles. INTERPRETATION The improved stratification of colorectal cancer patients from our computational methods can be beneficial for treatment plans and enrollment of patients in clinical trials. FUNDING This study was supported by the National Cancer Institutes (Grant No. R01CA230031 and P30CA034196). The funders had no roles in study design, data collection and analysis or preparation of the manuscript.
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Affiliation(s)
- Jie Zhou
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA; Department of Genetics and Genome Sciences, UCONN Health, Farmington, CT, USA
| | | | - Hany Deirawan
- Department of Pathology, Wayne State University, Detroit, MI, USA; Department of Dermatology, Wayne State University, Detroit, MI, USA
| | - Fayez Daaboul
- Department of Pathology, Wayne State University, Detroit, MI, USA
| | - Thazin Nwe Aung
- Department of Pathology, Yale University, New Haven, CT, USA
| | - Rafic Beydoun
- Department of Pathology, Wayne State University, Detroit, MI, USA
| | | | - Jeffrey H Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA; Department of Genetics and Genome Sciences, UCONN Health, Farmington, CT, USA.
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Yu D, Lu Z, Wang R, Xiang Y, Li H, Lu J, Zhang L, Chen H, Li W, Luan X, Chen L. FXR agonists for colorectal and liver cancers, as a stand-alone or in combination therapy. Biochem Pharmacol 2023; 212:115570. [PMID: 37119860 DOI: 10.1016/j.bcp.2023.115570] [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: 02/21/2023] [Revised: 04/20/2023] [Accepted: 04/21/2023] [Indexed: 05/01/2023]
Abstract
Farnesoid X receptor (FXR, NR1H4) is generally considered as a tumor suppressor of colorectal and liver cancers. The interaction between FXR, bile acids (BAs) and gut microbiota is closely associated with an increased risk of colorectal and liver cancers. Increasing evidence shows that FXR agonists may be potential therapeutic agents for colorectal and liver cancers. However, FXR agonists alone do not produce the desired results due to the complicated pathogenesis and single therapeutic mechanism, which suggests that effective treatments will require a multimodal approach. Based on the principle of improvingefficacy andreducingside effects, combination therapy is currently receiving considerable attention. In this review, colorectal and liver cancers are grouped together to discuss the effects of FXR agonists alone or in combination for combating the two cancers. We hope that this review will provide a theoretical basis for the clinical application of novel FXR agonists or combination with FXR agonists against colorectal and liver cancers.
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Affiliation(s)
- Danmei Yu
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Zhou Lu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Ruyu Wang
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Yusen Xiang
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Hongtao Li
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Jiani Lu
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Lijun Zhang
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Hongzhuan Chen
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Xin Luan
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
| | - Lili Chen
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
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Li G, Zhang H, Zhao J, Liu Q, Jiao J, Yang M, Wu C. Machine learning-based construction of immunogenic cell death-related score for improving prognosis and response to immunotherapy in melanoma. Aging (Albany NY) 2023; 15:2667-2688. [PMID: 37036471 PMCID: PMC10120887 DOI: 10.18632/aging.204636] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 03/24/2023] [Indexed: 04/09/2023]
Abstract
BACKGROUND Immunogenic cell death (ICD) is a form of regulated cell death (RCD) which could drive the activation of the innate and adaptive immune responses. In this work, we aimed to develop an ICD-related signature to facilitate the assessment of prognosis and immunotherapy response for melanoma patients. METHODS A set of machine learning methods, including consensus clustering, non-negative matrix factorization (NMF) method and least absolute shrinkage and selection operator (LASSO) logistic regression model, and bioinformatics analytic tools were integrated to construct an ICD-related risk score (ICDscore). CIBERSORT and ESTIMATE algorithm were used to evaluate the infiltration of immune cells. The 'pRRophetic' package in R and 6 cohorts of melanoma patients receiving immunotherapy were used for therapy sensitivity analyses. The predictive performance between ICDscore with other mRNA signatures were also compared. RESULTS The ICDscore could predict prognosis and immunotherapy response in multiple cohorts, and displayed superior performance than other forms of cell death-related signatures or 52 published signatures. The melanoma patients with low ICDscore were marked with high infiltration of immune cells, high expression of immune checkpoint inhibitor-related genes, and increased tumor mutation burden. CONCLUSIONS In conclusion, we constructed a stable and robust ICD-related signature for evaluating the prognosis and benefits of immunotherapy, and it could serve as a promising tool to guide decision-making and surveillance for individual melanoma patients.
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Affiliation(s)
- Guoyin Li
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan, China
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi’an, Shaanxi, China
- Academy of Medical Science, Zhengzhou University, Zhengzhou, Henan, China
| | - Huina Zhang
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan, China
| | - Jin Zhao
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan, China
| | - Qiongwen Liu
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan, China
| | - Jinke Jiao
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan, China
| | - Mingsheng Yang
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan, China
| | - Changjing Wu
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan, China
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18
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Xin C, Lai Y, Ji L, Wang Y, Li S, Hao L, Zhang W, Meng R, Xu J, Hong Y, Lou Z. A novel 9-gene signature for the prediction of postoperative recurrence in stage II/III colorectal cancer. Front Genet 2023; 13:1097234. [PMID: 36704343 PMCID: PMC9871489 DOI: 10.3389/fgene.2022.1097234] [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: 11/18/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023] Open
Abstract
Background: Individualized recurrence risk prediction in patients with stage II/III colorectal cancer (CRC) is crucial for making postoperative treatment decisions. However, there is still a lack of effective approaches for identifying patients with stage II and III CRC at a high risk of recurrence. In this study, we aimed to establish a credible gene model for improving the risk assessment of patients with stage II/III CRC. Methods: Recurrence-free survival (RFS)-related genes were screened using Univariate Cox regression analysis in GSE17538, GSE39582, and GSE161158 cohorts. Common prognostic genes were identified by Venn diagram and subsequently subjected to least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis for signature construction. Kaplan-Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were used to assess the predictive accuracy and superiority of our risk model. Single-sample gene set enrichment analysis (ssGSEA) was employed to investigate the relationship between the infiltrative abundances of immune cells and risk scores. Genes significantly associated with the risk scores were identified to explore the biological implications of the 9-gene signature. Results: Survival analysis identified 347 RFS-related genes. Using these genes, a 9-gene signature was constructed, which was composed of MRPL41, FGD3, RBM38, SPINK1, DKK1, GAL3ST4, INHBB, CTB-113P19.1, and FAM214B. K-M curves verified the survival differences between the low- and high-risk groups classified by the 9-gene signature. The area under the curve (AUC) values of this signature were close to or no less than the previously reported prognostic signatures and clinical factors, suggesting that this model could provide improved RFS prediction. The ssGSEA algorithm estimated that eight immune cells, including regulatory T cells, were aberrantly infiltrated in the high-risk group. Furthermore, the signature was associated with multiple oncogenic pathways, including cell adhesion and angiogenesis. Conclusion: A novel RFS prediction model for patients with stage II/III CRC was constructed using multicohort validation. The proposed signature may help clinicians better manage patients with stage II/III CRC.
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Affiliation(s)
- Cheng Xin
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
| | - Yi Lai
- Department of Head and Neck Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | | | - Ye Wang
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
| | - Shihao Li
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
| | - Liqiang Hao
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
| | - Wei Zhang
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
| | - Ronggui Meng
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
| | - Jun Xu
- Department of Gastrointestinal Surgery, Changhai Hospital, Shanghai, China,*Correspondence: Jun Xu, ; Yonggang Hong, ; Zheng Lou,
| | - Yonggang Hong
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China,*Correspondence: Jun Xu, ; Yonggang Hong, ; Zheng Lou,
| | - Zheng Lou
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China,*Correspondence: Jun Xu, ; Yonggang Hong, ; Zheng Lou,
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Zheng X, Zhang C, Zheng D, Guo Q, Maierhaba M, Xue L, Zeng X, Wu Y, Gao W. An original cuproptosis-related genes signature effectively influences the prognosis and immune status of head and neck squamous cell carcinoma. Front Genet 2023; 13:1084206. [PMID: 36685880 PMCID: PMC9845781 DOI: 10.3389/fgene.2022.1084206] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 12/15/2022] [Indexed: 01/06/2023] Open
Abstract
Background: Recently, a non-apoptotic cell death pathway that is dependent on the presence of copper ions was proposed, named as cuproptosis. Cuproptosis have been found to have a strong association with the clinical progression and prognosis of several cancers. Head and neck squamous cell carcinoma (HNSC) are among the most common malignant tumors, with a 5-year relative survival rate ranging between 40% and 50%. The underlying mechanisms and clinical significance of cuproptosis-related genes (CRGs) in HNSC progression have not been clarified. Methods: In this study, expression pattern, biological functions, Immunohistochemistry (IHC), gene variants and immune status were analyzed to investigate the effects of CRGs on HNSC progression. Moreover, a 12-CRGs signature and nomogram were also constructed for prognosis prediction of HNSC. Results: The results revealed that some CRGs were dysregulated, had somatic mutations, and CNV in HNSC tissues. Among them, ISCA2 was found to be upregulated in HNSC and was strongly correlated with the overall survival (OS) of HNSC patients (HR = 1.13 [1.01-1.26], p-value = 0.0331). Functionally, CRGs was mainly associated with the TCA cycle, cell cycle, iron-sulfur cluster assembly, p53 signaling pathway, chemical carcinogenesis, and carbon metabolism in cancer. A 12-CRGs signature for predicting the OS was constructed which included, CAT, MTFR1L, OXA1L, POLE, NTHL1, DNA2, ATP7B, ISCA2, GLRX5, NDUFA1, and NDUFB2. This signature showed good prediction performance on the OS (HR = 5.3 [3.4-8.2], p-value = 3.4e-13) and disease-specific survival (HR = 6.4 [3.6-11], p-value = 2.4e-10). Furthermore, 12-CRGs signature significantly suppressed the activation of CD4+ T cells and antigen processing and presentation. Finally, a nomogram based on a 12-CRGs signature and clinical features was constructed which showed a significantly adverse effect on OS (HR = 1.061 [1.042-1.081], p-value = 1.6e-10) of HNSC patients. Conclusion: This study reveals the association of CRGs with the progression of HNSC based on multi-omics analysis. The study of CRGs is expected to improve clinical diagnosis, immunotherapeutic responsiveness and prognosis prediction of HNSC.
