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Sun L, Jiao YW, Cui FQ, Liu J, Xu ZY, Sun DL. tRF-Leu reverse breast cancer cells chemoresistance by regulation of BIRC5. Discov Oncol 2024; 15:449. [PMID: 39278863 PMCID: PMC11402887 DOI: 10.1007/s12672-024-01317-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 09/05/2024] [Indexed: 09/18/2024] Open
Abstract
OBJECTIVE Accumulating studies reported the crucial roles of tRFs in tumorigenesis. However, their further mechanisms and clinical values remains unclear. This study aimed at the further investigation of tRF-Leu in breast cancer chemotherapy resistance. METHODS The high-throughput sequencing was performed and identified the downregulation of tRF-Leu in MCF7/ADR cells. The function of tRF-Leu in breast cancer cells and breast cancer chemotherapy resistance was investigated in vitro and in vivo, including colony formation assay, CCK-8 assay, transwell assay and apoptosis assay. The binding site of tRF-Leu on BIRC5 was verified by dual-luciferase assay. RESULTS tRF-Leu was downregulated in MCF7/ADR cells. Overexpression of tRF-Leu inhibited the migration of breast cancer cells. Furthermore, tRF-Leu could reverse the resistance of MCF7/ADR cells to Adriamycin both in vitro and in vivo. BIRC5 was a target of tRF-Leu, which might be involved in the chemotherapy resistance regulation. CONCLUSION We demonstrated that tRF-Leu could inhibit the chemotherapy resistance of breast cancer by targeting BIRC5. These findings might identify new biomarkers of breast cancer therapy and bring new strategies to reverse chemotherapy resistance.
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Affiliation(s)
- Li Sun
- Hepatopancreatobiliary Surgery Department, The Third Affiliated Hospital of Soochow University, Changzhou First People's Hospital, Changzhou, China
- Department of General Surgery, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Yu-Wen Jiao
- Department of General Surgery, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Fu-Qi Cui
- Department Graduate School of Dalian Medical University, Dalian Medical University, Dalian, China
| | - Jin Liu
- Department Graduate School of Dalian Medical University, Dalian Medical University, Dalian, China
| | - Zhong-Ya Xu
- Children's Hospital of Nanjing Medical University, Nanjing, China.
| | - Dong-Lin Sun
- Hepatopancreatobiliary Surgery Department, The Third Affiliated Hospital of Soochow University, Changzhou First People's Hospital, Changzhou, China.
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Ji F, Qian H, Sun Z, Yang Y, Shi M, Gu H. A novel model based on lipid metabolism-related genes associated with immune microenvironment predicts metastasis of breast cancer. Discov Oncol 2024; 15:372. [PMID: 39190262 DOI: 10.1007/s12672-024-01253-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Accepted: 08/20/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND Breast cancer (BC) is the most prevalent malignant tumor among women worldwide and a significant cause of cancer-related deaths in females. Recent studies have shown that lipid metabolism-related genes (LMRGs) exhibit prognostic potential in various types of tumors, including BC. Our study aimed to establish a novel model to predict the metastasis of BC. METHODS Clinical information and corresponding RNA data of patients with BC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Consensus clustering was performed to identify novel molecular subgroups. Estimation of Stromal and Immune Cells in Malignant Tumor Tissues using Expression, microenvironment cell populations counter, microenvironment cell populations counter, and single-sample gene set enrichment analyses were employed to determine the tumor immune microenvironment and immune status of the identified subgroups. Functional analyses, including Gene Ontology and gene set enrichment analyses, were conducted to elucidate the underlying mechanisms. A prognostic risk model was constructed using the Least Absolute Shrinkage and Selection Operator algorithm and multivariate Cox regression analysis. RESULTS This study identified differential gene expression between patients with BC exhibiting metastasis and those without metastasis using public databases. Using the obtained data, we established predictive models based on six LMRGs. Furthermore, consensus clustering and prognostic score grouping analysis revealed that differentially expressed LMRGs influence tumor prognosis by regulating tumor immunity. To facilitate clinical application, we developed a nomogram integrating the risk model and clinical characteristics to accurately predict the prognosis of patients with BC. CONCLUSION We developed and validated a novel signature associated with LMRGs for predicting disease-free survival in patients with BC. The expression of LMRGs correlates with the immune microenvironment of patients with BC, providing new insights and improved strategies for the diagnosis and treatment of BC.
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Affiliation(s)
- Fan Ji
- Department of Radiology, Medical School, Affiliated Hospital of Nantong University, Nantong University, Nantong, China
| | - Hongyan Qian
- Cancer Research Center Nantong, Nantong Tumor Hospital, The Affiliated Tumor Hospital of Nantong University, Nantong University, Nantong, China
| | - Zhouna Sun
- Cancer Research Center Nantong, Nantong Tumor Hospital, The Affiliated Tumor Hospital of Nantong University, Nantong University, Nantong, China
| | - Ying Yang
- Cancer Research Center Nantong, Nantong Tumor Hospital, The Affiliated Tumor Hospital of Nantong University, Nantong University, Nantong, China
| | - Minxin Shi
- Cancer Research Center Nantong, Nantong Tumor Hospital, The Affiliated Tumor Hospital of Nantong University, Nantong University, Nantong, China.
| | - Hongmei Gu
- Department of Radiology, Medical School, Affiliated Hospital of Nantong University, Nantong University, Nantong, China.
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Li JR, Shaw VR, Parthasarathy A, Li Y, Amos CI, Cheng C. Prognostic stratification in DLBCL patients with aberrant MYC gene. Br J Haematol 2024. [PMID: 39137931 DOI: 10.1111/bjh.19699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Accepted: 07/31/2024] [Indexed: 08/15/2024]
Abstract
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease characterized by a subset of patients who exhibit treatment resistance and poor prognoses. Genomic assays have been widely employed to identify high-risk individuals characterized by rearrangements in the MYC, BCL2 and BCL6 genes. These patients typically undergo more aggressive therapeutic treatments; however, there remains a significant variation in their treatment outcomes. This study introduces an MYC signature score (MYCSS) derived from gene expression profiles, specifically designed to evaluate MYC overactivation in DLBCL patients. MYCSS was validated across several independent cohorts to assess its ability to stratify patients based on MYC-related genetic and molecular aberrations, enhancing the accuracy of prognostic evaluations compared to conventional MYC biomarkers. Our results indicate that MYCSS significantly refines prognostic accuracy beyond that of conventional MYC biomarkers focused on genetic aberrations. More importantly, we found that nearly 50% of patients identified as high risk by traditional MYC metrics actually share similar survival prospects with those having no MYC aberrations. These patients may benefit from standard GCB-based therapies rather than more aggressive treatments. MYCSS provides a robust signature that identifies high-risk patients, aiding in the precision treatment of DLBCL, and minimizing the potential for overtreatment.
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Affiliation(s)
- Jian-Rong Li
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
| | - Vikram R Shaw
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas, USA
| | - Abi Parthasarathy
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas, USA
| | - Yong Li
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
| | - Chao Cheng
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
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Rukhsana, Supty AT, Hussain M, Lee Y. STK3 higher expression association with clinical characteristics in intrinsic subtypes of breast cancer invasive ductal carcinoma patients. Breast Cancer Res Treat 2024; 206:119-129. [PMID: 38592540 DOI: 10.1007/s10549-024-07248-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 01/04/2024] [Indexed: 04/10/2024]
Abstract
PURPOSE STK3 has a central role in maintaining cell homeostasis, proliferation, growth, and apoptosis. Previously, we investigated the functional link between STK3/MST2, and estrogen receptor in MCF-7 breast cancer cells. To expand the investigation, this study evaluated STK3's higher expression and associated genes in breast cancer intrinsic subtypes using publicly available data. METHODS The relationship between clinical pathologic features and STK3 high expression was analyzed using descriptive and multivariate analysis. RESULTS Increased STK3 expression in breast cancer was significantly associated with higher pathological cancer stages, and a different expression level was observed in the intrinsic subtypes of breast cancer. Kaplan-Meier analysis showed that breast cancer with high STK3 had a lower survival rate in IDC patients than that with low STK3 expression (p < 0.05). The multivariate analysis unveiled a strong correlation between STK3 expression and the survival rate among IDC patients, demonstrating hazard ratios for lower expression. In the TCGA dataset, the hazard ratio was 0.56 (95% CI 0.34-0.94, p = 0.029) for patients deceased with tumor, and 0.62 (95% CI 0.42-0.92, p = 0.017) for all deceased patients. Additionally, in the METABRIC dataset, the hazard ratio was 0.76 (95% CI 0.64-0.91, p = 0.003) for those deceased with tumor. From GSEA outcomes 7 gene sets were selected based on statistical significance (FDR < 0.25 and p < 0.05). Weighted Sum model (WSM) derived top 5% genes also have higher expression in basal and lower in luminal A in association with STK3. CONCLUSION By introducing a novel bioinformatics approach that combines GSEA and WSM, the study successfully identified the top 5% of genes associated with higher expression of STK3.
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MESH Headings
- Aged
- Female
- Humans
- Middle Aged
- Biomarkers, Tumor/genetics
- Breast Neoplasms/genetics
- Breast Neoplasms/pathology
- Breast Neoplasms/mortality
- Breast Neoplasms/metabolism
- Carcinoma, Ductal, Breast/genetics
- Carcinoma, Ductal, Breast/pathology
- Carcinoma, Ductal, Breast/mortality
- Carcinoma, Ductal, Breast/metabolism
- Gene Expression Profiling
- Gene Expression Regulation, Neoplastic
- Kaplan-Meier Estimate
- Neoplasm Staging
- Prognosis
- Protein Serine-Threonine Kinases/genetics
- Protein Serine-Threonine Kinases/metabolism
- Serine-Threonine Kinase 3/analysis
- Serine-Threonine Kinase 3/genetics
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Affiliation(s)
- Rukhsana
- Department of Integrative Bioscience and Biotechnology, College of Life Science, Sejong University, 209-Neungdong-ro, Gwangjin-gu, 05006, Seoul, Korea
- College of Science and Engineering, University of Derby, Kedleston Road, Derby, DE22 1GB, UK
| | - Afia Tasnim Supty
- Department of Integrative Bioscience and Biotechnology, College of Life Science, Sejong University, 209-Neungdong-ro, Gwangjin-gu, 05006, Seoul, Korea
| | - Maqbool Hussain
- College of Science and Engineering, University of Derby, Kedleston Road, Derby, DE22 1GB, UK.
| | - YoungJoo Lee
- Department of Integrative Bioscience and Biotechnology, College of Life Science, Sejong University, 209-Neungdong-ro, Gwangjin-gu, 05006, Seoul, Korea.
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Zhang X, An K, Ge X, Sun Y, Wei J, Ren W, Wang H, Wang Y, Du Y, He L, Li O, Zhou S, Shi Y, Ren T, Yang YG, Kan Q, Tian X. NSUN2/YBX1 promotes the progression of breast cancer by enhancing HGH1 mRNA stability through m 5C methylation. Breast Cancer Res 2024; 26:94. [PMID: 38844963 PMCID: PMC11155144 DOI: 10.1186/s13058-024-01847-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 05/21/2024] [Indexed: 06/10/2024] Open
Abstract
BACKGROUND RNA m5C methylation has been extensively implicated in the occurrence and development of tumors. As the main methyltransferase, NSUN2 plays a crucial regulatory role across diverse tumor types. However, the precise impact of NSUN2-mediated m5C modification on breast cancer (BC) remains unclear. Our study aims to elucidate the molecular mechanism underlying how NSUN2 regulates the target gene HGH1 (also known as FAM203) through m5C modification, thereby promoting BC progression. Additionally, this study targets at preliminarily clarifying the biological roles of NSUN2 and HGH1 in BC. METHODS Tumor and adjacent tissues from 5 BC patients were collected, and the m5C modification target HGH1 in BC was screened through RNA sequencing (RNA-seq) and single-base resolution m5C methylation sequencing (RNA-BisSeq). Methylation RNA immunoprecipitation-qPCR (MeRIP-qPCR) and RNA-binding protein immunoprecipitation-qPCR (RIP-qPCR) confirmed that the methylation molecules NSUN2 and YBX1 specifically recognized and bound to HGH1 through m5C modification. In addition, proteomics, co-immunoprecipitation (co-IP), and Ribosome sequencing (Ribo-Seq) were used to explore the biological role of HGH1 in BC. RESULTS As the main m5C methylation molecule, NSUN2 is abnormally overexpressed in BC and increases the overall level of RNA m5C. Knocking down NSUN2 can inhibit BC progression in vitro or in vivo. Combined RNA-seq and RNA-BisSeq analysis identified HGH1 as a potential target of abnormal m5C modifications. We clarified the mechanism by which NSUN2 regulates HGH1 expression through m5C modification, a process that involves interactions with the YBX1 protein, which collectively impacts mRNA stability and protein synthesis. Furthermore, this study is the first to reveal the binding interaction between HGH1 and the translation elongation factor EEF2, providing a comprehensive understanding of its ability to regulate transcript translation efficiency and protein synthesis in BC cells. CONCLUSIONS This study preliminarily clarifies the regulatory role of the NSUN2-YBX1-m5C-HGH1 axis from post-transcriptional modification to protein translation, revealing the key role of abnormal RNA m5C modification in BC and suggesting that HGH1 may be a new epigenetic biomarker and potential therapeutic target for BC.
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Affiliation(s)
- Xuran Zhang
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshedong Rd, Zhengzhou, Henan, 450052, China
- Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Ke An
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshedong Rd, Zhengzhou, Henan, 450052, China
- Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Xin Ge
- Department of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Yuanyuan Sun
- Department of Translational Medicine Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Jingyao Wei
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshedong Rd, Zhengzhou, Henan, 450052, China
- Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Weihong Ren
- Department of Laboratory Medicine, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, 450000, China
| | - Han Wang
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshedong Rd, Zhengzhou, Henan, 450052, China
- Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Yueqin Wang
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshedong Rd, Zhengzhou, Henan, 450052, China
- Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Yue Du
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshedong Rd, Zhengzhou, Henan, 450052, China
- Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Lulu He
- Biobank of the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Ouwen Li
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshedong Rd, Zhengzhou, Henan, 450052, China
- Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Shaoxuan Zhou
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshedong Rd, Zhengzhou, Henan, 450052, China
- Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Yong Shi
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshedong Rd, Zhengzhou, Henan, 450052, China
- Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Tong Ren
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshedong Rd, Zhengzhou, Henan, 450052, China
- Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Yun-Gui Yang
- China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 101408, China.
| | - Quancheng Kan
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshedong Rd, Zhengzhou, Henan, 450052, China.
- Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, Henan, 450052, China.
| | - Xin Tian
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshedong Rd, Zhengzhou, Henan, 450052, China.
- Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, Henan, 450052, China.
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Wu Z, Li L, Xu T, Hu Y, Peng X, Zhang Z, Yao X, Peng Q. Elucidating the multifaceted roles of GPR146 in non-specific orbital inflammation: a concerted analytical approach through the prisms of bioinformatics and machine learning. Front Med (Lausanne) 2024; 11:1309510. [PMID: 38903815 PMCID: PMC11188444 DOI: 10.3389/fmed.2024.1309510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 05/13/2024] [Indexed: 06/22/2024] Open
Abstract
Background Non-specific Orbital Inflammation (NSOI) is a chronic idiopathic condition marked by extensive polymorphic lymphoid infiltration in the orbital area. The integration of metabolic and immune pathways suggests potential therapeutic roles for C-peptide and G protein-coupled receptor 146 (GPR146) in diabetes and its sequelae. However, the specific mechanisms through which GPR146 modulates immune responses remain poorly understood. Furthermore, the utility of GPR146 as a diagnostic or prognostic marker for NSOI has not been conclusively demonstrated. Methods We adopted a comprehensive analytical strategy, merging differentially expressed genes (DEGs) from the Gene Expression Omnibus (GEO) datasets GSE58331 and GSE105149 with immune-related genes from the ImmPort database. Our methodology combined LASSO regression and support vector machine-recursive feature elimination (SVM-RFE) for feature selection, followed by Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to explore gene sets co-expressed with GPR146, identifying a significant enrichment in immune-related pathways. The tumor microenvironment's immune composition was quantified using the CIBERSORT algorithm and the ESTIMATE method, which confirmed a positive correlation between GPR146 expression and immune cell infiltration. Validation of GPR146 expression was performed using the GSE58331 dataset. Results Analysis identified 113 DEGs associated with GPR146, with a significant subset showing distinct expression patterns. Using LASSO and SVM-RFE, we pinpointed 15 key hub genes. Functionally, these genes and GPR146 were predominantly linked to receptor ligand activity, immune receptor activity, and cytokine-mediated signaling. Specific immune cells, such as memory B cells, M2 macrophages, resting mast cells, monocytes, activated NK cells, plasma cells, and CD8+ T cells, were positively associated with GPR146 expression. In contrast, M0 macrophages, naive B cells, M1 macrophages, activated mast cells, activated memory CD4+ T cells, naive CD4+ T cells, and gamma delta T cells showed inverse correlations. Notably, our findings underscore the potential diagnostic relevance of GPR146 in distinguishing NSOI. Conclusion Our study elucidates the immunological signatures associated with GPR146 in the context of NSOI, highlighting its prognostic and diagnostic potential. These insights pave the way for GPR146 to be a novel biomarker for monitoring the progression of NSOI, providing a foundation for future therapeutic strategies targeting immune-metabolic pathways.
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Affiliation(s)
- Zixuan Wu
- Hunan University of Traditional Chinese Medicine, Changsha, Hunan, China
| | - Ling Li
- Dongying People’s Hospital (Dongying Hospital of Shandong Provincial Hospital Group), Dongying, Shandong, China
| | - Tingting Xu
- Dongying People’s Hospital (Dongying Hospital of Shandong Provincial Hospital Group), Dongying, Shandong, China
| | - Yi Hu
- Hunan University of Traditional Chinese Medicine, Changsha, Hunan, China
| | - Xin Peng
- Hunan University of Traditional Chinese Medicine, Changsha, Hunan, China
| | - Zheyuan Zhang
- Hunan University of Traditional Chinese Medicine, Changsha, Hunan, China
| | - Xiaolei Yao
- Department of Ophthalmology, The First Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Changsha, Hunan, China
| | - Qinghua Peng
- Hunan University of Traditional Chinese Medicine, Changsha, Hunan, China
- Department of Ophthalmology, The First Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Changsha, Hunan, China
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Liu X, Xie X, Sui C, Liu X, Song M, Luo Q, Zhan P, Feng J, Liu J. Unraveling the cross-talk between N6-methyladenosine modification and non-coding RNAs in breast cancer: Mechanisms and clinical implications. Int J Cancer 2024; 154:1877-1889. [PMID: 38429857 DOI: 10.1002/ijc.34900] [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: 10/11/2023] [Revised: 02/02/2024] [Accepted: 02/14/2024] [Indexed: 03/03/2024]
Abstract
In recent years, breast cancer (BC) has surpassed lung cancer as the most common malignant tumor worldwide and remains the leading cause of cancer death in women. The etiology of BC usually involves dysregulation of epigenetic mechanisms and aberrant expression of certain non-coding RNAs (ncRNAs). N6-methyladenosine (m6A), the most prevalent RNA modification in eukaryotes, widely exists in ncRNAs to affect its biosynthesis and function, and is an important regulator of tumor-related signaling pathways. Interestingly, ncRNAs can also regulate or target m6A modification, playing a key role in cancer progression. However, the m6A-ncRNAs regulatory network in BC has not been fully elucidated, especially the regulation of m6A modification by ncRNAs. Therefore, in this review, we comprehensively summarize the interaction mechanisms and biological significance of m6A modifications and ncRNAs in BC. Meanwhile, we also focused on the clinical application value of m6A modification in BC diagnosis and prognosis, intending to explore new biomarkers and potential therapeutic targets.
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Affiliation(s)
- Xuan Liu
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Luzhou, Sichuan, China
| | - Xuelong Xie
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Luzhou, Sichuan, China
| | - Chentao Sui
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Luzhou, Sichuan, China
| | - Xuexue Liu
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Luzhou, Sichuan, China
| | - Miao Song
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Luzhou, Sichuan, China
| | - Qing Luo
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Luzhou, Sichuan, China
| | - Ping Zhan
- Department of Obstetrics, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Jia Feng
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Luzhou, Sichuan, China
| | - Jinbo Liu
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Luzhou, Sichuan, China
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8
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Gygi JP, Konstorum A, Pawar S, Aron E, Kleinstein SH, Guan L. A supervised Bayesian factor model for the identification of multi-omics signatures. Bioinformatics 2024; 40:btae202. [PMID: 38603606 PMCID: PMC11078774 DOI: 10.1093/bioinformatics/btae202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 02/29/2024] [Accepted: 04/10/2024] [Indexed: 04/13/2024] Open
Abstract
MOTIVATION Predictive biological signatures provide utility as biomarkers for disease diagnosis and prognosis, as well as prediction of responses to vaccination or therapy. These signatures are identified from high-throughput profiling assays through a combination of dimensionality reduction and machine learning techniques. The genes, proteins, metabolites, and other biological analytes that compose signatures also generate hypotheses on the underlying mechanisms driving biological responses, thus improving biological understanding. Dimensionality reduction is a critical step in signature discovery to address the large number of analytes in omics datasets, especially for multi-omics profiling studies with tens of thousands of measurements. Latent factor models, which can account for the structural heterogeneity across diverse assays, effectively integrate multi-omics data and reduce dimensionality to a small number of factors that capture correlations and associations among measurements. These factors provide biologically interpretable features for predictive modeling. However, multi-omics integration and predictive modeling are generally performed independently in sequential steps, leading to suboptimal factor construction. Combining these steps can yield better multi-omics signatures that are more predictive while still being biologically meaningful. RESULTS We developed a supervised variational Bayesian factor model that extracts multi-omics signatures from high-throughput profiling datasets that can span multiple data types. Signature-based multiPle-omics intEgration via lAtent factoRs (SPEAR) adaptively determines factor rank, emphasis on factor structure, data relevance and feature sparsity. The method improves the reconstruction of underlying factors in synthetic examples and prediction accuracy of coronavirus disease 2019 severity and breast cancer tumor subtypes. AVAILABILITY AND IMPLEMENTATION SPEAR is a publicly available R-package hosted at https://bitbucket.org/kleinstein/SPEAR.