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Affiliation(s)
- Xiwang Zheng
- Shanxi Key Laboratory of Otorhinolaryngology Head and Neck Cancer, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China,Shanxi Province Clinical Medical Research Center for Precision Medicine of Head and Neck Cancer, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Chunming Zhang
- Shanxi Key Laboratory of Otorhinolaryngology Head and Neck Cancer, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China,Shanxi Province Clinical Medical Research Center for Precision Medicine of Head and Neck Cancer, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China,Department of Otolaryngology Head and Neck Surgery, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Defei Zheng
- Department of Hematology/Oncology, Children’s Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Qingbo Guo
- Shanxi Key Laboratory of Otorhinolaryngology Head and Neck Cancer, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China,Shanxi Province Clinical Medical Research Center for Precision Medicine of Head and Neck Cancer, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Mijiti Maierhaba
- Shanxi Key Laboratory of Otorhinolaryngology Head and Neck Cancer, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China,Shanxi Province Clinical Medical Research Center for Precision Medicine of Head and Neck Cancer, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Lingbin Xue
- Department of Otolaryngology Head and Neck Surgery, Longgang Otolaryngology Hospital, Shenzhen, Guangdong, China,Shenzhen Institute of Otolaryngology and Key Laboratory of Otolaryngology, Longgang Otolaryngology Hospital, Shenzhen, Guangdong, China
| | - Xianhai Zeng
- Department of Otolaryngology Head and Neck Surgery, Longgang Otolaryngology Hospital, Shenzhen, Guangdong, China,Shenzhen Institute of Otolaryngology and Key Laboratory of Otolaryngology, Longgang Otolaryngology Hospital, Shenzhen, Guangdong, China,*Correspondence: Xianhai Zeng, ; Yongyan Wu, ; Wei Gao,
| | - Yongyan Wu
- Department of Otolaryngology Head and Neck Surgery, Longgang Otolaryngology Hospital, Shenzhen, Guangdong, China,Shenzhen Institute of Otolaryngology and Key Laboratory of Otolaryngology, Longgang Otolaryngology Hospital, Shenzhen, Guangdong, China,*Correspondence: Xianhai Zeng, ; Yongyan Wu, ; Wei Gao,
| | - Wei Gao
- Department of Otolaryngology Head and Neck Surgery, Longgang Otolaryngology Hospital, Shenzhen, Guangdong, China,Shenzhen Institute of Otolaryngology and Key Laboratory of Otolaryngology, Longgang Otolaryngology Hospital, Shenzhen, Guangdong, China,*Correspondence: Xianhai Zeng, ; Yongyan Wu, ; Wei Gao,
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20
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Ershov P, Poyarkov S, Konstantinova Y, Veselovsky E, Makarova A. Transcriptomic Signatures in Colorectal Cancer Progression. Curr Mol Med 2023; 23:239-249. [PMID: 35490318 DOI: 10.2174/1566524022666220427102048] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/05/2021] [Accepted: 03/09/2022] [Indexed: 02/08/2023]
Abstract
AIMS Due to a large number of identified hub-genes encoding key molecular regulators, which are involved in signal transduction and metabolic pathways in cancers, it is relevant to systemize and update these findings. BACKGROUND Colorectal cancer (CRC) is the third leading cause of cancer death in the world, with high metastatic potential. Elucidating the pathogenic mechanisms and selection of novel biomarkers in CRC is of great clinical significance. OBJECTIVE This analytical review aims at the systematization of bioinformatics and experimental identification of hub-genes associated with CRC for a more consolidated understanding of common features in networks and pathways in CRC progression as well as hub-genes selection. RESULTS In total, 301 hub-genes were derived from 40 articles. The "core" consisted of 28 hub-genes (CCNB1, LPAR1, BGN, CXCL3, COL1A2, UBE2C, NMU, COL1A1, CXCL2, CXCL11, CDK1, TOP2A, AURKA, SST, CXCL5, MMP3, CCND1, TIMP1, CXCL8, CXCL1, CXCL12, MYC, CCNA2, GCG, GUCA2A, PAICS, PYY and THBS2) mentioned in not less than three articles and having clinical significance in cancerassociated pathways. Of them, there were two discrete clusters enriched in chemokine signaling and cell cycle regulatory genes. High expression levels of BGN and TIMP1 and low expression levels of CCNB1, CXCL3, CXCL2, CXCL2 and PAICS were associated with unfavorable overall survival of patients with CRC. Differently expressed genes such as LPAR1, SST, CXCL12, GUCA2A, and PYY were shown as down regulated, whereas BGN, CXCL3, UBE2C, NMU, CXCL11, CDK1, TOP2A, AURKA, MMP3, CCND1, CXCL1, MYC, CCNA2, PAICS were up regulated genes in CRC. It was also found that MMP3, THBS2, TIMP1 and CXCL12 genes were associated with metastatic CRC. Network analysis in ONCO.IO showed that upstream master regulators RELA, STAT3, SOX2, FOXM1, SMAD3 and NF-kB were connected with "core" hub-genes. Conclusión: Results obtained are of useful fundamental information on revealing the mechanism of pathogenicity, cellular target selection for optimization of therapeutic interventions, as well as transcriptomics prognostic and predictive biomarkers development.
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Affiliation(s)
- Pavel Ershov
- Department of Analysis and Forecasting of Medical and Biological Health Risks, Federal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks" of the Federal Medical Biological Agency, Moscow, Russia
| | - Stanislav Poyarkov
- Department of Analysis and Forecasting of Medical and Biological Health Risks, Federal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks" of the Federal Medical Biological Agency, Moscow, Russia
| | - Yulia Konstantinova
- Oncology Department, Federal Research and Clinical Center of Specialized Kinds of Medical Care and Medical Technology of the Federal Medical Biological Agency, Moscow, Russia
| | - Egor Veselovsky
- Department of Analysis and Forecasting of Medical and Biological Health Risks, Federal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks" of the Federal Medical Biological Agency, Moscow, Russia
| | - Anna Makarova
- Department of Analysis and Forecasting of Medical and Biological Health Risks, Federal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks" of the Federal Medical Biological Agency, Moscow, Russia
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21
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Survival Contradiction in Stage II, IIIA, And IIIB Colon Cancer: A Surveillance, Epidemiology, and End Result-Based Analysis. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:4088117. [PMID: 36437824 PMCID: PMC9683985 DOI: 10.1155/2022/4088117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/28/2022] [Accepted: 11/07/2022] [Indexed: 11/18/2022]
Abstract
There exists an inconsistency between stage and survival in the current American Joint Committee on Cancer (AJCC) staging system for colon cancer. In this study, we compared the clinicopathological characteristics and prognosis of colon cancer patients with stage II, IIIA, and IIIB disease based on the surveillance, epidemiology, and end results (SEER) database. Kaplan-Meier analysis was used to generate overall survival (OS) and cancer-specific survival (CSS) curves. The Cox regression was employed to identify risk factors. The competing risk model was completed by the cumulative incidence function and Gray's test. In the final population of 31,361 colon cancer patients, Kaplan-Meier curve analysis showed that stage IIIA had the highest OS and CSS, followed by stage IIA and IIIB, and IIB and IIC showed the worst OS and CSS. In the Cox model, the stage was proven to be an independent prognostic factor. In the competing risk model, stage IIIA colon cancer patients had the lowest 5-year cancer-specific death rate in stages II, IIIA, and IIIB. In conclusion, the prognosis of colon cancer patients in stage IIA was worse than that of patients in stage IIIA, while the survival rate of stage IIB and IIC was lower than that of stage IIIB.
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22
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Xiong Q, Zhang Y, Li J, Zhu Q. Small Non-Coding RNAs in Human Cancer. Genes (Basel) 2022; 13:genes13112072. [PMID: 36360311 PMCID: PMC9690286 DOI: 10.3390/genes13112072] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/06/2022] [Accepted: 11/07/2022] [Indexed: 11/11/2022] Open
Abstract
Small non-coding RNAs are widespread in the biological world and have been extensively explored over the past decades. Their fundamental roles in human health and disease are increasingly appreciated. Furthermore, a growing number of studies have investigated the functions of small non-coding RNAs in cancer initiation and progression. In this review, we provide an overview of the biogenesis of small non-coding RNAs with a focus on microRNAs, PIWI-interacting RNAs, and a new class of tRNA-derived small RNAs. We discuss their biological functions in human cancer and highlight their clinical application as molecular biomarkers or therapeutic targets.
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Affiliation(s)
- Qunli Xiong
- Department of Abdominal Oncology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yaguang Zhang
- State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Junjun Li
- Department of Radiation Oncology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Qing Zhu
- Department of Abdominal Oncology, West China Hospital, Sichuan University, Chengdu 610041, China
- Correspondence:
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Construction of a Colorectal Cancer Prognostic Risk Model and Screening of Prognostic Risk Genes Using Machine-Learning Algorithms. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9408839. [PMID: 36267311 PMCID: PMC9578894 DOI: 10.1155/2022/9408839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/14/2022] [Accepted: 09/19/2022] [Indexed: 12/09/2022]
Abstract
This study is aimed at constructing a prognostic risk model for colorectal cancer (CRC) using machine-learning algorithms to provide accurate staging and screening of credible prognostic risk genes. We extracted CRC data from GSE126092 and GSE156355 of the Gene Expression Omnibus (GEO) database and datasets from TCGA to analyze the differentially expressed genes (DEGs) using bioinformatics analysis. Among the 330 shared DEGs related to CRC prognosis, we divided the analysis period into different phases and applied univariate COX regression, LASSO, and multivariate COX regression analysis. GO analysis and KEGG analysis revealed that the functions of these DEGs were primarily focused on cell cycle, DNA replication, cell mitosis, and other related functions, and this confirmed our results from a biological perspective. Finally, a prognostic risk model for CRC based on the CHGA, CLU, PLK1, AXIN2, NR3C2, IL17RB, GCG, and AJUBA genes was constructed, and the risk score enabled us to predict the prognosis for CRC. To obtain a comprehensive and accurate model, we used both internal and external evaluations, and the model was able to correctly differentiate patients with CRC into a high-risk group with poor prognosis and a low-risk group with good prognosis. The AUC values of the 3-, 5-, and 10-year survival ROC curves were 0.715, 0.721, and 0.777, respectively, according to the internal evaluation, and the AUC values were 0.606, 0.698, and 0.608, respectively, for the external evaluation using GSE39582 from the GEO database. We determined that CLU, PLK1, and IL17RB could be considered to be independent prognostic factors for CRC with significantly different expression (P < 0.05). Using machine-learning methods, a prognostic risk model comprised of eight genes was constructed. Not only does this model provide improved treatment guidance, but it also provides a novel perspective for analyzing survival conditions at a deeper biological level.
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Xu C, Li F, Liu Z, Yan C, Xiao J. A novel cell senescence-related IncRNA survival model associated with the tumor immune environment in colorectal cancer. Front Immunol 2022; 13:1019764. [PMID: 36275644 PMCID: PMC9583265 DOI: 10.3389/fimmu.2022.1019764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 09/22/2022] [Indexed: 12/16/2022] Open
Abstract
Long noncoding RNAs have a major role in tumorigenesis, development, and metastasis in colorectal cancer (CRC), participate in the regulation of cell senescence and are related to the prognosis of CRC. Therefore, it is important to validate cell senescence-related lncRNAs that correlate with prognosis in CRC.
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Affiliation(s)
- Chengfei Xu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chengdu Medical College, Chengdu Medical College, Chengdu, China
- School of Clinical Medicine, Chengdu Medical College, Chengdu, China
| | - Fanghan Li
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chengdu Medical College, Chengdu Medical College, Chengdu, China
- School of Clinical Medicine, Chengdu Medical College, Chengdu, China
| | - Zilin Liu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chengdu Medical College, Chengdu Medical College, Chengdu, China
- School of Clinical Medicine, Chengdu Medical College, Chengdu, China
| | - Chuanjing Yan
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chengdu Medical College, Chengdu Medical College, Chengdu, China
- School of Clinical Medicine, Chengdu Medical College, Chengdu, China
- *Correspondence: Chuanjing Yan, ; Jiangwei Xiao,
| | - Jiangwei Xiao
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chengdu Medical College, Chengdu Medical College, Chengdu, China
- School of Clinical Medicine, Chengdu Medical College, Chengdu, China
- *Correspondence: Chuanjing Yan, ; Jiangwei Xiao,
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Identification and Validation of Two Heterogeneous Molecular Subtypes and a Prognosis Predictive Model for Hepatocellular Carcinoma Based on Pyroptosis. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:8346816. [PMID: 36071875 PMCID: PMC9441383 DOI: 10.1155/2022/8346816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 06/27/2022] [Accepted: 08/09/2022] [Indexed: 12/24/2022]
Abstract
Hepatocellular carcinoma (HCC) is a worldwide malignant cancer with high incidence and mortality. Considering the high heterogeneity of HCC, clarifying molecular characteristics associated with HCC development could help improve patients' outcomes. Pyroptosis is a novel form of cell death and is noted to be implicated in HCC pathogenesis whereas its molecular feature in HCC is unclear. Thus, we intended to clarify the molecular characteristic as well as the clinical significance of pyroptosis for HCC. A systematic bioinformatics analysis was conducted among 40 pyroptosis-related genes based on The Cancer Genome Atlas, the International Cancer Genome Consortium, and the Gene Expression Omnibus databases. A total of 12 HCC-associated pyroptosis-related genes (HPRGs) were identified to be overexpressed in HCC tissues and significantly connected to patients' poor survival. Through consensus clustering based on the HPRGs' expression, we found patients could be stratified into two distinctive pyroptosis subtypes, PyLow and PyHigh. The PyHigh group owned a notable lower survival rate and a higher high-grade proportion compared with the PyLow subtype. Besides, patients' sensitivities to chemotherapeutic drugs also presented distinctive differences between the two subtypes. Indicated by pathway enrichment analysis and immune characteristic difference analysis, the distinctions between the pyroptosis subtypes may be related to tumor immunity. Further, a five-gene risk model composed of BAK1, CHMP4A, CHMP4B, DHX9, and GSDME was established. Subsequent analyses demonstrated that the model could credibly classify patients as low or high risk and was an independent prognostic indicator for HCC. Abnormal expressions of the five genes were validated by biological experiments and new bioinformatics analysis. In conclusion, this study recognized and verified two heterogeneous pyroptosis subtypes and a predictable prognosis model for HCC. Our work may help facilitate the clinical management and treatment of HCC and understand the functions of pyroptosis in oncology.
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Lei T, Zhang Y, Wang X, Liu W, Feng W, Song W. A Diagnostic Model Using Exosomal Genes for Colorectal Cancer. Front Genet 2022; 13:863747. [PMID: 35910195 PMCID: PMC9334773 DOI: 10.3389/fgene.2022.863747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 05/19/2022] [Indexed: 12/24/2022] Open
Abstract
Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Exosomes have great potential as liquid biopsy specimens due to their presence and stability in body fluids. However, the function and diagnostic values of exosomal genes in CRC are poorly understood. In the present study, exosomal data of CRC and healthy samples from the exoRBase 2.0 and Gene Expression Omnibus (GEO) databases were used, and 38 common exosomal genes were identified. Through the least absolute shrinkage and selection operator (Lasso) analysis, support vector machine recursive feature elimination (SVM-RFE) analysis, and logistic regression analysis, a diagnostic model of the training set was constructed based on 6 exosomal genes. The diagnostic model was internally validated in the test and exoRBase 2.0 database and externally validated in the GEO database. In addition, the co-expression analysis was used to cluster co-expression modules, and the enrichment analysis was performed on module genes. Then a protein–protein interaction and competing endogenous RNA network were constructed and 10 hub genes were identified using module genes. In conclusion, the results provided a comprehensive understanding of the functions of exosomal genes in CRC as well as a diagnostic model related to exosomal genes.
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Affiliation(s)
- Tianxiang Lei
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Laboratory of General Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yongxin Zhang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Laboratory of General Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaofeng Wang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Laboratory of General Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wenwei Liu
- Center for Digestive Disease, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Wei Feng
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Laboratory of General Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wu Song
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Wu Song,
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Chen Q, Wang Y, Liu Y, Xi B. ESRRG, ATP4A, and ATP4B as Diagnostic Biomarkers for Gastric Cancer: A Bioinformatic Analysis Based on Machine Learning. Front Physiol 2022; 13:905523. [PMID: 35812327 PMCID: PMC9262247 DOI: 10.3389/fphys.2022.905523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
Based on multiple bioinformatics methods and machine learning techniques, this study was designed to explore potential hub genes of gastric cancer with a diagnostic value. The novel biomarkers were detected through multiple databases of gastric cancer–related genes. The NCBI Gene Expression Omnibus (GEO) database was used to obtain gene expression files. Three hub genes (ESRRG, ATP4A, and ATP4B) were detected through a combination of weighted gene co-expression network analysis (WGCNA), gene–gene interaction network analysis, and supervised feature selection method. GEPIA2 was used to verify the differences in the expression levels of the hub genes in normal and cancer tissues in the RNA-seq levels of Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases. The objectivity of potential hub genes was also verified by immunohistochemistry in the Human Protein Atlas (HPA) database and transcription factor–hub gene regulatory network. Machine learning (ML) methods including data pre-processing, model selection and cross-validation, and performance evaluation were examined on the hub-gene expression profiles in five Gene Expression Omnibus datasets and verified on a GEO external validation (EV) dataset. Six supervised learning models (support vector machine, random forest, k-nearest neighbors, neural network, decision tree, and eXtreme Gradient Boosting) and one semi-supervised learning model (label spreading) were established to evaluate the diagnostic value of biomarkers. Among the six supervised models, the support vector machine (SVM) algorithm was the most effective one according to calculated performance metrics, including 0.93 and 0.99 area under the curve (AUC) scores on the test and external validation datasets, respectively. Furthermore, the semi-supervised model could also successfully learn and predict sample types, achieving a 0.986 AUC score on the EV dataset, even when 10% samples in the five GEO datasets were labeled. In conclusion, three hub genes (ATP4A, ATP4B, and ESRRG) closely related to gastric cancer were mined, based on which the ML diagnostic model of gastric cancer was conducted.
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Affiliation(s)
- Qiu Chen
- Medical College, Yangzhou University, Yangzhou, China
| | - Yu Wang
- College of Physics Science and Technology, Yangzhou University, Yangzhou, China
| | - Yongjun Liu
- College of Physics Science and Technology, Yangzhou University, Yangzhou, China
| | - Bin Xi
- College of Physics Science and Technology, Yangzhou University, Yangzhou, China
- *Correspondence: Bin Xi,
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Xu S, Zhou Y, Luo J, Chen S, Xie J, Liu H, Wang Y, Li Z. Integrated Analysis of a Ferroptosis-Related LncRNA Signature for Evaluating the Prognosis of Patients with Colorectal Cancer. Genes (Basel) 2022; 13:1094. [PMID: 35741856 PMCID: PMC9223081 DOI: 10.3390/genes13061094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 06/15/2022] [Accepted: 06/15/2022] [Indexed: 12/18/2022] Open
Abstract
LncRNAs have been well known for their multiple functions in the tumorigenesis, development, and relapse of colorectal cancer (CRC). Accumulating studies demonstrated that the expression of lncRNAs can be regulated by ferroptosis, a biological process that has been revealed to suppress CRC progression. However, the functions and clinical implications of ferroptosis-associated lncRNAs in CRC remain largely unknown. We, herein, aim to construct a prognostic signature with ferroptosis-related lncRNAs for the prognostic estimation of CRC patients. Firstly, we identified the lncRNAs related to ferroptosis based on the RNA-Seq data of CRC from the TCGA database. The univariate and multivariate Cox analyses were then performed to establish a prognostic signature composed of eight ferroptosis-related lncRNAs (AL161729.4, AC010973.2, CCDC144NL-AS1, AC009549.1, LINC01857, AP003555.1, AC099850.3, and AC008494.3). Furthermore, we divided the CRC patients into high- and low-risk groups based on the signature and found the overall survival (OS) of patients in the high-risk group was significantly shorter than that in the low-risk group (p = 3.31 × 10-11). Moreover, the patients in the high-risk groups had shorter recurrence-free survival (RFS) (p = 6.5 × 10-3) and disease-free survival (DFS) (p = 4.27 × 10-4), as well as higher tumor recurrence rate. Additionally, we found that the oncogenic pathways were enriched in the high-risk group, whereas the ferroptosis pathway that probably repressed CRC development was enriched in the low-risk group. In summary, our signature may provide a theoretical foundation for not only accurate judgment for prognosis but also evaluation for recurrence and metastasis in CRC patients.