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Affiliation(s)
- Jeremy P Gygi
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT 06520, United States
| | - Anna Konstorum
- Department of Pathology, Yale School of Medicine, New Haven, CT 06520, United States
| | - Shrikant Pawar
- Department of Genetics, Yale Center for Genomic Analysis (YCGA), Yale School of Medicine, New Haven, CT 06520, United States
| | - Edel Aron
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT 06520, United States
| | - Steven H Kleinstein
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT 06520, United States
- Department of Pathology, Yale School of Medicine, New Haven, CT 06520, United States
- Department of Immunobiology, Yale School of Medicine, New Haven, CT 06520, United States
| | - Leying Guan
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, United States
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Li N, Yu K, Huang D, Li S, Zeng D, Li J, Fan L. Molecular Characterization of Cuproptosis-related lncRNAs: Defining Molecular Subtypes and a Prognostic Signature of Ovarian Cancer. Biol Trace Elem Res 2024; 202:1428-1445. [PMID: 37528285 DOI: 10.1007/s12011-023-03780-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 07/14/2023] [Indexed: 08/03/2023]
Abstract
Cuproptosis, a newly discovered form of programmed cell death, relies on mitochondrial respiration, the chain of which has been found to be altered in ovarian cancer (OC). The current work probed into the effects of Cuproptosis on the prognosis, immune microenvironment and therapeutic response of OC based on Cuproptosis-related lncRNAs. Data on OC gene expression and clinical characteristics were collected from TCGA, ICGC and GEO databases, and mRNA and lncRNA were distinguished. Cuproptosis-related lncRNAs were screened for consensus clustering analysis. Differentially expressed lncRNAs (DElncRNAs) were identified between clusters, and least absolute shrinkage and selection operator (LASSO) and Cox regression analysis were performed to establish a prognostic signature. Its potential value in OC was evaluated by Gene Set Enrichment Analysis (GSEA), tumor cell mutation and immune microenvironment analysis, and response to immunotherapy and antineoplastic drugs. According to the classification scheme of Cuproptosis-related lncRNAs, OC was divided into four molecular subtypes, which were different in survival time, immune characteristics and somatic mutation. The prognostic signature between subtypes included 10 lncRNAs, which were significantly correlated with the prognosis, immune microenvironment related indexes, the expression of immune checkpoint molecules and the sensitivity of antineoplastic drug Paclitaxel and Gefitinib of OC. We examined the expression of ten LncRNAs in OC cell lines and found that LINC00189, ZFHX4-AS1, RPS6KA2-IT1 and C9orf106 were expressed elevated in OC cell lines, and LINC00861, LINC00582, DEPDC1-AS1, LINC01556, LEMD1-AS1, TYMSOS expression was decreased in OC cell lines. The results of CCK8 showed that the cell viability of OC cells decreased after inhibition of C9orf106, whereas the cell viability of OC cells increased after inhibition of LEMD1-AS1. This work revealed new Cuproptosis-related lncRNA molecular subtypes exhibiting tumor microenvironment (TME) heterogeneity for OC and proposed a prognostic signature that may have benefits in understanding the prognosis, pathological features and immune microenvironment of OC patients.
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Affiliation(s)
- Nan Li
- Reproductive Medicine Center, Liuzhou Maternity and Child Health Care Hospital, Liuzhou, 545001, China
- Liuzhou Institute of Reproduction and Genetics, Liuzhou, 545001, China
- Affiliated Maternity Hospital and Affiliated Children's Hospital of Guangxi, University of Science and Technology, Liuzhou, 545001, China
- Guangxi Health Commission Key Laboratory of Birth Cohort Study in Pregnant Women of Advanced Age, Liuzhou, 545001, China
| | - Kai Yu
- College of Animal Science and Technology, Guangxi University, Nanning, 530004, China
| | - Delun Huang
- Department of Physiology, Guangxi University of Chinese Medicine, Nanning, 530001, China
| | - Shu Li
- Reproductive Medicine Center, Liuzhou Maternity and Child Health Care Hospital, Liuzhou, 545001, China
- Liuzhou Institute of Reproduction and Genetics, Liuzhou, 545001, China
- Affiliated Maternity Hospital and Affiliated Children's Hospital of Guangxi, University of Science and Technology, Liuzhou, 545001, China
- Guangxi Health Commission Key Laboratory of Birth Cohort Study in Pregnant Women of Advanced Age, Liuzhou, 545001, China
| | - Dingyuan Zeng
- Affiliated Maternity Hospital and Affiliated Children's Hospital of Guangxi, University of Science and Technology, Liuzhou, 545001, China
- The Department of Obstetrics and Gynecology, Liuzhou Maternity and Child Health Care Hospital, Liuzhou, 545001, China
| | - Jingjing Li
- Reproductive Medicine Center, Liuzhou Maternity and Child Health Care Hospital, Liuzhou, 545001, China.
- Liuzhou Institute of Reproduction and Genetics, Liuzhou, 545001, China.
- Affiliated Maternity Hospital and Affiliated Children's Hospital of Guangxi, University of Science and Technology, Liuzhou, 545001, China.
- Guangxi Health Commission Key Laboratory of Birth Cohort Study in Pregnant Women of Advanced Age, Liuzhou, 545001, China.
| | - Li Fan
- Reproductive Medicine Center, Liuzhou Maternity and Child Health Care Hospital, Liuzhou, 545001, China.
- Liuzhou Institute of Reproduction and Genetics, Liuzhou, 545001, China.
- Affiliated Maternity Hospital and Affiliated Children's Hospital of Guangxi, University of Science and Technology, Liuzhou, 545001, China.
- Guangxi Health Commission Key Laboratory of Birth Cohort Study in Pregnant Women of Advanced Age, Liuzhou, 545001, China.
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Wu Z, Fang C, Hu Y, Peng X, Zhang Z, Yao X, Peng Q. Bioinformatic validation and machine learning-based exploration of purine metabolism-related gene signatures in the context of immunotherapeutic strategies for nonspecific orbital inflammation. Front Immunol 2024; 15:1318316. [PMID: 38605967 PMCID: PMC11007227 DOI: 10.3389/fimmu.2024.1318316] [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: 11/07/2023] [Accepted: 03/20/2024] [Indexed: 04/13/2024] Open
Abstract
Background Nonspecific orbital inflammation (NSOI) represents a perplexing and persistent proliferative inflammatory disorder of idiopathic nature, characterized by a heterogeneous lymphoid infiltration within the orbital region. This condition, marked by the aberrant metabolic activities of its cellular constituents, starkly contrasts with the metabolic equilibrium found in healthy cells. Among the myriad pathways integral to cellular metabolism, purine metabolism emerges as a critical player, providing the building blocks for nucleic acid synthesis, such as DNA and RNA. Despite its significance, the contribution of Purine Metabolism Genes (PMGs) to the pathophysiological landscape of NSOI remains a mystery, highlighting a critical gap in our understanding of the disease's molecular underpinnings. Methods To bridge this knowledge gap, our study embarked on an exploratory journey to identify and validate PMGs implicated in NSOI, employing a comprehensive bioinformatics strategy. By intersecting differential gene expression analyses with a curated list of 92 known PMGs, we aimed to pinpoint those with potential roles in NSOI. Advanced methodologies, including Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), facilitated a deep dive into the biological functions and pathways associated with these PMGs. Further refinement through Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) enabled the identification of key hub genes and the evaluation of their diagnostic prowess for NSOI. Additionally, the relationship between these hub PMGs and relevant clinical parameters was thoroughly investigated. To corroborate our findings, we analyzed expression data from datasets GSE58331 and GSE105149, focusing on the seven PMGs identified as potentially crucial to NSOI pathology. Results Our investigation unveiled seven PMGs (ENTPD1, POLR2K, NPR2, PDE6D, PDE6H, PDE4B, and ALLC) as intimately connected to NSOI. Functional analyses shed light on their involvement in processes such as peroxisome targeting sequence binding, seminiferous tubule development, and ciliary transition zone organization. Importantly, the diagnostic capabilities of these PMGs demonstrated promising efficacy in distinguishing NSOI from non-affected states. Conclusions Through rigorous bioinformatics analyses, this study unveils seven PMGs as novel biomarker candidates for NSOI, elucidating their potential roles in the disease's pathogenesis. These discoveries not only enhance our understanding of NSOI at the molecular level but also pave the way for innovative approaches to monitor and study its progression, offering a beacon of hope for individuals afflicted by this enigmatic condition.
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Affiliation(s)
- Zixuan Wu
- Hunan University of Traditional Chinese Medicine, Changsha, Hunan, China
| | - Chi Fang
- Department of Ophthalmology, the First Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Changsha, Hunan, China
| | - Yi Hu
- Hunan University of Traditional Chinese Medicine, Changsha, Hunan, China
| | - Xin Peng
- Hunan University of Traditional Chinese Medicine, Changsha, Hunan, China
| | - Zheyuan Zhang
- Hunan University of Traditional Chinese Medicine, Changsha, Hunan, China
| | - Xiaolei Yao
- Department of Ophthalmology, the First Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Changsha, Hunan, China
| | - Qinghua Peng
- Hunan University of Traditional Chinese Medicine, Changsha, Hunan, China
- Department of Ophthalmology, the First Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Changsha, Hunan, China
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Xu J, Guo K, Sheng X, Huang Y, Wang X, Dong J, Qin H, Wang C. Correlation analysis of disulfidptosis-related gene signatures with clinical prognosis and immunotherapy response in sarcoma. Sci Rep 2024; 14:7158. [PMID: 38531930 DOI: 10.1038/s41598-024-57594-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 03/20/2024] [Indexed: 03/28/2024] Open
Abstract
Disulfidptosis, a newly discovered type of programmed cell death, could be a mechanism of cell death controlled by SLC7A11. This could be closely associated with tumor development and advancement. Nevertheless, the biological mechanism behind disulfidptosis-related genes (DRGs) in sarcoma (SARC) is uncertain. This study identified three valuable genes (SLC7A11, RPN1, GYS1) associated with disulfidptosis in sarcoma (SARC) and developed a prognostic model. The multiple databases and RT-qPCR data confirmed the upregulated expression of prognostic DRGs in SARC. The TCGA internal and ICGC external validation cohorts were utilized to validate the predictive model capacity. Our analysis of DRG riskscores revealed that the low-risk group exhibited a more favorable prognosis than the high-risk group. Furthermore, we observed a significant association between DRG riskscores and different clinical features, immune cell infiltration, immune therapeutic sensitivity, drug sensitivity, and RNA modification regulators. In addition, two external independent immunetherapy datasets and clinical tissue samples were collected, validating the value of the DRGs risk model in predicting immunotherapy response. Finally, the SLC7A11/hsa-miR-29c-3p/LINC00511, and RPN1/hsa-miR-143-3p/LINC00511 regulatory axes were constructed. This study provided DRG riskscore signatures to predict prognosis and response to immunotherapy in SARC, guiding personalized treatment decisions.
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Affiliation(s)
- Juan Xu
- Department of Oncology, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Kangwen Guo
- Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Xiaoan Sheng
- Department of Oncology, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Yuting Huang
- Department of Oncology, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Xuewei Wang
- Department of Oncology, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Juanjuan Dong
- Department of Oncology, Chaohu Hospital of Anhui Medical University, Hefei, China.
| | - Haotian Qin
- National and Local Joint Engineering Research Center of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, China.
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, China.
| | - Chao Wang
- Department of Oncology, Chaohu Hospital of Anhui Medical University, Hefei, China.
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Ma J, Hu J, Zhao L, Wu Z, Li R, Deng W. Identification of clinical prognostic factors and analysis of ferroptosis-related gene signatures in the bladder cancer immune microenvironment. BMC Urol 2024; 24:6. [PMID: 38172792 PMCID: PMC10765654 DOI: 10.1186/s12894-023-01354-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 10/27/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Bladder cancer (BLCA) is a prevalent malignancy affecting the urinary system and poses a significant burden in terms of both incidence and mortality rates on a global scale. Among all BLCA cases, non-muscle invasive bladder cancer constitutes approximately 75% of the total. In recent years, the concept of ferroptosis, an iron-dependent form of regulated cell death marked by the accumulation of lipid peroxides, has captured the attention of researchers worldwide. Nevertheless, the precise involvement of ferroptosis-related genes (FRGs) in the anti-BLCA response remains inadequately elucidated. METHODS The integration of BLCA samples from the TCGA and GEO datasets facilitated the quantitative evaluation of FRGs, offering potential insights into their predictive capabilities. Leveraging the wealth of information encompassing mRNAsi, gene mutations, CNV, TMB, and clinical features within these datasets further enriched the analysis, augmenting its robustness and reliability. Through the utilization of Lasso regression, a prediction model was developed, enabling accurate prognostic assessments within the context of BLCA. Additionally, co-expression analysis shed light on the complex relationship between gene expression patterns and FRGs, unraveling their functional relevance and potential implications in BLCA. RESULTS FRGs exhibited increased expression levels in the high-risk cohort of BLCA patients, even in the absence of other clinical indicators, suggesting their potential as prognostic markers. GSEA revealed enrichment of immunological and tumor-related pathways specifically in the high-risk group. Furthermore, notable differences were observed in immune function and m6a gene expression between the low- and high-risk groups. Several genes, including MYBPH, SOST, SPRR2A, and CRNN, were found to potentially participate in the oncogenic processes underlying BLCA. Additionally, CYP4F8, PDZD3, CRTAC1, and LRTM1 were identified as potential tumor suppressor genes. Significant discrepancies in immunological function and m6a gene expression were observed between the two risk groups, further highlighting the distinct molecular characteristics associated with different prognostic outcomes. Notably, strong correlations were observed among the prognostic model, CNVs, SNPs, and drug sensitivity profiles. CONCLUSIONS FRGs are associated with the onset and progression of BLCA. A FRGs signature offers a viable alternative to predict BLCA, and these FRGs show a prospective research area for BLCA targeted treatment in the future.