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Affiliation(s)
- Shaohua Xu
- Research Institute of Hunan University in Chongqing, Chongqing 401120, China; (S.X.); (Y.Z.)
- Hunan Provincial Key Laboratory of Medical Virology, Institute of Pathogen Biology and Immunology of College of Biology, Hunan University, Changsha 410082, China; (J.L.); (S.C.); (J.X.); (H.L.)
| | - Yanjie Zhou
- Research Institute of Hunan University in Chongqing, Chongqing 401120, China; (S.X.); (Y.Z.)
- Hunan Provincial Key Laboratory of Medical Virology, Institute of Pathogen Biology and Immunology of College of Biology, Hunan University, Changsha 410082, China; (J.L.); (S.C.); (J.X.); (H.L.)
| | - Junyun Luo
- Hunan Provincial Key Laboratory of Medical Virology, Institute of Pathogen Biology and Immunology of College of Biology, Hunan University, Changsha 410082, China; (J.L.); (S.C.); (J.X.); (H.L.)
| | - Su Chen
- Hunan Provincial Key Laboratory of Medical Virology, Institute of Pathogen Biology and Immunology of College of Biology, Hunan University, Changsha 410082, China; (J.L.); (S.C.); (J.X.); (H.L.)
| | - Jiahui Xie
- Hunan Provincial Key Laboratory of Medical Virology, Institute of Pathogen Biology and Immunology of College of Biology, Hunan University, Changsha 410082, China; (J.L.); (S.C.); (J.X.); (H.L.)
| | - Hui Liu
- Hunan Provincial Key Laboratory of Medical Virology, Institute of Pathogen Biology and Immunology of College of Biology, Hunan University, Changsha 410082, China; (J.L.); (S.C.); (J.X.); (H.L.)
| | - Yirong Wang
- Bioinformatics Center, College of Biology, Hunan University, Changsha 410082, China
| | - Zhaoyong Li
- Research Institute of Hunan University in Chongqing, Chongqing 401120, China; (S.X.); (Y.Z.)
- Hunan Provincial Key Laboratory of Medical Virology, Institute of Pathogen Biology and Immunology of College of Biology, Hunan University, Changsha 410082, China; (J.L.); (S.C.); (J.X.); (H.L.)
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Zhou L, Yu Y, Wen R, Zheng K, Jiang S, Zhu X, Sui J, Gong H, Lou Z, Hao L, Yu G, Zhang W. Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer. Front Oncol 2022; 12:863094. [PMID: 35619909 PMCID: PMC9127348 DOI: 10.3389/fonc.2022.863094] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 03/29/2022] [Indexed: 01/07/2023] Open
Abstract
Background Most prognostic signatures for colorectal cancer (CRC) are developed to predict overall survival (OS). Gene signatures predicting recurrence-free survival (RFS) are rarely reported, and postoperative recurrence results in a poor outcome. Thus, we aim to construct a robust, individualized gene signature that can predict both OS and RFS of CRC patients. Methods Prognostic genes that were significantly associated with both OS and RFS in GSE39582 and TCGA cohorts were screened via univariate Cox regression analysis and Venn diagram. These genes were then submitted to least absolute shrinkage and selection operator (LASSO) regression analysis and followed by multivariate Cox regression analysis to obtain an optimal gene signature. Kaplan-Meier (K-M), calibration curves and receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of this signature. A nomogram integrating prognostic factors was constructed to predict 1-, 3-, and 5-year survival probabilities. Function annotation and pathway enrichment analyses were used to elucidate the biological implications of this model. Results A total of 186 genes significantly associated with both OS and RFS were identified. Based on these genes, LASSO and multivariate Cox regression analyses determined an 8-gene signature that contained ATOH1, CACNB1, CEBPA, EPPHB2, HIST1H2BJ, INHBB, LYPD6, and ZBED3. Signature high-risk cases had worse OS in the GSE39582 training cohort (hazard ratio [HR] = 1.54, 95% confidence interval [CI] = 1.42 to 1.67) and the TCGA validation cohort (HR = 1.39, 95% CI = 1.24 to 1.56) and worse RFS in both cohorts (GSE39582: HR = 1.49, 95% CI = 1.35 to 1.64; TCGA: HR = 1.39, 95% CI = 1.25 to 1.56). The area under the curves (AUCs) of this model in the training and validation cohorts were all around 0.7, which were higher or no less than several previous models, suggesting that this signature could improve OS and RFS prediction of CRC patients. The risk score was related to multiple oncological pathways. CACNB1, HIST1H2BJ, and INHBB were significantly upregulated in CRC tissues. Conclusion A credible OS and RFS prediction signature with multi-cohort and cross-platform compatibility was constructed in CRC. This signature might facilitate personalized treatment and improve the survival of CRC patients.
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Affiliation(s)
- Leqi Zhou
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
| | - Yue Yu
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
| | - Rongbo Wen
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
| | - Kuo Zheng
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
| | - Siyuan Jiang
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
| | - Xiaoming Zhu
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
| | - Jinke Sui
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
| | - Haifeng Gong
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
| | - Zheng Lou
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
| | - Liqiang Hao
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
| | - Guanyu Yu
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
| | - Wei Zhang
- Department of Colorectal Surgery, Changhai Hospital, Shanghai, China
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Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4715998. [PMID: 35035840 PMCID: PMC8759849 DOI: 10.1155/2022/4715998] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 11/17/2021] [Indexed: 12/14/2022]
Abstract
The quantity of data required to give a valid analysis grows exponentially as machine learning dimensionality increases. In a single experiment, microarrays or gene expression profiling assesses and determines gene expression levels and patterns in various cell types or tissues. The advent of DNA microarray technology has enabled simultaneous intensive care of hundreds of gene expressions on a single chip, advancing cancer categorization. The most challenging aspect of categorization is working out many information points from many sources. The proposed approach uses microarray data to train deep learning algorithms on extracted features and then uses the Latent Feature Selection Technique to reduce classification time and increase accuracy. The feature-selection-based techniques will pick the important genes before classifying microarray data for cancer prediction and diagnosis. These methods improve classification accuracy by removing duplicate and superfluous information. The Artificial Bee Colony (ABC) technique of feature selection was proposed in this research using bone marrow PC gene expression data. The ABC algorithm, based on swarm intelligence, has been proposed for gene identification. The ABC has been used here for feature selection that generates a subset of features and every feature produced by the spectators, making this a wrapper-based feature selection system. This method's main goal is to choose the fewest genes that are critical to PC performance while also increasing prediction accuracy. Convolutional Neural Networks were used to classify tumors without labelling them. Lung, kidney, and brain cancer datasets were used in the procedure's training and testing stages. Using the cross-validation technique of k-fold methodology, the Convolutional Neural Network has an accuracy rate of 96.43%. The suggested research includes techniques for preprocessing and modifying gene expression data to enhance future cancer detection accuracy.
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Wang Z, Zhang Z, Zhang K, Zhou Q, Chen S, Zheng H, Wang G, Cai S, Wang F, Li S. Multi-Omics Characterization of a Glycerolipid Metabolism-Related Gene Enrichment Score in Colon Cancer. Front Oncol 2022; 12:881953. [PMID: 35600382 PMCID: PMC9117699 DOI: 10.3389/fonc.2022.881953] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
Background Glycerolipid metabolism is involved in the genesis and progression of colon cancer. The current study aims at exploring the prognostic value and potential molecular mechanism of glycerolipid metabolism-related genes in colon cancer from the perspective of multi-omics. Methods Clinical information and mRNA expression data of patients with colon cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Single-sample gene set enrichment analysis (ssGSEA) was applied to calculate the glycerolipid metabolism-related gene enrichment score (GLMS). Univariable and multivariable Cox regression analyses were used to study the prognostic value of GLMS in TCGA-COAD and GSE39582 cohorts. The molecular mechanism of the prognostic factor was investigated via immune cell infiltration estimation and correlation analysis of cancer hallmark pathways. Single-cell transcriptomic dataset GSE146771 was used to identify the cell populations which glycerolipid metabolism targeted on. Results The GLMS was found to be associated with tumor location and consensus molecular types (CMSs) of colon cancer in TCGA-COAD cohort (P < 0.05). Patients in the low-GLMS group exhibited poorer overall survival (OS) in TCGA cohort (P = 0.03; HR, 0.63; 95% CI, 0.42-0.94), which was further validated in the GSE39582 dataset (P < 0.001; HR, 0.57; 95% CI, 0.43-0.76). The association between the GLMS and OS remained significant in the multivariable analysis (TCGA cohort: P = 0.04; HR, 0.64; 95% CI, 0.42-0.98; GSE39582 cohort: P < 0.001; HR, 0.60; 95% CI, 0.45-0.80). The GLMS was positively correlated with cancer hallmark pathways including bile acid metabolism, xenobiotic metabolism, and peroxisome and negatively correlated with pathways such as interferon gamma response, allograft rejection, apoptosis, and inflammatory response (P < 0.05). Increased immune infiltration and upregulated expression of immune checkpoints were observed in patients with lower GLMS (P < 0.05). Single-cell datasets verified the different distribution of GLMS in cell subsets, with significant enrichment of GLMS in malignant cells and Tprolif cells. Conclusion We demonstrated that GLMS was a potential independent prognostic factor for colon cancer. The GLMS was also correlated with several cancer hallmark pathways, as well as immune microenvironment.
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Affiliation(s)
- Zhiyu Wang
- Department of Medical Oncology, Hebei Key Laboratory of Cancer Radiotherapy and Chemotherapy, Affiliated Hospital of Hebei University, Baoding, China
| | - Zhuoqi Zhang
- Department of Gastrointestinal Surgery, Affiliated Hospital of Hebei University, Baoding, China
| | - Ke Zhang
- General Surgery Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qiaoxia Zhou
- Medical Department, Burning Rock Biotech, Guangzhou, China
| | - Sidong Chen
- Medical Department, Burning Rock Biotech, Guangzhou, China
| | - Hao Zheng
- General Surgery Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Guoqiang Wang
- Medical Department, Burning Rock Biotech, Guangzhou, China
| | - Shangli Cai
- Medical Department, Burning Rock Biotech, Guangzhou, China
| | - Fujing Wang
- General Surgery Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Shenglong Li
- General Surgery Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
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Bironzo P, Primo L, Novello S, Righi L, Candeloro S, Manganaro L, Bussolino F, Pirri F, Scagliotti GV. Clinical-molecular prospective cohort study in Non-Small Cell Lung Cancer (PROMOLE study): a comprehensive approach to identify new predictive markers of pharmacological response. Clin Lung Cancer 2022; 23:e347-e352. [DOI: 10.1016/j.cllc.2022.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/11/2022] [Accepted: 05/08/2022] [Indexed: 11/03/2022]
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Identification of molecular subtypes and a novel prognostic model of diffuse large B-cell lymphoma based on a metabolism-associated gene signature. J Transl Med 2022; 20:186. [PMID: 35468826 PMCID: PMC9036805 DOI: 10.1186/s12967-022-03393-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 04/11/2022] [Indexed: 12/13/2022] Open
Abstract
Background Diffuse large B cell lymphoma (DLBCL) is the most common lymphoma in adults. Metabolic reprogramming in tumors is closely related to the immune microenvironment. This study aimed to explore the interactions between metabolism-associated genes (MAGs) and DLBCL prognosis and their potential associations with the immune microenvironment. Methods Gene expression and clinical data on DLBCL patients were obtained from the GEO database. Metabolism-associated molecular subtypes were identified by consensus clustering. A prognostic risk model containing 14 MAGs was established using Lasso-Cox regression in the GEO training cohort. It was then validated in the GEO internal testing cohort and TCGA external validation cohort. GO, KEGG and GSVA were used to explore the differences in enriched pathways between high- and low-risk groups. ESTIMATE, CIBERSORT, and ssGSEA analyses were used to assess the immune microenvironment. Finally, WGCNA analysis was used to identify two hub genes among the 14 model MAGs, and they were preliminarily verified in our tissue microarray (TMA) using multiple fluorescence immunohistochemistry (mIHC). Results Consensus clustering divided DLBCL patients into two metabolic subtypes with significant differences in prognosis and the immune microenvironment. Poor prognosis was associated with an immunosuppressive microenvironment. A prognostic risk model was constructed based on 14 MAGs and it was used to classify the patients into two risk groups; the high-risk group had poorer prognosis and an immunosuppressive microenvironment characterized by low immune score, low immune status, high abundance of immunosuppressive cells, and high expression of immune checkpoints. Cox regression, ROC curve analysis, and a nomogram indicated that the risk model was an independent prognostic factor and had a better prognostic value than the International Prognostic Index (IPI) score. The risk model underwent multiple validations and the verification of the two hub genes in TMA indicated consistent results with the bioinformatics analyses. Conclusions The molecular subtypes and a risk model based on MAGs proposed in our study are both promising prognostic classifications in DLBCL, which may provide novel insights for developing accurate targeted cancer therapies. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03393-9.
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Identification of a 5-Nutrient Stress-Sensitive Gene Signature to Predict Survival for Colorectal Cancer. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2587120. [PMID: 35496037 PMCID: PMC9039781 DOI: 10.1155/2022/2587120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/14/2022] [Accepted: 03/26/2022] [Indexed: 12/01/2022]
Abstract
Background The high heterogeneity and the complexity of the tumor microenvironment of colorectal cancer (CRC) have enhanced the difficulty of prognosis prediction based on conventional clinical indicators. Recent studies revealed that tumor cells could overcome various nutritional deficiencies by gene regulation and metabolic remodeling. However, whether differentially expressed genes (DEGs) in CRC cells under kinds of nutrient deficiency could be used to predict prognosis remained unveiled. Methods Three datasets (GSE70976, GSE13548, and GSE116087), in which colon cancer cells were, respectively, cultured in serum-free, glucose-free, or glutamine-free medium, were included to delineate the profiles of gene expression by nutrient stress. DEGs were figured out in three datasets, and gene functional analysis was performed. Survival analyses and Cox proportional hazards model were then used to identify nutrient stress sensitive genes in CRC datasets (GSE39582 and TCGA COAD). Then, a 5-gene signature was constructed and the risk scores were also calculated. Survival analyses, cox analyses, and nomogram were applied to predict the prognosis of patients with colorectal cancer. The effectiveness of the risk model was also tested. Results A total of 48 genes were found to be dysregulated in serum, glucose, or glutamine-deprived CRC cells, which were mainly enriched in cell cycle and endoplasmic reticulum stress pathways. After further analyses, 5 genes, MCM5, MCM6, CDCA2, GINS2, and SPC25, were identified to be differentially expressed in CRC and be related to prognosis of in CRC datasets. We used the above nutrient stress-sensitive genes to construct a risk scoring model. CRC samples in the datasets were divided into low-risk and high-risk groups. Data showed that higher risk scores were associated with better outcomes and risk scores decreased significantly with tumor procession. Moreover, the risk score could be used to predict the probability of survival based on nomogram. Conclusions The 5-nutrient stress-sensitive gene signature could act as an independent biomarker for survival prediction of CRC patients.
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Li D, Wang R, Wu N, Yu Y. LncRNA HULC as a potential predictor of prognosis and clinicopathological features in patients with digestive system tumors: a meta-analysis. Aging (Albany NY) 2022; 14:1797-1811. [PMID: 35183058 PMCID: PMC8908940 DOI: 10.18632/aging.203903] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 02/02/2022] [Indexed: 11/25/2022]
Abstract
Objective: This meta-analysis aimed to evaluate the correlation between lncRNA HULC, prognosis and clinicopathological characteristics in patients with digestive system tumors. Methods: The relevant literatures were collected through PubMed, Web of Science and Embase up to February 2021. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to assess the prognostic value of HULC in patients with digestive system tumors. The clinicopathological characteristics of HULC in patients were estimated by odds ratios (ORs). Results: A total of 14 studies involving 1312 patients were included. The up-regulated expression level of HULC was associated with poorer overall survival (OS) in patients with digestive system tumors (HR = 1.83, 95% CI: 1.05-3.19, P = 0.033). Subgroup analysis showed that cancer type (pancreatic cancer or gastric cancer), residence region (China, Japan or Korea), and specimen (serum) significantly associated between HULC and OS. In addition, high HULC expression significantly increased the risk of high TNM stage (OR = 2.51, 95%CI: 1.36-4.62, P < 0.05), poor differentiation (OR = 1.38, 95%CI: 1.02-1.87, P < 0.05) and lymphatic node metastasis (LNM, OR = 4.93, 95% CI: 3.47-6.99, P < 0.05). Conclusions: High expression level of HULC is related to OS, TNM stage, differentiation and LNM. Therefore, HULC can be used as a new potential predictor for prognosis and clinicopathological features of patients with digestive system tumors.
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Affiliation(s)
- Duo Li
- Department of Gastroenterology, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei, China
| | - Rui Wang
- Department of Gastroenterology, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei, China
| | - Na Wu
- Department of Gastroenterology, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei, China
| | - Yongqiang Yu
- Department of Gastroenterology, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei, China
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Liu Z, Liu L, Weng S, Guo C, Dang Q, Xu H, Wang L, Lu T, Zhang Y, Sun Z, Han X. Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer. Nat Commun 2022; 13:816. [PMID: 35145098 PMCID: PMC8831564 DOI: 10.1038/s41467-022-28421-6] [Citation(s) in RCA: 193] [Impact Index Per Article: 96.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 01/21/2022] [Indexed: 12/22/2022] Open
Abstract
Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Nevertheless, the clinical significance of immune-related lncRNAs remains largely unexplored. In this study, we develope a machine learning-based integrative procedure for constructing a consensus immune-related lncRNA signature (IRLS). IRLS is an independent risk factor for overall survival and displays stable and powerful performance, but only demonstrates limited predictive value for relapse-free survival. Additionally, IRLS possesses distinctly superior accuracy than traditional clinical variables, molecular features, and 109 published signatures. Besides, the high-risk group is sensitive to fluorouracil-based adjuvant chemotherapy, while the low-risk group benefits more from bevacizumab. Notably, the low-risk group displays abundant lymphocyte infiltration, high expression of CD8A and PD-L1, and a response to pembrolizumab. Taken together, IRLS could serve as a robust and promising tool to improve clinical outcomes for individual CRC patients. Identification of long non-coding RNA (lncRNA) signatures could be used to improve cancer clinical outcome. Here the authors developed a machine learning-based integrative procedure to construct a consensus immune-related lncRNA signature to predict prognosis, recurrence and treatment benefits in colorectal cancer.
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Affiliation(s)
- Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.,Interventional Institute of Zhengzhou University, Zhengzhou, Henan, China.,Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan, China
| | - Long Liu
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Siyuan Weng
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Chunguang Guo
- Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Qin Dang
- Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Hui Xu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Libo Wang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Taoyuan Lu
- Department of Cerebrovascular Disease, Zhengzhou University People's Hospital, Zhengzhou, Henan, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zhenqiang Sun
- Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
| | - Xinwei Han
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China. .,Interventional Institute of Zhengzhou University, Zhengzhou, Henan, China. .,Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan, China.