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Affiliation(s)
- Jiafu Ma
- Emergency Department, People's Hospital Affiliated to Shandong First Medical University, Jinan, 250011, Shandong Province, China
| | - Jianting Hu
- Department of Urology, Laiyang People's Hospital, Yantai City, 265202, Shandong Province, China
| | - Leizuo Zhao
- Dongying People's Hospital, Dongying, 257091, Shandong Province, China
| | - Zixuan Wu
- Guangzhou University of Chinese Medicine, Guangzhou, 510006, Guangdong Province, China
| | - Rongfen Li
- Dongying People's Hospital, Dongying, 257091, Shandong Province, China.
| | - Wentao Deng
- Dongying People's Hospital, Dongying, 257091, Shandong Province, China.
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Yuan M, Ding H, Guo B, Yang M, Yang Y, Xu XS. Image-Based Subtype Classification for Glioblastoma Using Deep Learning: Prognostic Significance and Biologic Relevance. JCO Clin Cancer Inform 2024; 8:e2300154. [PMID: 38231003 DOI: 10.1200/cci.23.00154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 11/03/2023] [Accepted: 11/21/2023] [Indexed: 01/18/2024] Open
Abstract
PURPOSE To apply deep learning algorithms to histopathology images, construct image-based subtypes independent of known clinical and molecular classifications for glioblastoma, and produce novel insights into molecular and immune characteristics of the glioblastoma tumor microenvironment. MATERIALS AND METHODS Using whole-slide hematoxylin and eosin images from 214 patients with glioblastoma in The Cancer Genome Atlas (TCGA), a fine-tuned convolutional neural network model extracted deep learning features. Biclustering was used to identify subtypes and image feature modules. Prognostic value of image subtypes was assessed via Cox regression on survival outcomes and validated with 189 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set. Morphological, molecular, and immune characteristics of glioblastoma image subtypes were analyzed. RESULTS Four distinct subtypes and modules (imClust1-4) were identified for the TCGA patients with glioblastoma on the basis of the image feature data. The glioblastoma image subtypes were significantly associated with overall survival (OS; P = .028) and progression-free survival (P = .003). Apparent association was also observed for disease-specific survival (P = .096). imClust2 had the best prognosis for all three survival end points (eg, after 25 months, imClust2 had >7% surviving patients than the other subtypes). Examination of OS in the external validation using the unseen CPTAC data set showed consistent patterns. Multivariable Cox analyses confirmed that the image subtypes carry unique prognostic information independent of known clinical and molecular predictors. Molecular and immune profiling revealed distinct immune compositions of the tumor microenvironment in different image subtypes and may provide biologic explanations for the patterns in patients' outcomes. CONCLUSION Our image-based subtype classification on the basis of deep learning models is a novel tool to refine risk stratification in cancers. The image subtypes detected for glioblastoma represent a promising prognostic biomarker with distinct molecular and immune characteristics and may facilitate developing novel, individualized immunotherapies for glioblastoma.
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Affiliation(s)
- Min Yuan
- Department of Health Data Science, Anhui Medical University, Hefei, China
| | - Haolun Ding
- Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, China
| | - Bangwei Guo
- School of Data Science, University of Science and Technology of China, Hefei, China
| | - Miaomiao Yang
- Clinical Pathology Center, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yaning Yang
- Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, China
| | - Xu Steven Xu
- Clinical Pharmacology and Quantitative Science, Genmab Inc, Princeton, NJ
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Yang C, Cheng X, Gao S, Pan Q. Integrating bulk and single-cell data to predict the prognosis and identify the immune landscape in HNSCC. J Cell Mol Med 2024; 28:e18009. [PMID: 37882107 PMCID: PMC10805493 DOI: 10.1111/jcmm.18009] [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: 07/13/2023] [Revised: 09/20/2023] [Accepted: 10/13/2023] [Indexed: 10/27/2023] Open
Abstract
The complex interplay between tumour cells and the tumour microenvironment (TME) underscores the necessity for gaining comprehensive insights into disease progression. This study centres on elucidating the elusive the elusive role of endothelial cells within the TME of head and neck squamous cell carcinoma (HNSCC). Despite their crucial involvement in angiogenesis and vascular function, the mechanistic diversity of endothelial cells among HNSCC patients remains largely uncharted. Leveraging advanced single-cell RNA sequencing (scRNA-Seq) technology and the Scissor algorithm, we aimed to bridge this knowledge gap and illuminate the intricate interplay between endothelial cells and patient prognosis within the context of HNSCC. Here, endothelial cells were categorized into Scissorhigh and Scissorlow subtypes. We identified Scissor+ endothelial cells exhibiting pro-tumorigenic profiles and constructed a prognostic risk model for HNSCC. Additionally, four biomarkers also were identified by analysing the gene expression profiles of patients with HNSCC and a prognostic risk prediction model was constructed based on these genes. Furthermore, the correlations between endothelial cells and prognosis of patients with HNSCC were analysed by integrating bulk and single-cell sequencing data, revealing a close association between SHSS and the overall survival (OS) of HNSCC patients with malignant endothelial cells. Finally, we validated the prognostic model by RT-qPCR and IHC analysis. These findings enhance our comprehension of TME heterogeneity at the single-cell level and provide a prognostic model for HNSCC.
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Affiliation(s)
- Chunlong Yang
- Clinical Research CenterAffiliated Hospital of Guangdong Medical UniversityZhanjiangChina
| | - Xiaoning Cheng
- Zhanjiang Central HospitalGuangdong Medical UniversityZhanjiangChina
| | - Shenglan Gao
- Clinical Research CenterAffiliated Hospital of Guangdong Medical UniversityZhanjiangChina
| | - Qingjun Pan
- Clinical Research CenterAffiliated Hospital of Guangdong Medical UniversityZhanjiangChina
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15
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Wu Z, Gao Y, Cao L, Peng Q, Yao X. Purine metabolism-related genes and immunization in thyroid eye disease were validated using bioinformatics and machine learning. Sci Rep 2023; 13:18391. [PMID: 37884559 PMCID: PMC10603126 DOI: 10.1038/s41598-023-45048-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/15/2023] [Indexed: 10/28/2023] Open
Abstract
Thyroid eye disease (TED), an autoimmune inflammatory disorder affecting the orbit, exhibits a range of clinical manifestations. While the disease presentation can vary, cases that adhere to a prototypical pattern typically commence with mild symptoms that subsequently escalate in severity before entering a phase of stabilization. Notably, the metabolic activity of cells implicated in the disease substantially deviates from that of healthy cells, with purine metabolism representing a critical facet of cellular material metabolism by supplying components essential for DNA and RNA synthesis. Nevertheless, the precise involvement of Purine Metabolism Genes (PMGs) in the defensive mechanism against TED remains largely unexplored. The present study employed a bioinformatics approach to identify and validate potential PMGs associated with TED. A curated set of 65 candidate PMGs was utilized to uncover novel PMGs through a combination of differential expression analysis and a PMG dataset. Furthermore, GSEA and GSVA were employed to explore the biological functions and pathways associated with the newly identified PMGs. Subsequently, the Lasso regression and SVM-RFE algorithms were applied to identify hub genes and assess the diagnostic efficacy of the top 10 PMGs in distinguishing TED. Additionally, the relationship between hub PMGs and clinical characteristics was investigated. Finally, the expression levels of the identified ten PMGs were validated using the GSE58331 and GSE105149 datasets. This study revealed ten PMGs related with TED. PRPS2, PFAS, ATIC, NT5C1A, POLR2E, POLR2F, POLR3B, PDE3A, ADSS, and NTPCR are among the PMGs. The biological function investigation revealed their participation in processes such as RNA splicing, purine-containing chemical metabolism, and purine nucleotide metabolism. Furthermore, the diagnostic performance of the 10 PMGs in differentiating TED was encouraging. This study was effective in identifying ten PMGs linked to TED. These findings provide light on potential new biomarkers for TED and open up possibilities for tracking disease development.
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Affiliation(s)
- Zixuan Wu
- Hunan University of Traditional Chinese Medicine, Changsha, 410208, Hunan Province, China
| | - Yuan Gao
- Hunan University of Traditional Chinese Medicine, Changsha, 410208, Hunan Province, China
| | - Liyuan Cao
- Hunan University of Traditional Chinese Medicine, Changsha, 410208, Hunan Province, China
| | - Qinghua Peng
- Hunan University of Traditional Chinese Medicine, Changsha, 410208, Hunan Province, China.
- Department of Ophthalmology, the First Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Changsha, 410007, Hunan Province, China.
| | - Xiaolei Yao
- Department of Ophthalmology, the First Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Changsha, 410007, Hunan Province, China.
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Zhao J, Zhang Q, Liu M, Zhao X. MRI-based radiomics approach for the prediction of recurrence-free survival in triple-negative breast cancer after breast-conserving surgery or mastectomy. Medicine (Baltimore) 2023; 102:e35646. [PMID: 37861556 PMCID: PMC10589522 DOI: 10.1097/md.0000000000035646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 09/22/2023] [Indexed: 10/21/2023] Open
Abstract
To explore the value of a radiomics signature and develop a nomogram combined with a radiomics signature and clinical factors for predicting recurrence-free survival in triple-negative breast cancer patients. We enrolled 151 patients from the cancer imaging archive who underwent preoperative contrast-enhanced magnetic resonance imaging. They were assigned to training, validation and external validation cohorts. Image features with coefficients not equal to zero in the 10-fold cross-validation were selected to generate a radiomics signature. Based on the optimal cutoff value of the radiomics signature determined by maximally selected log-rank statistics, patients were stratified into high- and low-risk groups in the training and validation cohorts. Kaplan-Meier survival analysis was performed for both groups. Kaplan-Meier survival distributions in these groups were compared using log-rank tests. Univariate and multivariate Cox regression analyses were used to construct clinical and combined models. Concordance index was used to assess the predictive performance of the 3 models. Calibration of the combined model was assessed using calibration curves. Four image features were selected to generate the radiomics signature. The Kaplan-Meier survival distributions of patients in the 2 groups were significantly different in the training (P < .001) and validation cohorts (P = .001). The C-indices of the radiomics model, clinical model, and combined model in the training and validation cohorts were 0.772, 0.700, 0.878, and 0.744, 0.574, 0.777, respectively. The C-indices of the radiomics model, clinical model, and combined model in the external validation cohort were 0.778, 0.733, 0.822, respectively. The calibration curves of the combined model showed good calibration. The radiomics signature can predict recurrence-free survival of patients with triple-negative breast cancer and improve the predictive performance of the clinical model.