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Ahluwalia P, Mondal AK, Ahluwalia M, Sahajpal NS, Jones K, Jilani Y, Gahlay GK, Barrett A, Kota V, Rojiani AM, Kolhe R. Clinical and molecular assessment of an onco-immune signature with prognostic significance in patients with colorectal cancer. Cancer Med 2022; 11:1573-1586. [PMID: 35137551 PMCID: PMC8921909 DOI: 10.1002/cam4.4568] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 11/24/2021] [Accepted: 12/28/2021] [Indexed: 12/22/2022] Open
Abstract
Understanding the complex tumor microenvironment is key to the development of personalized therapies for the treatment of cancer including colorectal cancer (CRC). In the past decade, significant advances in the field of immunotherapy have changed the paradigm of cancer treatment. Despite significant improvements, tumor heterogeneity and lack of appropriate classification tools for CRC have prevented accurate risk stratification and identification of a wider patient population that may potentially benefit from targeted therapies. To identify novel signatures for accurate prognostication of CRC, we quantified gene expression of 12 immune‐related genes using a medium‐throughput NanoString quantification platform in 93 CRC patients. Multivariate prognostic analysis identified a combined four‐gene prognostic signature (TGFB1, PTK2, RORC, and SOCS1) (HR: 1.76, 95% CI: 1.05–2.95, *p < 0.02). The survival trend was captured in an independent gene expression data set: GSE17536 (177 patients; HR: 3.31, 95% CI: 1.99–5.55, *p < 0.01) and GSE14333 (226 patients; HR: 2.47, 95% CI: 1.35–4.53, *p < 0.01). Further, gene set enrichment analysis of the TCGA data set associated higher prognostic scores with epithelial–mesenchymal transition (EMT) and inflammatory pathways. Comparatively, a lower prognostic score was correlated with oxidative phosphorylation and MYC and E2F targets. Analysis of immune parameters identified infiltration of T‐reg cells, CD8+ T cells, M2 macrophages, and B cells in high‐risk patient groups along with upregulation of immune exhaustion genes. This molecular study has identified a novel prognostic gene signature with clinical utility in CRC. Therefore, along with prognostic features, characterization of immune cell infiltrates and immunosuppression provides actionable information that should be considered while employing personalized medicine.
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Affiliation(s)
- Pankaj Ahluwalia
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, Georgia, USA
| | - Ashis K Mondal
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, Georgia, USA
| | | | - Nikhil S Sahajpal
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, Georgia, USA
| | - Kimya Jones
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, Georgia, USA
| | - Yasmeen Jilani
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, Georgia, USA
| | - Gagandeep K Gahlay
- Department of Molecular Biology and Biochemistry, Guru Nanak Dev University, Amritsar, India
| | - Amanda Barrett
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, Georgia, USA
| | - Vamsi Kota
- Department of Medicine, Medical College of Georgia at Augusta University, Augusta, Georgia, USA
| | - Amyn M Rojiani
- Department of Pathology, Penn State College of Medicine, Hershey, USA
| | - Ravindra Kolhe
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, Georgia, USA
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Alorda-Clara M, Torrens-Mas M, Morla-Barcelo PM, Martinez-Bernabe T, Sastre-Serra J, Roca P, Pons DG, Oliver J, Reyes J. Use of Omics Technologies for the Detection of Colorectal Cancer Biomarkers. Cancers (Basel) 2022; 14:817. [PMID: 35159084 PMCID: PMC8834235 DOI: 10.3390/cancers14030817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/31/2022] [Accepted: 02/04/2022] [Indexed: 12/14/2022] Open
Abstract
Colorectal cancer (CRC) is one of the most frequently diagnosed cancers with high mortality rates, especially when detected at later stages. Early detection of CRC can substantially raise the 5-year survival rate of patients, and different efforts are being put into developing enhanced CRC screening programs. Currently, the faecal immunochemical test with a follow-up colonoscopy is being implemented for CRC screening. However, there is still a medical need to describe biomarkers that help with CRC detection and monitor CRC patients. The use of omics techniques holds promise to detect new biomarkers for CRC. In this review, we discuss the use of omics in different types of samples, including breath, urine, stool, blood, bowel lavage fluid, or tumour tissue, and highlight some of the biomarkers that have been recently described with omics data. Finally, we also review the use of extracellular vesicles as an improved and promising instrument for biomarker detection.
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Affiliation(s)
- Marina Alorda-Clara
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
- Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, Edificio S, E-07120 Palma de Mallorca, Illes Balears, Spain
| | - Margalida Torrens-Mas
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
- Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, Edificio S, E-07120 Palma de Mallorca, Illes Balears, Spain
- Translational Research in Aging and Longevity (TRIAL) Group, Instituto de Investigación Sanitaria Illes Balears (IdISBa), E-07120 Palma de Mallorca, Illes Balears, Spain
| | - Pere Miquel Morla-Barcelo
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
| | - Toni Martinez-Bernabe
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
- Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, Edificio S, E-07120 Palma de Mallorca, Illes Balears, Spain
| | - Jorge Sastre-Serra
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
- Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, Edificio S, E-07120 Palma de Mallorca, Illes Balears, Spain
- Ciber Fisiopatología Obesidad y Nutrición (CB06/03) Instituto Salud Carlos III, E-28029 Madrid, Madrid, Spain
| | - Pilar Roca
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
- Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, Edificio S, E-07120 Palma de Mallorca, Illes Balears, Spain
- Ciber Fisiopatología Obesidad y Nutrición (CB06/03) Instituto Salud Carlos III, E-28029 Madrid, Madrid, Spain
| | - Daniel Gabriel Pons
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
- Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, Edificio S, E-07120 Palma de Mallorca, Illes Balears, Spain
| | - Jordi Oliver
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
- Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, Edificio S, E-07120 Palma de Mallorca, Illes Balears, Spain
- Ciber Fisiopatología Obesidad y Nutrición (CB06/03) Instituto Salud Carlos III, E-28029 Madrid, Madrid, Spain
| | - Jose Reyes
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
- Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, Edificio S, E-07120 Palma de Mallorca, Illes Balears, Spain
- Servicio Aparato Digestivo, Hospital Comarcal de Inca, E-07300 Inca, Illes Balears, Spain
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Meng T, Lan Z, Zhao X, Niu L, Chen C, Zhang W. Comprehensive bioinformatics analysis of functional molecules in colorectal cancer. J Gastrointest Oncol 2022; 13:231-245. [PMID: 35284121 PMCID: PMC8899732 DOI: 10.21037/jgo-21-921] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 01/30/2022] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is the 3rd most common cancer and the 2nd leading cause of cancer-related death. Numerous studies have found that aberrations in cellular molecules play an important role in the development of tumors. Studying and determining the interactions between these molecules can contribute to the diagnosis, treatment, and prognosis of tumors. METHODS The GSE151021, GSE156720, and GSE156719 data sets were analyzed to screen the differentially expressed messenger RNAs (DEmRNAs), long non-coding RNAs (DElncRNAs), and microRNAs (DEmiRNAs) in CRC. Database for Annotation, Visualization and Integrated Discovery (DAVID) and the Search Tool for the Retrieval of Interacting Genes/Proteins software were used to examine gene enrichment and the hub genes. Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and UALCAN was used to verify the expression of the hub genes. To analyze the overall survival (OS) of the hub genes, Kaplan-Meier plotter (KM plotter) was performed. Finally, the miRCancer database, TargetScan, and GSE156719 were used to identify the targets of the identified miRNAs. To predict the lncRNA-miRNA interactions, we used DIANA-LncBase v2 and GSE156720. Finally, the visualization protein‑protein interaction (PPI), competitive endogenous RNA (ceRNA) network was constructed using Cytoscape v3.1. RESULTS By analyzing GSE151021 and GSE156720, 23 upregulated mRNAs and 10 downregulated mRNAs were identified as sharing the differentially expressed genes (DEGs) between CRC and adjacent tissues. Furthermore, nucleolar protein 14 (NOP14), the sonic hedgehog (SHH) signaling molecule, phorbol-12-myristate-13-acetate-induced protein 1 (PMAIP1), the BCL2 apoptosis regulator (BCL2), and zinc finger E-box binding homeobox 2 (ZEB2) were considered hub genes. The constructed lncRNA-miRNA-mRNA network revealed 7 intersecting miRNAs (4 upregulated and 3 downregulated), 79 lncRNAs (40 upregulated and 39 downregulated), and 5 mRNAs (3 upregulated and 2 downregulated). Finally, we determined that the dysregulation of lncRNAs, such as HCG16, CASC9, SNHG16, HAND2-AS1, and NR2F1-AS1, secluded altered the expression of several miRNAs, such as hsa-miR-193a-5p, hsa-miR-485-5p, hsa-miR-17-5p, and hsa-miR-92a-3p, and affected the occurrence and development of CRC. CONCLUSIONS We identified a series of DElncRNAs, DEmRNAs, and DEmiRNAs in CRC that might be considered potential biomarkers in understanding the complex molecular pathways leading to CRC development.