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Affiliation(s)
- Jingwei Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qi Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Muqing Liu
- Department of Radiology, Chaoyang Central Hospital, Chaoyang, China
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Wu Z, Feng Z, Wei H, Lin C, Chen K. Development and validation of prognostic index based on purine metabolism genes in patients with bladder cancer. Front Med (Lausanne) 2023; 10:1193133. [PMID: 37780567 PMCID: PMC10536175 DOI: 10.3389/fmed.2023.1193133] [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: 04/05/2023] [Accepted: 07/07/2023] [Indexed: 10/03/2023] Open
Abstract
Background Bladder cancer (BLCA) is a prevalent malignancy affecting the urinary system and is associated with significant morbidity and mortality worldwide. Dysregulation of tumor metabolic pathways is closely linked to the initiation and proliferation of BLCA. Tumor cells exhibit distinct metabolic activities compared to normal cells, and the purine metabolism pathway, responsible for providing essential components for DNA and RNA synthesis, is believed to play a crucial role. However, the precise involvement of Purine Metabolism Genes (PMGs) in the defense mechanism against BLCA remains elusive. Methods The integration of BLCA samples from the TCGA and GEO datasets facilitated the quantitative evaluation of PMGs, offering potential insights into their predictive capabilities. Leveraging the wealth of information encompassing mRNAsi, gene mutations, CNV, TMB, and clinical features within these datasets further enriched the analysis, augmenting its robustness and reliability. Through the utilization of Lasso regression, a prediction model was developed, enabling accurate prognostic assessments within the context of BLCA. Additionally, co-expression analysis shed light on the complex relationship between gene expression patterns and PMGs, unraveling their functional relevance and potential implications in BLCA. Results PMGs exhibited increased expression levels in the high-risk cohort of BLCA patients, even in the absence of other clinical indicators, suggesting their potential as prognostic markers. GSEA revealed enrichment of immunological and tumor-related pathways specifically in the high-risk group. Furthermore, notable differences were observed in immune function and m6a gene expression between the low- and high-risk groups. Several genes, including CLDN6, CES1, SOST, SPRR2A, MYBPH, CGB5, and KRT1, were found to potentially participate in the oncogenic processes underlying BLCA. Additionally, CRTAC1 was identified as potential tumor suppressor genes. Significant discrepancies in immunological function and m6a gene expression were observed between the two risk groups, further highlighting the distinct molecular characteristics associated with different prognostic outcomes. Notably, strong correlations were observed among the prognostic model, CNVs, SNPs, and drug sensitivity profiles. Conclusion PMGs have been implicated in the etiology and progression of bladder cancer (BLCA). Prognostic models corresponding to this malignancy aid in the accurate prediction of patient outcomes. Notably, exploring the potential therapeutic targets within the tumor microenvironment (TME) such as PMGs and immune cell infiltration holds promise for effective BLCA management, albeit necessitating further research. Moreover, the identification of a gene signature associated with purine Metabolism presents a credible and alternative approach for predicting BLCA, signifying a burgeoning avenue for targeted therapeutic investigations in the field of BLCA.
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Affiliation(s)
- Zixuan Wu
- Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Ziqing Feng
- Department of Clinical Laboratory, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hongyan Wei
- Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Chuying Lin
- Department of Clinical Laboratory, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ke Chen
- Department of Clinical Laboratory, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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Wu HW, Wu JD, Yeh YP, Wu TH, Chao CH, Wang W, Chen TW. DoSurvive: A webtool for investigating the prognostic power of a single or combined cancer biomarker. iScience 2023; 26:107269. [PMID: 37609633 PMCID: PMC10440714 DOI: 10.1016/j.isci.2023.107269] [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: 11/22/2022] [Revised: 05/26/2023] [Accepted: 06/28/2023] [Indexed: 08/24/2023] Open
Abstract
We present DoSurvive, a user-friendly survival analysis web tool and a cancer prognostic biomarker centered database. DoSurvive is the first database that allows users to perform multivariant survival analysis for cancers with customized gene/patient list. DoSurvive offers three survival analysis methods, Log rank test, Cox regression and accelerated failure time model (AFT), for users to analyze five types of quantitative features (mRNA, miRNA, lncRNA, protein and methylation of CpG islands) with four survival types, i.e. overall survival, disease-specific survival, disease-free interval, and progression-free interval, in 33 cancer types. Notably, the implemented AFT model provides an alternative method for genes/features which failed the proportional hazard assumption in Cox regression. With the unprecedented number of survival models implemented and high flexibility in analysis, DoSurvive is a unique platform for the identification of clinically relevant targets for cancer researcher and practitioners. DoSurvive is freely available at http://dosurvive.lab.nycu.edu.tw/.
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Affiliation(s)
- Hao-Wei Wu
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 30068, Taiwan
| | - Jian-De Wu
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 30068, Taiwan
| | - Yen-Ping Yeh
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 30068, Taiwan
| | - Timothy H. Wu
- Institute of Ecology and Evolutionary Biology, National Taiwan University, Taipei 10617, Taiwan
| | - Chi-Hong Chao
- Institute of Molecular Medicine and Bioengineering, National Yang Ming Chiao Tung University, Hsinchu 30068, Taiwan
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 30068, Taiwan
- Center For Intelligent Drug Systems and Smart Bio-devices (IDSB), National Yang Ming Chiao Tung University, Hsinchu 30068, Taiwan
| | - Weijing Wang
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu 30068, Taiwan
| | - Ting-Wen Chen
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 30068, Taiwan
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 30068, Taiwan
- Center For Intelligent Drug Systems and Smart Bio-devices (IDSB), National Yang Ming Chiao Tung University, Hsinchu 30068, Taiwan
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Wu Z, Li X, Gu Z, Xia X, Yang J. Pyrimidine metabolism regulator-mediated molecular subtypes display tumor microenvironmental hallmarks and assist precision treatment in bladder cancer. Front Oncol 2023; 13:1102518. [PMID: 37664033 PMCID: PMC10470057 DOI: 10.3389/fonc.2023.1102518] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 03/20/2023] [Indexed: 09/05/2023] Open
Abstract
Background Bladder cancer (BLCA) is a common urinary system malignancy with a significant morbidity and death rate worldwide. Non-muscle invasive BLCA accounts for over 75% of all BLCA cases. The imbalance of tumor metabolic pathways is associated with tumor formation and proliferation. Pyrimidine metabolism (PyM) is a complex enzyme network that incorporates nucleoside salvage, de novo nucleotide synthesis, and catalytic pyrimidine degradation. Metabolic reprogramming is linked to clinical prognosis in several types of cancer. However, the role of pyrimidine metabolism Genes (PyMGs) in the BLCA-fighting process remains poorly understood. Methods Predictive PyMGs were quantified in BLCA samples from the TCGA and GEO datasets. TCGA and GEO provided information on stemness indices (mRNAsi), gene mutations, CNV, TMB, and corresponding clinical features. The prediction model was built using Lasso regression. Co-expression analysis was conducted to investigate the relationship between gene expression and PyM. Results PyMGs were overexpressed in the high-risk sample in the absence of other clinical symptoms, demonstrating their predictive potential for BLCA outcome. Immunological and tumor-related pathways were identified in the high-risk group by GSWA. Immune function and m6a gene expression varied significantly between the risk groups. In BLCA patients, DSG1, C6orf15, SOST, SPRR2A, SERPINB7, MYBPH, and KRT1 may participate in the oncology process. Immunological function and m6a gene expression differed significantly between the two groups. The prognostic model, CNVs, single nucleotide polymorphism (SNP), and drug sensitivity all showed significant gene connections. Conclusions BLCA-associated PyMGs are available to provide guidance in the prognostic and immunological setting and give evidence for the formulation of PyM-related molecularly targeted treatments. PyMGs and their interactions with immune cells in BLCA may serve as therapeutic targets.
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Affiliation(s)
- Zixuan Wu
- School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China
| | - Xiaohuan Li
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zhenchang Gu
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xinhua Xia
- School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China
| | - Jing Yang
- School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China
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Wen G, Li L. FGCNSurv: dually fused graph convolutional network for multi-omics survival prediction. Bioinformatics 2023; 39:btad472. [PMID: 37522887 PMCID: PMC10412406 DOI: 10.1093/bioinformatics/btad472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 05/24/2023] [Accepted: 07/29/2023] [Indexed: 08/01/2023] Open
Abstract
MOTIVATION Survival analysis is an important tool for modeling time-to-event data, e.g. to predict the survival time of patient after a cancer diagnosis or a certain treatment. While deep neural networks work well in standard prediction tasks, it is still unclear how to best utilize these deep models in survival analysis due to the difficulty of modeling right censored data, especially for multi-omics data. Although existing methods have shown the advantage of multi-omics integration in survival prediction, it remains challenging to extract complementary information from different omics and improve the prediction accuracy. RESULTS In this work, we propose a novel multi-omics deep survival prediction approach by dually fused graph convolutional network (GCN) named FGCNSurv. Our FGCNSurv is a complete generative model from multi-omics data to survival outcome of patients, including feature fusion by a factorized bilinear model, graph fusion of multiple graphs, higher-level feature extraction by GCN and survival prediction by a Cox proportional hazard model. The factorized bilinear model enables to capture cross-omics features and quantify complex relations from multi-omics data. By fusing single-omics features and the cross-omics features, and simultaneously fusing multiple graphs from different omics, GCN with the generated dually fused graph could capture higher-level features for computing the survival loss in the Cox-PH model. Comprehensive experimental results on real-world datasets with gene expression and microRNA expression data show that the proposed FGCNSurv method outperforms existing survival prediction methods, and imply its ability to extract complementary information for survival prediction from multi-omics data. AVAILABILITY AND IMPLEMENTATION The codes are freely available at https://github.com/LiminLi-xjtu/FGCNSurv.
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Affiliation(s)
- Gang Wen
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
| | - Limin Li
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
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Li L, Yang W, Jia D, Zheng S, Gao Y, Wang G. Establishment of a N1-methyladenosine-related risk signature for breast carcinoma by bioinformatics analysis and experimental validation. Breast Cancer 2023:10.1007/s12282-023-01458-1. [PMID: 37178414 DOI: 10.1007/s12282-023-01458-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 04/09/2023] [Indexed: 05/15/2023]
Abstract
OBJECTIVES Breast carcinoma (BRCA) has resulted in a huge health burden globally. N1-methyladenosine (m1A) RNA methylation has been proven to play key roles in tumorigenesis. Nevertheless, the function of m1A RNA methylation-related genes in BRCA is indistinct. METHODS The RNA sequencing (RNA-seq), copy-number variation (CNV), single-nucleotide variant (SNV), and clinical data of BRCA were acquired via The Cancer Genome Atlas (TCGA) database. In addition, the GSE20685 dataset, the external validation set, was acquired from the Gene Expression Omnibus (GEO) database. 10 m1A RNA methylation regulators were obtained from the previous literature, and further analyzed through differential expression analysis by rank-sum test, mutation by SNV data, and mutual correlation by Pearson Correlation Analysis. Furthermore, the differentially expressed m1A-related genes were selected through overlapping m1A-related module genes obtained by weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs) in BRCA and DEGs between high- and low- m1A score subgroups. The m1A-related model genes in the risk signature were derived by univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. In addition, a nomogram was built through univariate and multivariate Cox analyses. After that, the immune infiltration between the high- and low-risk groups was investigated through ESTIMATE and CIBERSORT. Finally, the expression trends of model genes in clinical BRCA samples were further confirmed by quantitative real-time PCR (RT‒qPCR). RESULTS Eighty-five differentially expressed m1A-related genes were obtained. Among them, six genes were selected as prognostic biomarkers to build the risk model. The validation results of the risk model showed that its prediction was reliable. In addition, Cox independent prognosis analysis revealed that age, risk score, and stage were independent prognostic factors for BRCA. Moreover, 13 types of immune cells were different between the high- and low-risk groups and the immune checkpoint molecules TIGIT, IDO1, LAG3, ICOS, PDCD1LG2, PDCD1, CD27, and CD274 were significantly different between the two risk groups. Ultimately, RT-qPCR results confirmed that the model genes MEOX1, COL17A1, FREM1, TNN, and SLIT3 were significantly up-regulated in BRCA tissues versus normal tissues. CONCLUSIONS An m1A RNA methylation regulator-related prognostic model was constructed, and a nomogram based on the prognostic model was constructed to provide a theoretical reference for individual counseling and clinical preventive intervention in BRCA.
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Affiliation(s)
- Leilei Li
- Department of Pathology, Kunming Medical University, Kunming, Yunnan, 650500, People's Republic of China
| | - Wenhui Yang
- Department of Digestive Oncology, Cancer Center, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, Shanxi, 030032, People's Republic of China
| | - Daqi Jia
- Department of Pathology, Kunming Medical University, Kunming, Yunnan, 650500, People's Republic of China
| | - Shiqi Zheng
- Department of Pathology, Kunming Medical University, Kunming, Yunnan, 650500, People's Republic of China
| | - Yuzhe Gao
- Department of Breast Surgery, Guizhou Provincial People's Hospital, Guiyang, Guizhou, 550002, People's Republic of China.
| | - Guanghui Wang
- Department of Breast Surgery, Guizhou Provincial People's Hospital, Guiyang, Guizhou, 550002, People's Republic of China.