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Affiliation(s)
- Tao Meng
- Department of Gastrointestinal Surgery, Xinjiang Medical University Tumor Hospital, Urumqi, China
| | - Zhangzhang Lan
- School of Medicine, Southern University of Science and Technology, Shenzhen, China
| | - Xiaoling Zhao
- CheerLand Clinical Laboratory Co., Ltd., Peking University Medical Industrial Park, Zhongguancun Life Science Park, Beijing, China
| | - Li Niu
- CheerLand Clinical Laboratory Co., Ltd., Peking University Medical Industrial Park, Zhongguancun Life Science Park, Beijing, China
- Shenzhen Cheerland Biotechnology Co., Ltd., Cheerland-Watson Center for Life Sciences and Technology, Shenzhen, China
| | - Chuan Chen
- Shenzhen Cheerland Biotechnology Co., Ltd., Cheerland-Watson Center for Life Sciences and Technology, Shenzhen, China
| | - Wenyong Zhang
- School of Medicine, Southern University of Science and Technology, Shenzhen, China
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Zhou S, Cai Y, Xu Z, Peng B, Liang Q, Peng J, Yan Y. Identification of a pyroptosis-related lncRNA signature in the regulation of prognosis, metabolism signals and immune infiltration in lung adenocarcinoma. Front Endocrinol (Lausanne) 2022; 13:964362. [PMID: 36034461 PMCID: PMC9401518 DOI: 10.3389/fendo.2022.964362] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 07/25/2022] [Indexed: 12/14/2022] Open
Abstract
Pyroptosis is a cell death pathway that plays a significant role in lung adenocarcinoma (LUAD). Also, studies regarding the correlation between the expression of long non-coding RNAs (lncRNAs) and the mechanism of LUAD has aroused concern around the world. The purpose of this paper is to explore the underlying relationship of differentially expressed lncRNAs and pyroptosis-related genes. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression were applied to construct a prognostic risk score model from the TCGA database. A pyroptosis-related five-lncRNA signature (CRNDE, HHLA3, MIR193BHG, LINC00941, LINC01843) was considered to be correlated to the prognosis and immune response of LUAD patients. In addition, the cytological experiments revealed that aberrantly expressed HHLA3 displayed a proliferation promotion role in LUAD cells A549 and H460. Next, the forest and nomogram plots have shown this lncRNA signature could be served as an independent prognostic factor for LUAD. The ROC curves further identified the prognostic value of the five-lncRNA signature. The infiltration of immune cells, such as T cells CD8, T cells CD4 memory resting, T cells CD4 memory activated and M0 macrophages were greatly different between the high-risk group and the low-risk group. It implicated that the signature is significantly effective in immunotherapy of LUAD patients. This study has supplied a novel pyroptosis-related lncRNA signature and provided a predictive model for prognosis and immune response of LUAD patients.
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Affiliation(s)
- Shuyi Zhou
- Department of Hepatobiliary Surgery, Hunan Provincial People’s Hospital Xingsha Branch (People’s Hospital of Changsha County), Hunan Normal University, Changsha, China
| | - Yuan Cai
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Zhijie Xu
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
- Department of Pathology, Xiangya Changde Hospital, Changde, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Bi Peng
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Qiuju Liang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China
| | - Jinwu Peng
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
- Department of Pathology, Xiangya Changde Hospital, Changde, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Yuanliang Yan, ; Jinwu Peng,
| | - Yuanliang Yan
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Yuanliang Yan, ; Jinwu Peng,
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Integrative analysis from multi-center studies identities a consensus machine learning-derived lncRNA signature for stage II/III colorectal cancer. EBioMedicine 2021; 75:103750. [PMID: 34922323 PMCID: PMC8686027 DOI: 10.1016/j.ebiom.2021.103750] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 11/30/2021] [Accepted: 11/30/2021] [Indexed: 02/07/2023] Open
Abstract
Background Long non-coding RNAs (lncRNAs) have recently emerged as essential biomarkers of cancer progression. However, studies are limited regarding lncRNAs correlated with recurrence and fluorouracil-based adjuvant chemotherapy (ACT) in stage II/III colorectal cancer (CRC). Methods 1640 stage II/III CRC patients were enrolled from 15 independent datasets and a clinical in-house cohort. 10 prevalent machine learning algorithms were collected and then combined into 76 combinations. 109 published transcriptome signatures were also retrieved. qRT-PCR assay was performed to verify our model. Findings We comprehensively identified 27 stably recurrence-related lncRNAs from multi-center cohorts. According to these lncRNAs, a consensus machine learning-derived lncRNA signature (CMDLncS) that exhibited best power for predicting recurrence risk was determined from 76 kinds of algorithm combinations. A high CMDLncS indicated unfavorable recurrence and mortality rates. CMDLncS not only could work independently of common clinical traits (e.g., AJCC stage) and molecular features (e.g., microsatellite state, KRAS mutation), but also presented dramatically better performance than these variables. qRT-PCR results from 173 patients further verified our in-silico findings and assessed its feasible in different centers. Comparisons of CMDLncS with 109 published transcriptome signatures further demonstrated its predictive superiority. Additionally, patients with high CMDLncS benefited more from fluorouracil-based ACT and were characterized by activation of stromal and epithelial-mesenchymal transition, while patients with low CMDLncS suggested the sensitivity to bevacizumab and displayed enhanced immune activation. Interpretation CMDLncS provides an attractive platform for identifying patient at high risk of recurrence and could optimize precision treatment to improve the clinical outcomes in stage II/III CRC. Funding This study was supported by the National Natural Science Foundation of China (81,972,663); Henan Province Young and Middle‐Aged Health Science and Technology Innovation Talent Project (YXKC2020037); and Henan Provincial Health Commission Joint Youth Project (SB201902014).
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Ma C, Zhang X, Zhao X, Zhang N, Zhou S, Zhang Y, Li P. Predicting the Survival and Immune Landscape of Colorectal Cancer Patients Using an Immune-Related lncRNA Pair Model. Front Genet 2021; 12:690530. [PMID: 34552614 PMCID: PMC8451271 DOI: 10.3389/fgene.2021.690530] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/29/2021] [Indexed: 12/12/2022] Open
Abstract
Background Accumulating evidence has demonstrated that immune-related long non-coding ribonucleic acids (irlncRNAs) can be used as prognostic indicators of overall survival (OS) in patients with colorectal cancer (CRC). Our aim in this research, therefore, was to construct a risk model using irlncRNA pairs with no requirement for a specific expression level, in hope of reliably predicting the prognosis and immune landscape of CRC patients. Methods Clinical and transcriptome profiling data of CRC patients downloaded from the Cancer Genome Atlas (TCGA) database were analyzed to identify differentially expressed (DE) irlncRNAs. The irlncRNA pairs significantly correlated with the prognosis of patients were screened out by univariable Cox regression analysis and a prognostic model was constructed by Lasso and multivariate Cox regression analyses. A receiver operating characteristic (ROC) curve was then plotted, with the area under the curve calculated to confirm the reliability of the model. Based on the optimal cutoff value, CRC patients in the high- or low-risk groups were distinguished, laying the ground for evaluating the risk model from the following perspectives: survival, clinicopathological traits, tumor-infiltrating immune cells (TIICs), antitumor drug efficacy, kinase inhibitor efficacy, and molecules related to immune checkpoints. Results A prognostic model consisting of 15 irlncRNA pairs was constructed, which was found to have a high correlation with patient prognosis in a cohort from the TCGA (p < 0.001, HR = 1.089, 95% CI [1.067-1.112]). According to both univariate and multivariate Cox analyses, this model could be used as an independent prognostic indicator in the TCGA cohort (p < 0.001). Effective differentiation between high- and low-risk patients was also accomplished, on the basis of aggressive clinicopathological characteristics, sensitivity to antitumor drugs, and kinase inhibitors, the tumor immune infiltration status, and the expression levels of specific molecules related to immune checkpoints. Conclusion The prognostic model established with irlncRNA pairs is a promising indicator for prognosis prediction in CRC patients.
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Affiliation(s)
- Chao Ma
- Medical School of Chinese PLA, Beijing, China.,Department of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xin Zhang
- State Key Laboratory of Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Xudong Zhao
- Department of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Nan Zhang
- Department of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Sixin Zhou
- Department of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yonghui Zhang
- Medical School of Chinese PLA, Beijing, China.,Department of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Peiyu Li
- Department of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
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Luo M, Yang X, Chen HN, Nice EC, Huang C. Drug resistance in colorectal cancer: An epigenetic overview. Biochim Biophys Acta Rev Cancer 2021; 1876:188623. [PMID: 34481016 DOI: 10.1016/j.bbcan.2021.188623] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 08/29/2021] [Accepted: 08/30/2021] [Indexed: 02/08/2023]
Abstract
Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Despite significant progress that has been made in therapies against CRC over the past decades, drug resistance is still a major limitation in CRC treatment. Numerous investigations have unequivocally shown that epigenetic regulation plays an important role in CRC drug resistance because of the high rate of epigenetic alterations in multiple genes during cancer development or drug treatment. Furthermore, the reversibility of epigenetic alterations provides novel therapeutic strategies to overcome drug resistance using small molecules, which can target non-coding RNAs or reverse histone modification and DNA methylation. In this review, we discuss epigenetic regulation in CRC drug resistance and the possible role of preventing or reversing CRC drug resistance using epigenetic therapy in CRC treatment.
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Affiliation(s)
- Maochao Luo
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang 315020, China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Collaborative Innovation Center for Biotherapy, Chengdu 610041, China
| | - Xingyue Yang
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang 315020, China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Collaborative Innovation Center for Biotherapy, Chengdu 610041, China
| | - Hai-Ning Chen
- Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Edouard C Nice
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia.
| | - Canhua Huang
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang 315020, China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Collaborative Innovation Center for Biotherapy, Chengdu 610041, China.
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Zhu J, Ao H, Liu M, Cao K, Ma J. UBE2T promotes autophagy via the p53/AMPK/mTOR signaling pathway in lung adenocarcinoma. J Transl Med 2021; 19:374. [PMID: 34461934 PMCID: PMC8407090 DOI: 10.1186/s12967-021-03056-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 08/24/2021] [Indexed: 12/25/2022] Open
Abstract
Background Ubiquitin-conjugating enzyme E2T (UBE2T) acts as an oncogene in various types of cancer. However, the mechanisms behind its oncogenic role remain unclear in lung cancer. This study aims to explore the function and clinical relevance of UBE2T in lung cancer. Methods Lentiviral vectors were used to mediate UBE2T depletion or overexpress UBE2T in lung cancer cells. CCK8 analysis and western blotting were performed to investigate the effects of UBE2T on proliferation, autophagy, and relevant signaling pathways. To exploit the clinical significance of UBE2T, we performed immunohistochemistry staining with an anti-UBE2T antibody on 131 NSCLC samples. Moreover, we downloaded the human lung adenocarcinoma (LUAD) dataset from The Cancer Atlas Project (TCGA). Lasso Cox regression model was adopted to establish a prognostic model with UBE2T-correlated autophagy genes. Results We found that UBE2T stimulated proliferation and autophagy, and silencing this gene abolished autophagy in lung cancer cells. As suggested by Gene set enrichment analysis, we observed that UBE2T downregulated p53 levels in A549 cells and vice versa. Blockade of p53 counteracted the inhibitory effects of UBE2T depletion on autophagy. Meanwhile, the AMPK/mTOR signaling pathway was activated during UBE2T-mediated autophagy, suggesting that UBE2T promotes autophagy via the p53/AMPK/mTOR pathway. Interestingly, UBE2T overexpression increased cisplatin-trigged autophagy and led to cisplatin resistance of A549 cells, whereas inhibiting autophagy reversed drug resistance. However, no association was observed between UEB2T and overall survival in a population of 131 resectable NSCLC patients. Therefore, we developed and validated a multiple gene signature by considering UBE2T and its relevance in autophagy in lung cancer. The risk score derived from the prognostic signature significantly stratified LUAD patients into low- and high-risk groups with different overall survival. The risk score might independently predict prognosis. Interestingly, nomogram and decision curve analysis demonstrated that the signature’s prognostic accuracy culminated while combined with clinical features. Finally, the risk score showed great potential in predicting clinical chemosensitivity. Conclusions We found that UBE2T upregulates autophagy in NSCLC cells by activating the p53/AMPK/mTOR signaling pathway. The clinical predicting ability of UBE2T in LUAD can be improved by considering the autophagy-regulatory role of UBE2T. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-03056-1.
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Affiliation(s)
- Jinhong Zhu
- Department of Clinical Laboratory, Biobank, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China
| | - Haijiao Ao
- Department of Clinical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China
| | - Mingdong Liu
- Department of Clinical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China
| | - Kui Cao
- Department of Clinical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China
| | - Jianqun Ma
- Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China.
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Core Circadian Clock Proteins as Biomarkers of Progression in Colorectal Cancer. Biomedicines 2021; 9:biomedicines9080967. [PMID: 34440171 PMCID: PMC8391187 DOI: 10.3390/biomedicines9080967] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/23/2021] [Accepted: 07/31/2021] [Indexed: 12/13/2022] Open
Abstract
Colorectal cancer (CRC) is one of the most common tumours in developed countries. Although its incidence and mortality rates have decreased, its prognosis has not changed, and a high percentage of patients with CRC develop relapse (metachronous metastasis, MM, or local recurrence, LR) during their disease. The identification of these patients is very important for their correct management, but the lack of prognostic markers makes it difficult. Given the connection between circadian disruption and cancer development and progression, we aimed to analyse the prognostic significance of core circadian proteins in CRC. We measured the expression of PER1-3, CRY1-2, BMAL1 and NR1D2 in a cohort of CRC patients by immunohistochemistry (IHC) and analysed their prognostic potential in this disease. A low expression of PER2 and BMAL1 was significantly associated with metastasis at the moment of disease diagnosis, whereas a high expression of CRY1 appeared as an independent prognostic factor of MM development. A high expression of NR1D2 appeared as an independent prognostic factor of LR development after disease diagnosis. Moreover, patients with a low expression of BMAL1 and a high expression of CRY1 showed lower OS and DFS at five years. Although these markers need to be validated in larger and different ethnic cohorts, the simplicity of IHC makes these proteins candidates for personalizing CRC treatment.
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Feng Z, Qian H, Li K, Lou J, Wu Y, Peng C. Development and Validation of a 7-Gene Prognostic Signature to Improve Survival Prediction in Pancreatic Ductal Adenocarcinoma. Front Mol Biosci 2021; 8:676291. [PMID: 34095229 PMCID: PMC8176016 DOI: 10.3389/fmolb.2021.676291] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 05/06/2021] [Indexed: 12/14/2022] Open
Abstract
Background: Previous prognostic signatures of pancreatic ductal adenocarcinoma (PDAC) are mainly constructed to predict the overall survival (OS), and their predictive accuracy needs to be improved. Gene signatures that efficaciously predict both OS and disease-free survival (DFS) are of great clinical significance but are rarely reported. Methods: Univariate Cox regression analysis was adopted to screen common genes that were significantly associated with both OS and DFS in three independent cohorts. Multivariate Cox regression analysis was subsequently performed on the identified genes to determine an optimal gene signature in the MTAB-6134 training cohort. The Kaplan-Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were employed to assess the predictive accuracy. Biological process and pathway enrichment analyses were conducted to elucidate the biological role of this signature. Results: Multivariate Cox regression analysis determined a 7-gene signature that contained ASPH, DDX10, NR0B2, BLOC1S3, FAM83A, SLAMF6, and PPM1H. The signature had the ability to stratify PDAC patients with different OS and DFS, both in the training and validation cohorts. ROC curves confirmed the moderate predictive accuracy of this signature. Mechanically, the signature was related to multiple cancer-related pathways. Conclusion: A novel OS and DFS prediction model was constructed in PDAC with multi-cohort and cross-platform compatibility. This signature might foster individualized therapy and appropriate management of PDAC patients.
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Affiliation(s)
- Zengyu Feng
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Department of General Surgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Hao Qian
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kexian Li
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jianyao Lou
- Department of General Surgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yulian Wu
- Department of General Surgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chenghong Peng
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Ahluwalia P, Ahluwalia M, Mondal AK, Sahajpal N, Kota V, Rojiani MV, Rojiani AM, Kolhe R. Prognostic and therapeutic implications of extracellular matrix associated gene signature in renal clear cell carcinoma. Sci Rep 2021; 11:7561. [PMID: 33828127 PMCID: PMC8026590 DOI: 10.1038/s41598-021-86888-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 03/22/2021] [Indexed: 12/14/2022] Open
Abstract
Complex interactions in tumor microenvironment between ECM (extra-cellular matrix) and cancer cell plays a central role in the generation of tumor supportive microenvironment. In this study, the expression of ECM-related genes was explored for prognostic and immunological implication in clear cell renal clear cell carcinoma (ccRCC). Out of 964 ECM genes, higher expression (z-score > 2) of 35 genes showed significant association with overall survival (OS), progression-free survival (PFS) and disease-specific survival (DSS). On comparison to normal tissue, 12 genes (NUDT1, SIGLEC1, LRP1, LOXL2, SERPINE1, PLOD3, ZP3, RARRES2, TGM2, COL3A1, ANXA4, and POSTN) showed elevated expression in kidney tumor (n = 523) compared to normal (n = 100). Further, Cox proportional hazard model was utilized to develop 12 genes ECM signature that showed significant association with overall survival in TCGA dataset (HR = 2.45; 95% CI [1.78-3.38]; p < 0.01). This gene signature was further validated in 3 independent datasets from GEO database. Kaplan-Meier log-rank test significantly associated patients with elevated expression of this gene signature with a higher risk of mortality. Further, differential gene expression analysis using DESeq2 and principal component analysis (PCA) identified genes with the highest fold change forming distinct clusters between ECM-rich high-risk and ECM-poor low-risk patients. Geneset enrichment analysis (GSEA) identified significant perturbations in homeostatic kidney functions in the high-risk group. Further, higher infiltration of immunosuppressive T-reg and M2 macrophages was observed in high-risk group patients. The present study has identified a prognostic signature with associated tumor-promoting immune niche with clinical utility in ccRCC. Further exploration of ECM dynamics and validation of this gene signature can assist in design and application of novel therapeutic approaches.
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Affiliation(s)
- Pankaj Ahluwalia
- Department of Pathology, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Meenakshi Ahluwalia
- Department of Pathology, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Ashis K Mondal
- Department of Pathology, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Nikhil Sahajpal
- Department of Pathology, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Vamsi Kota
- Department of Medicine, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Mumtaz V Rojiani
- Department of Pathology, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Amyn M Rojiani
- Department of Pathology, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Ravindra Kolhe
- Department of Pathology, Medical College of Georgia, Augusta University, Augusta, GA, USA.
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Feng Z, Li K, Wu Y, Peng C. Transcriptomic Profiling Identifies DCBLD2 as a Diagnostic and Prognostic Biomarker in Pancreatic Ductal Adenocarcinoma. Front Mol Biosci 2021; 8:659168. [PMID: 33834039 PMCID: PMC8021715 DOI: 10.3389/fmolb.2021.659168] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 03/05/2021] [Indexed: 12/14/2022] Open
Abstract
Background: Accumulating evidence shows that the elevated expression of DCBLD2 (discoidin, CUB and LCCL domain-containing protein 2) is associated with unfavorable prognosis of various cancers. However, the correlation of DCBLD2 expression value with the diagnosis and prognosis of pancreatic ductal adenocarcinoma (PDAC) has not yet been elucidated. Methods: Univariate Cox regression analysis was used to screen robust survival-related genes. Expression pattern of selected genes was investigated in PDAC tissues and normal tissues from multiple cohorts. Kaplan–Meier (K–M) survival curves, ROC curves and calibration curves were employed to assess prognostic performance. The relationship between DCBLD2 expression and immune cell infiltrates was conducted by CIBERSORT software. Biological processes and KEGG pathway enrichment analyses were adopted to clarify the potential function of DCBLD2 in PDAC. Results: Univariate analysis, K–M survival curves and calibration curves indicated that DCBLD2 was a robust prognostic factor for PDAC with cross-cohort compatibility. Upregulation of DCBLD2 was observed in dissected PDAC tissues as well as extracellular vesicles from both plasma and serum samples of PDAC patients. Both DCBLD2 expression in tissue and extracellular vesicles had significant diagnostic value. Besides, DCBLD2 expression was correlated with infiltrating level of CD8+ T cells and macrophage M2 cells. Functional enrichment revealed that DCBLD2 might be involved in cell motility, angiogenesis, and cancer-associated pathways. Conclusion: Our study systematically analyzed the potential diagnostic, prognostic and therapeutic value of DCBLD2 in PDAC. All the findings indicated that DCBLD2 might play a considerably oncogenic role in PDAC with diagnostic, prognostic and therapeutic potential. These preliminary results of bioinformatics analyses need to be further validated in more prospective studies.
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Affiliation(s)
- Zengyu Feng
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Department of General Surgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Kexian Li
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yulian Wu
- Department of General Surgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chenghong Peng
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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