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Huang T, Fan B, Qiu Y, Zhang R, Wang X, Wang C, Lin H, Yan T, Dong W. Application of DCE-MRI radiomics signature analysis in differentiating molecular subtypes of luminal and non-luminal breast cancer. Front Med (Lausanne) 2023; 10:1140514. [PMID: 37181350 PMCID: PMC10166881 DOI: 10.3389/fmed.2023.1140514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/03/2023] [Indexed: 05/16/2023] Open
Abstract
Background The goal of this study was to develop and validate a radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) preoperatively differentiating luminal and non-luminal molecular subtypes in patients with invasive breast cancer. Methods One hundred and thirty-five invasive breast cancer patients with luminal (n = 78) and non-luminal (n = 57) molecular subtypes were divided into training set (n = 95) and testing set (n = 40) in a 7:3 ratio. Demographics and MRI radiological features were used to construct clinical risk factors. Radiomics signature was constructed by extracting radiomics features from the second phase of DCE-MRI images and radiomics score (rad-score) was calculated. Finally, the prediction performance was evaluated in terms of calibration, discrimination, and clinical usefulness. Results Multivariate logistic regression analysis showed that no clinical risk factors were independent predictors of luminal and non-luminal molecular subtypes in invasive breast cancer patients. Meanwhile, the radiomics signature showed good discrimination in the training set (AUC, 0.86; 95% CI, 0.78-0.93) and the testing set (AUC, 0.80; 95% CI, 0.65-0.95). Conclusion The DCE-MRI radiomics signature is a promising tool to discrimination luminal and non-luminal molecular subtypes in invasive breast cancer patients preoperatively and noninvasively.
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Affiliation(s)
- Ting Huang
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Yingying Qiu
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Rui Zhang
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Xiaolian Wang
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Chaoxiong Wang
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Huashan Lin
- Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha, China
| | - Ting Yan
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Wentao Dong
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
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Zhu JY, He HL, Jiang XC, Bao HW, Chen F. Multimodal ultrasound features of breast cancers: correlation with molecular subtypes. BMC Med Imaging 2023; 23:57. [PMID: 37069528 PMCID: PMC10111677 DOI: 10.1186/s12880-023-00999-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 03/15/2023] [Indexed: 04/19/2023] Open
Abstract
OBJECTIVES To investigate whether multimodal intratumour and peritumour ultrasound features correlate with specific breast cancer molecular subtypes. METHODS From March to November 2021, a total of 85 patients with histologically proven breast cancer underwent B-mode, real-time elastography (RTE), colour Doppler flow imaging (CDFI) and contrast-enhanced ultrasound (CEUS). The time intensity curve (TIC) of CEUS was obtained, and the peak and time to peak (TTP) were analysed. Chi-square and binary logistic regression were used to analyse the connection between multimodal ultrasound imaging features and breast cancer molecular subtype. RESULTS Among 85 breast cancers, the subtypes were as follows: luminal A (36 cases, 42.4%), luminal B (20 cases, 23.5%), human epidermal growth factor receptor-2 positive (HER2+) (16 cases, 18.8%), and triple negative breast cancer (TNBC) (13 cases, 15.3%). Binary logistic regression models showed that RTE (P < 0.001) and CDFI (P = 0.036) were associated with the luminal A cancer subtype (C-index: 0.741), RTE (P = 0.016) and the peak ratio between intratumour and corpus mamma (P = 0.036) were related to the luminal B cancer subtype (C-index: 0.788). The peak ratio between peritumour and intratumour (P = 0.039) was related to the HER2 + cancer subtype (C-index: 0.859), and CDFI (P = 0.002) was associated with the TNBC subtype (C-index: 0.847). CONCLUSIONS Multimodal ultrasound features could be powerful predictors of specific breast cancer molecular subtypes. The intra- and peritumour CEUS features play assignable roles in separating luminal B and HER2 + breast cancer subtypes.
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Affiliation(s)
- Jun-Yan Zhu
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Ultrasound, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Han-Lu He
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Ultrasound, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiao-Chun Jiang
- Department of Ultrasound, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Hai-Wei Bao
- Department of Ultrasound Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fen Chen
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Department of Ultrasound, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.
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Chen C, Wang J, Dong C, Lim D, Feng Z. Development of a risk model to predict prognosis in breast cancer based on cGAS-STING-related genes. Front Genet 2023; 14:1121018. [PMID: 37051596 PMCID: PMC10083333 DOI: 10.3389/fgene.2023.1121018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 03/14/2023] [Indexed: 03/29/2023] Open
Abstract
Background: Breast cancer (BRCA) is regarded as a lethal and aggressive cancer with increasing morbidity and mortality worldwide. cGAS-STING signaling regulates the crosstalk between tumor cells and immune cells in the tumor microenvironment (TME), emerging as an important DNA-damage mechanism. However, cGAS-STING-related genes (CSRGs) have rarely been investigated for their prognostic value in breast cancer patients.Methods: Our study aimed to construct a risk model to predict the survival and prognosis of breast cancer patients. We obtained 1087 breast cancer samples and 179 normal breast tissue samples from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEX) database, 35 immune-related differentially expression genes (DEGs) from cGAS-STING-related genes were systematically assessed. The Cox regression was applied for further selection, and 11 prognostic-related DEGs were used to develop a machine learning-based risk assessment and prognostic model.Results: We successfully developed a risk model to predict the prognostic value of breast cancer patients and its performance acquired effective validation. The results derived from Kaplan-Meier analysis revealed that the low-risk score patients had better overall survival (OS). The nomogram that integrated the risk score and clinical information was established and had good validity in predicting the overall survival of breast cancer patients. Significant correlations were observed between the risk score and tumor-infiltrating immune cells, immune checkpoints and the response to immunotherapy. The cGAS-STING-related genes risk score was also relevant to a series of clinic prognostic indicators such as tumor staging, molecular subtype, tumor recurrence, and drug therapeutic sensibility in breast cancer patients.Conclusion: cGAS-STING-related genes risk model provides a new credible risk stratification method to improve the clinical prognostic assessment for breast cancer.
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Affiliation(s)
- Chen Chen
- Department of Occupational Health and Occupational Medicine, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Junxiao Wang
- Department of Occupational Health and Occupational Medicine, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Chao Dong
- Department of Occupational Health and Occupational Medicine, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - David Lim
- Translational Health Research Institute, School of Health Sciences, Western Sydney University, Campbelltown, NSW, Australia
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Zhihui Feng
- Department of Occupational Health and Occupational Medicine, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- *Correspondence: Zhihui Feng,
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Yang WX, Gao HW, Cui JB, Zhang AA, Wang FF, Xie JQ, Lu MH, You CG. Development and validation of a coagulation-related genes prognostic model for hepatocellular carcinoma. BMC Bioinformatics 2023; 24:89. [PMID: 36894886 PMCID: PMC9996845 DOI: 10.1186/s12859-023-05220-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 03/03/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) has a high incidence and mortality worldwide, which seriously threatens people's physical and mental health. Coagulation is closely related to the occurrence and development of HCC. Whether coagulation-related genes (CRGs) can be used as prognostic markers for HCC remains to be investigated. METHODS Firstly, we identified differentially expressed coagulation-related genes of HCC and control samples in the datasets GSE54236, GSE102079, TCGA-LIHC, and Genecards database. Then, univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis were used to determine the key CRGs and establish the coagulation-related risk score (CRRS) prognostic model in the TCGA-LIHC dataset. The predictive capability of the CRRS model was evaluated by Kaplan-Meier survival analysis and ROC analysis. External validation was performed in the ICGC-LIRI-JP dataset. Besides, combining risk score and age, gender, grade, and stage, a nomogram was constructed to quantify the survival probability. We further analyzed the correlation between risk score and functional enrichment, pathway, and tumor immune microenvironment. RESULTS We identified 5 key CRGs (FLVCR1, CENPE, LCAT, CYP2C9, and NQO1) and constructed the CRRS prognostic model. The overall survival (OS) of the high-risk group was shorter than that of the low-risk group. The AUC values for 1 -, 3 -, and 5-year OS in the TCGA dataset were 0.769, 0.691, and 0.674, respectively. The Cox analysis showed that CRRS was an independent prognostic factor for HCC. A nomogram established with risk score, age, gender, grade, and stage, has a better prognostic value for HCC patients. In the high-risk group, CD4+T cells memory resting, NK cells activated, and B cells naive were significantly lower. The expression levels of immune checkpoint genes in the high-risk group were generally higher than that in the low-risk group. CONCLUSIONS The CRRS model has reliable predictive value for the prognosis of HCC patients.
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Affiliation(s)
- Wan-Xia Yang
- Laboratory Medicine Center, Lanzhou University Second Hospital, Lanzhou, China
| | - Hong-Wei Gao
- Laboratory Medicine Center, Lanzhou University Second Hospital, Lanzhou, China
| | - Jia-Bo Cui
- Laboratory Medicine Center, Lanzhou University Second Hospital, Lanzhou, China
| | - An-An Zhang
- Laboratory Medicine Center, Lanzhou University Second Hospital, Lanzhou, China
| | - Fang-Fang Wang
- Laboratory Medicine Center, Lanzhou University Second Hospital, Lanzhou, China
| | - Jian-Qin Xie
- Anesthesiology Department, Lanzhou University Second Hospital, Lanzhou, China
| | - Ming-Hua Lu
- Laboratory Medicine Center, Lanzhou University Second Hospital, Lanzhou, China
| | - Chong-Ge You
- Laboratory Medicine Center, Lanzhou University Second Hospital, Lanzhou, China.
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Ji Y, Zhao Z, Cheng Y, Bu W, Zhao X, Luo Y, Tang J. Special transcriptome landscape and molecular prognostic signature of non-smoking head and neck cancer patients. Funct Integr Genomics 2023; 23:79. [PMID: 36882550 DOI: 10.1007/s10142-023-01002-6] [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: 01/27/2023] [Revised: 02/22/2023] [Accepted: 02/25/2023] [Indexed: 03/09/2023]
Abstract
As a well-known behavioral risk factor for human health, smoking is involved in carcinogenesis, tumor progression, and therapeutic interventions of head and neck squamous cell carcinoma (HNSCC). The stratification of disease subtypes according to tobacco use is expressively needed for HNSCC precision therapy. High-throughput transcriptome profiling by RNA sequencing (RNA-seq) from The Cancer Genome Atlas (TCGA) was collected and collated for differential expression analysis and pathway enrichment analysis to characterize the molecular landscape for non-smoking HNSCC patients. Molecular prognostic signatures specific to non-smoking HNSCC patients were identified by the least absolute shrinkage and selection operator (LASSO) analysis and were then verified via internal and external validation cohorts. While proceeding to immune cell infiltration and after drug sensitivity analysis was further carried out, a proprietary nomogram was finally developed for their respective clinical applications. In what it relates to the non-smoking cohort, the enrichment analysis pointed to human papillomavirus (HPV) infection and PI3K-Akt signaling pathway, with the prognostic signature consisting of another ten prognostic genes (COL22A1, ADIPOQ, RAG1, GREM1, APBA2, SPINK9, SPP1, ARMC4, C6, and F2RL2). These signatures showed to be independent factors, and the related nomograms were, thus, constructed for their further and respective clinical applications. While the molecular landscapes and proprietary prognostic signature were characterized based on non-smoking HNSCC patients, a clinical nomogram was constructed to provide better HNSCC patient classification and guide treatment for non-smoking HNSCC patients. Nonetheless, there are still significant challenges in the recognition, diagnosis, treatment, and understanding of the potentially efficient mechanisms of HNSCC with no tobacco use.
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Affiliation(s)
- Yaya Ji
- School of Medicine, Nantong University, Nantong, 226001, China
| | - Zixuan Zhao
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong, 226019, China
| | - Yulan Cheng
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong, 226019, China
| | - Wenxia Bu
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong, 226019, China
| | - Xinyuan Zhao
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong, 226019, China
| | - Yonghua Luo
- Nantong Fourth People's Hospital, Nantong, 226000, China.
| | - Juan Tang
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong, 226019, China.
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Di X, Xiang L, Jian Z. YAP-mediated mechanotransduction in urinary bladder remodeling: Based on RNA-seq and CUT&Tag. Front Genet 2023; 14:1106927. [PMID: 36741311 PMCID: PMC9895788 DOI: 10.3389/fgene.2023.1106927] [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: 11/24/2022] [Accepted: 01/10/2023] [Indexed: 01/22/2023] Open
Abstract
Yes-associated protein (YAP) is an important transcriptional coactivator binding to transcriptional factors that engage in many downstream gene transcription. Partial bladder outlet obstruction (pBOO) causes a massive burden to patients and finally leads to bladder fibrosis. Several cell types engage in the pBOO pathological process, including urothelial cells, smooth muscle cells, and fibroblasts. To clarify the function of YAP in bladder fibrosis, we performed the RNA-seq and CUT&Tag of the bladder smooth muscle cell to analyze the YAP ablation of human bladder smooth muscle cells (hBdSMCs) and immunoprecipitation of YAP. 141 differentially expressed genes (DEGs) were identified through RNA-seq between YAP-knockdown and nature control. After matching with the results of CUT&Tag, 36 genes were regulated directly by YAP. Then we identified the hub genes in the DEGs, including CDCA5, CENPA, DTL, NCAPH, and NEIL3, that contribute to cell proliferation. Thus, our study provides a regulatory network of YAP in smooth muscle proliferation. The possible effects of YAP on hBdSMC might be a vital target for pBOO-associated bladder fibrosis.
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Affiliation(s)
- Xingpeng Di
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Liyuan Xiang
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, Sichuan, China,Department of Clinical Research Management, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhongyu Jian
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, Sichuan, China,*Correspondence: Zhongyu Jian,
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Construction and characterization of a cuproptosis- and immune checkpoint-based LncRNAs signature for breast cancer risk stratification. Breast Cancer 2023; 30:393-411. [PMID: 36662399 DOI: 10.1007/s12282-023-01434-9] [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: 06/24/2022] [Accepted: 01/06/2023] [Indexed: 01/21/2023]
Abstract
BACKGROUND Cuproptosis is the most recently identified form of cell death, and copper homeostasis is an important cancer therapeutic target. However, the therapeutic benefits of cuproptosis-targeted treatment in BRCA remain undetermined. This study utilized LncRNAs linked to cuproptosis genes and immune checkpoint genes to generate a BRCA predictive signature. METHODS We screened a population of LncRNAs that correlated with both cuproptosis genes and immune checkpoint genes and used ten of these LncRNAs to construct a prognosis-predictive signature. We then validated and proved the efficacy of the signature in predicting the prognosis of BRCA patients. We also unraveled the relationship between the signature and the immunological milieu, immune function, and susceptibility to chemotherapy. RESULTS The signature derived from the ten cuproptosis- and immune-related prognostic LncRNAs (CuImP-LncRNAs) can be implied to categorize patients into two groups, including the high- and low-risk groups. The value of the signature was validated, and the risk score was verified as an independent prognostic indicator. The TIME and TMB distribution patterns and chemosensitivity were depicted in the high- and low-risk groups, respectively. Patients of the high-risk group with a suppressive immunological intratumor context were more sensitive to a broad range of antitumor agents. In contrast, low-risk individuals with active immune function responded more favorably to immunotherapy. CONCLUSION Our findings provided a novel and effective model for predicting BRCA prognosis and the propensity to different treatment modalities, thus contributing to the optimization of personalized BRCA therapy in the future.
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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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Xu QT, Wang ZW, Cai MY, Wei JF, Ding Q. A novel cuproptosis-related prognostic 2-lncRNAs signature in breast cancer. Front Pharmacol 2023; 13:1115608. [PMID: 36699089 PMCID: PMC9868634 DOI: 10.3389/fphar.2022.1115608] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 12/21/2022] [Indexed: 01/11/2023] Open
Abstract
Background: Cuproptosis, a newly defined regulated form of cell death, is mediated by the accumulation of copper ions in cells and related to protein lipoacylation. Seven genes have been reported as key genes of cuproptosis phenotype. Cuproptosis may be developed by subsequent research as a target to treat cancer, such as breast cancer. Long-noncoding RNA (lncRNA) has been proved to play a vital role in regulating the biological process of breast cancer. However, the role of lncRNAs in cuproptosis is poorly studied. Methods: Based on TCGA (The Cancer Genome Atlas) database and integrated several R packages, we screened out 153 cuproptosis-related lncRNAs and constructed a novel cuproptosis-related prognostic 2-lncRNAs signature (BCCuS) in breast cancer and then verified. By using pRRophetic package and machine learning, 72 anticancer drugs, significantly related to the model, were screened out. qPCR was used to detect the differentially expression of two model lncRNAs and seven cuproptosis genes between 10 pairs of breast cancer tissue samples and adjacent samples. Results: We constructed a novel cuproptosis-related prognostic 2-lncRNAs (USP2-AS1, NIFK-AS1) signature (BCCuS) in breast cancer. Univariate COX analysis (p < .001) and multivariate COX analysis (p < .001) validated that BCCuS was an independent prognostic factor for breast cancer. Overall survival Kaplan Meier-plotter, ROC curve and Risk Plot validated the prognostic value of BCCuS both in test set and verification set. Nomogram and C-index proved that BCCuS has strong correlation with clinical decision-making. BCCuS still maintain inspection efficiency when patients were splitting into Stage I-II (p = .024) and Stage III-IV (p = .003) breast cancer. BCCuS-high group and BCCuS-low group showed significant differences in gene mutation frequency, immune function, TIDE (tumor immune dysfunction and exclusion) score and other phenotypes. TMB (tumor mutation burden)-high along with BCCuS-high group had the lowest Survival probability (p = .005). 36 anticancer drugs whose sensitivity (IC50) was significantly related to the model were screened out using pRRophetic package. qPCR results showed that two model lncRNAs (USP2-AS1, NIFK-AS1) and three Cuproptosis genes (FDX1, PDHA1, DLAT) expressed differently between 10 pairs of breast cancer tissue samples and adjacent samples. Conclusion: The current study reveals that cuproptosis-related prognostic 2-lncRNAs signature (BCCuS) may be useful in predicting the prognosis, biological characteristics, and appropriate treatment of breast cancer patients.
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Affiliation(s)
- Qi-Tong Xu
- Jiangsu Breast Disease Center, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Zi-Wen Wang
- Jiangsu Breast Disease Center, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Meng-Yuan Cai
- Jiangsu Breast Disease Center, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Ji-Fu Wei
- Department of Pharmacy, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China,*Correspondence: Ji-Fu Wei, ; Qiang Ding,
| | - Qiang Ding
- Jiangsu Breast Disease Center, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China,*Correspondence: Ji-Fu Wei, ; Qiang Ding,
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Wolf J, Reinhard T, Hajdu RI, Schlunck G, Auw-Haedrich C, Lange C. Transcriptional Profiling Identifies Prognostic Gene Signatures for Conjunctival Extranodal Marginal Zone Lymphoma. Biomolecules 2023; 13:115. [PMID: 36671500 PMCID: PMC9855408 DOI: 10.3390/biom13010115] [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: 12/02/2022] [Revised: 12/30/2022] [Accepted: 01/02/2023] [Indexed: 01/11/2023] Open
Abstract
This study characterizes the transcriptional profile and the cellular tumor microenvironment of conjunctival extranodal marginal zone lymphoma (EMZL) and identifies prognostically relevant biomarkers. Ten formalin-fixed and paraffin-embedded conjunctival EMZL and eight healthy conjunctival specimens were analyzed by Massive Analysis of cDNA Ends (MACE) RNA sequencing. The 3417 upregulated genes in conjunctival EMZL were involved in processes such as B cell proliferation and Rac protein signaling, whereas the 1188 downregulated genes contributed most significantly to oxidative phosphorylation and UV protection. The tumor microenvironment, as determined by deconvolution analysis, was mainly composed of multiple B cell subtypes which reflects the tumor's B cell lineage. However, several T cell types, including T helper 2 cells and regulatory T cells, as well as innate immune cell types, such as anti-inflammatory macrophages and plasmacytoid dendritic cells, were also strongly enriched in conjunctival EMZL. A 13-biomarker prognostic panel, including S100A8 and S100A9, classified ocular and extraocular tumor recurrence, exceeded prognostic accuracy of Ann Arbor and American Joint Committee on Cancer (AJCC) staging, and demonstrated prognostic value for patient survival in 21 different cancer types in a database of 12,332 tumor patients. These findings may lead to new options of targeted therapy and may improve prognostic prediction for conjunctival EMZL.
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Affiliation(s)
- Julian Wolf
- Eye Center, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
- Molecular Surgery Laboratory, Stanford University, Palo Alto, CA 94304, USA
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA 94304, USA
| | - Thomas Reinhard
- Eye Center, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Rozina Ida Hajdu
- Eye Center, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
- Department of Ophthalmology, Semmelweis University, 1085 Budapest, Hungary
| | - Günther Schlunck
- Eye Center, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Claudia Auw-Haedrich
- Eye Center, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Clemens Lange
- Ophtha-Lab, Department of Ophthalmology, St. Franziskus Hospital, 48145 Münster, Germany
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Wu X, Shi Y, Wang M, Li A. CAMR: cross-aligned multimodal representation learning for cancer survival prediction. Bioinformatics 2023; 39:btad025. [PMID: 36637188 PMCID: PMC9857974 DOI: 10.1093/bioinformatics/btad025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 12/10/2022] [Accepted: 01/12/2023] [Indexed: 01/14/2023] Open
Abstract
MOTIVATION Accurately predicting cancer survival is crucial for helping clinicians to plan appropriate treatments, which largely improves the life quality of cancer patients and spares the related medical costs. Recent advances in survival prediction methods suggest that integrating complementary information from different modalities, e.g. histopathological images and genomic data, plays a key role in enhancing predictive performance. Despite promising results obtained by existing multimodal methods, the disparate and heterogeneous characteristics of multimodal data cause the so-called modality gap problem, which brings in dramatically diverse modality representations in feature space. Consequently, detrimental modality gaps make it difficult for comprehensive integration of multimodal information via representation learning and therefore pose a great challenge to further improvements of cancer survival prediction. RESULTS To solve the above problems, we propose a novel method called cross-aligned multimodal representation learning (CAMR), which generates both modality-invariant and -specific representations for more accurate cancer survival prediction. Specifically, a cross-modality representation alignment learning network is introduced to reduce modality gaps by effectively learning modality-invariant representations in a common subspace, which is achieved by aligning the distributions of different modality representations through adversarial training. Besides, we adopt a cross-modality fusion module to fuse modality-invariant representations into a unified cross-modality representation for each patient. Meanwhile, CAMR learns modality-specific representations which complement modality-invariant representations and therefore provides a holistic view of the multimodal data for cancer survival prediction. Comprehensive experiment results demonstrate that CAMR can successfully narrow modality gaps and consistently yields better performance than other survival prediction methods using multimodal data. AVAILABILITY AND IMPLEMENTATION CAMR is freely available at https://github.com/wxq-ustc/CAMR. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xingqi Wu
- School of Information Science and Technology, University of Science and Technology of China, Hefei AH230027, China
| | - Yi Shi
- School of Information Science and Technology, University of Science and Technology of China, Hefei AH230027, China
| | - Minghui Wang
- School of Information Science and Technology, University of Science and Technology of China, Hefei AH230027, China
| | - Ao Li
- School of Information Science and Technology, University of Science and Technology of China, Hefei AH230027, China
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Fan Y, S Chan A, Zhu J, Yi Leung S, Fan X. A Bayesian model for identifying cancer subtypes from paired methylation profiles. Brief Bioinform 2022; 24:6961790. [PMID: 36575828 PMCID: PMC9851340 DOI: 10.1093/bib/bbac568] [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: 07/29/2022] [Revised: 10/19/2022] [Accepted: 11/22/2022] [Indexed: 12/29/2022] Open
Abstract
Aberrant DNA methylation is the most common molecular lesion that is crucial for the occurrence and development of cancer, but has thus far been underappreciated as a clinical tool for cancer classification, diagnosis or as a guide for therapeutic decisions. Partly, this has been due to a lack of proven algorithms that can use methylation data to stratify patients into clinically relevant risk groups and subtypes that are of prognostic importance. Here, we proposed a novel Bayesian model to capture the methylation signatures of different subtypes from paired normal and tumor methylation array data. Application of our model to synthetic and empirical data showed high clustering accuracy, and was able to identify the possible epigenetic cause of a cancer subtype.
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Affiliation(s)
- Yetian Fan
- School of Mathematics and Statistics, Liaoning University, Shenyang, 110036, China,Department of Statistics, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong SAR, China
| | - April S Chan
- Department of Pathology, School of Clinical Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Jun Zhu
- Sema4, Stamford, CT, 06902, USA,Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Suet Yi Leung
- Department of Pathology, School of Clinical Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Xiaodan Fan
- Corresponding author: Xiaodan Fan, Department of Statistics, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong SAR, China. E-mail:
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Yuan M, Xu J, Cao S, Sun S. DDX1 is a prognostic biomarker and correlates with immune infiltrations in hepatocellular carcinoma. BMC Immunol 2022; 23:59. [PMID: 36451087 PMCID: PMC9710136 DOI: 10.1186/s12865-022-00533-0] [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: 08/03/2022] [Accepted: 11/14/2022] [Indexed: 12/05/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is one of the leading lethal malignant tumors worldwide. DEAD-box (DDX) family helicases are implicated in numerous human cancers. However, the role of DDX1 in HCC has not yet been fully elucidated. We downloaded gene expression data and clinical information data of HCC from The Cancer Genome Atlas and International Cancer Genome Consortium (ICGC) database and conducted subsequent analyses using the R package and online portal. The results revealed that HCC tissues had higher DDX1 expression compared with either paired or unpaired normal tissues. The increased DDX1 expression was closely related to the advanced pathological grade and histologic grade of HCC. Further analysis suggested that patients with high DDX1 expression contributed to poor prognosis The Cox regression analysis revealed that the expression level of DDX1 was an independent prognostic factor for HCC. In addition, an ICGC cohort was used for external validation. The cBio-Portal, MethSurv, and UALCAN database were used for evaluating the genomic mechanism. Moreover, the Tumor Immune Estimation Resource dataset and QUANTISEQ algorithm revealed that DDX1 expression positively correlates with immune infiltrating cells. We also identified the DDX1-related differentially expressed genes (DEGs) and explored their biological functions by GO, KEGG, and GSEA analyses, which indicated that DDX1 may regulate the progression of HCC. In general, increased DDX1 expression predicts a poor prognosis and drives the progression of HCC.
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Affiliation(s)
- Mengping Yuan
- grid.417384.d0000 0004 1764 2632Department of Gastroenterology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, 325000 People’s Republic of China
| | - Jinyong Xu
- Department of Pathology, Shenzhen Hyzen Hospital, Shenzhen, 518038 People’s Republic of China
| | - Shuguang Cao
- grid.417384.d0000 0004 1764 2632Department of Gastroenterology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, 325000 People’s Republic of China
| | - Shuangshuang Sun
- grid.417384.d0000 0004 1764 2632Department of Oncology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, 325000 People’s Republic of China
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Li X, Jonnagaddala J, Cen M, Zhang H, Xu S. Colorectal Cancer Survival Prediction Using Deep Distribution Based Multiple-Instance Learning. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1669. [PMID: 36421523 PMCID: PMC9689861 DOI: 10.3390/e24111669] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/09/2022] [Accepted: 11/12/2022] [Indexed: 06/16/2023]
Abstract
Most deep-learning algorithms that use Hematoxylin- and Eosin-stained whole slide images (WSIs) to predict cancer survival incorporate image patches either with the highest scores or a combination of both the highest and lowest scores. In this study, we hypothesize that incorporating wholistic patch information can predict colorectal cancer (CRC) cancer survival more accurately. As such, we developed a distribution-based multiple-instance survival learning algorithm (DeepDisMISL) to validate this hypothesis on two large international CRC WSIs datasets called MCO CRC and TCGA COAD-READ. Our results suggest that combining patches that are scored based on percentile distributions together with the patches that are scored as highest and lowest drastically improves the performance of CRC survival prediction. Including multiple neighborhood instances around each selected distribution location (e.g., percentiles) could further improve the prediction. DeepDisMISL demonstrated superior predictive ability compared to other recently published, state-of-the-art algorithms. Furthermore, DeepDisMISL is interpretable and can assist clinicians in understanding the relationship between cancer morphological phenotypes and a patient's cancer survival risk.
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Affiliation(s)
- Xingyu Li
- School of Management, University of Science and Technology of China, Hefei 230026, China
| | - Jitendra Jonnagaddala
- School of Population Health, University of New South Wales, Sydney, NSW 2052, Australia
| | - Min Cen
- School of Management, University of Science and Technology of China, Hefei 230026, China
| | - Hong Zhang
- School of Management, University of Science and Technology of China, Hefei 230026, China
| | - Steven Xu
- Clinical Pharmacology and Quantitative Science, Genmab US, Inc., Princeton, NJ 08540, USA
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Tao Y, Gao C, Qian D, Cao D, Han L, Yang L. Regulatory mechanism of fibrosis-related genes in patients with heart failure. Front Genet 2022; 13:1032572. [DOI: 10.3389/fgene.2022.1032572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/06/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Heart failure (HF) is a complex clinical syndrome characterized by the inability to match cardiac output with metabolic needs. Research on regulatory mechanism of fibrosis-related genes in patients with HF is very limited. In order to understand the mechanism of fibrosis in the development and progression of HF, fibrosis -related hub genes in HF are screened and verified.Methods: RNA sequencing data was obtained from the Gene Expression Omnibus (GEO) cohorts to identify differentially expressed genes (DEGs). Thereafter, fibrosis-related genes were obtained from the GSEA database and that associated with HF were screened out. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis was carried out to analyze the biological function of fibrosis-related DEGs. The protein-protein interaction (PPI) network of hub genes was constructed via the STRING database. Moreover, the diagnostic value of hub genes for HF was confirmed using ROC curves and expression analysis. Finally, quantitative real time PCR was used to detect the expression levels of mRNAs.Results: A total of 3, 469 DEGs were identified closely related to HF, and 1, 187 fibrosis-related DEGs were obtained and analyzed for GO and KEGG enrichment. The enrichment results of fibrosis-related DEGs were consistent with that of DEGs. A total of 10 hub genes (PPARG, KRAS, JUN, IL10, TLR4, STAT3, CXCL8, CCL2, IL6, IL1β) were selected via the PPI network. Receiver operating characteristic curve analysis was estimated in the test cohort, and 6 genes (PPARG, KRAS, JUN, IL10, TLR4, STAT3) with AUC more than 0.7 were identified as diagnosis genes. Moreover, miRNA-mRNA and TF-mRNA regulatory networks were constructed. Finally, quantitative real time PCR revealed these 6 genes may be used as the potential diagnostic biomarkers of HF.Conclusion: In this study, 10 fibrosis-related hub genes in the HF were identified and 6 of them were demonstrated as potential diagnostic biomarkers for HF.
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Wang X, Huang Y, Li S, Zhang H. Integrated machine learning methods identify FNDC3B as a potential prognostic biomarker and correlated with immune infiltrates in glioma. Front Immunol 2022; 13:1027154. [PMID: 36275754 PMCID: PMC9582524 DOI: 10.3389/fimmu.2022.1027154] [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: 08/24/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Background Recent discoveries have revealed that fibronectin type III domain containing 3B (FNDC3B) acts as an oncogene in various cancers; however, its role in glioma remains unclear. Methods In this study, we comprehensively investigated the expression, prognostic value, and immune significance of FNDC3B in glioma using several databases and a variety of machine learning algorithms. RNA expression data and clinical information of 529 patients from the Cancer Genome Atlas (TCGA) and 1319 patients from Chinese Glioma Genome Atlas (CGGA) databases were downloaded for further investigation. To evaluate whether FNDC3B expression can predict clinical prognosis of glioma, we constructed a clinical nomogram to estimate long-term survival probabilities. The predicted nomogram was validated by CGGA cohorts. Differentially expressed genes (DEGs) were detected by the Wilcoxon test based on the TCGA-LGG dataset and the weighted gene co-expression network analysis (WGCNA) was implemented to identify the significant module associated with the expression level of FNDC3B. Furthermore, we investigated the correlation between FNDC3B with cancer immune infiltrates using TISIDB, ESTIMATE, and CIBERSORTx. Results Higher FNDC3B expression displayed a remarkably worse overall survival and the expression level of FNDC3B was an independent prognostic indicator for patients with glioma. Based on TCGA LGG dataset, a co-expression network was established and the hub genes were identified. FNDC3B expression was positively correlated to the tumor-infiltrating lymphocytes and immune infiltration score, and high FNDC3B expression was accompanied by the increased expression of B7-H3, PD-L1, TIM-3, PD-1, and CTLA-4. Moreover, expression of FNDC3B was significantly associated with infiltrating levels of several types of immune cells and most of their gene markers in glioma. Conclusion This study demonstrated that FNDC3B may be involved in the occurrence and development of glioma and can be regarded as a promising prognostic and immunotherapeutic biomarker for the treatment of glioma.
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Affiliation(s)
- Xiao Wang
- Department of Nephrology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Yeping Huang
- Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Shanshan Li
- Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Hong Zhang
- Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
- *Correspondence: Hong Zhang,
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Yip HF, Chowdhury D, Wang K, Liu Y, Gao Y, Lan L, Zheng C, Guan D, Lam KF, Zhu H, Tai X, Lu A. ReDisX, a machine learning approach, rationalizes rheumatoid arthritis and coronary artery disease patients uniquely upon identifying subpopulation differentiation markers from their genomic data. Front Med (Lausanne) 2022; 9:931860. [PMID: 36072953 PMCID: PMC9441882 DOI: 10.3389/fmed.2022.931860] [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: 05/03/2022] [Accepted: 07/28/2022] [Indexed: 11/29/2022] Open
Abstract
Diseases originate at the molecular-genetic layer, manifest through altered biochemical homeostasis, and develop symptoms later. Hence, symptomatic diagnosis is inadequate to explain the underlying molecular-genetic abnormality and individual genomic disparities. The current trends include molecular-genetic information relying on algorithms to recognize the disease subtypes through gene expressions. Despite their disposition toward disease-specific heterogeneity and cross-disease homogeneity, a gap still exists in describing the extent of homogeneity within the heterogeneous subpopulation of different diseases. They are limited to obtaining the holistic sense of the whole genome-based diagnosis resulting in inaccurate diagnosis and subsequent management. Addressing those ambiguities, our proposed framework, ReDisX, introduces a unique classification system for the patients based on their genomic signatures. In this study, it is a scalable machine learning algorithm deployed to re-categorize the patients with rheumatoid arthritis and coronary artery disease. It reveals heterogeneous subpopulations within a disease and homogenous subpopulations across different diseases. Besides, it identifies granzyme B (GZMB) as a subpopulation-differentiation marker that plausibly serves as a prominent indicator for GZMB-targeted drug repurposing. The ReDisX framework offers a novel strategy to redefine disease diagnosis through characterizing personalized genomic signatures. It may rejuvenate the landscape of precision and personalized diagnosis and a clue to drug repurposing.
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Affiliation(s)
- Hiu F. Yip
- Computational Medicine Laboratory, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
- Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
- Department of Mathematics, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
| | - Debajyoti Chowdhury
- Computational Medicine Laboratory, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
- Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
| | - Kexin Wang
- National Key Clinical Specialty, Engineering Technology Research Center of Education Ministry of China, Guangzhou, China
- Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Neurosurgery Institute, Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yujie Liu
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Yao Gao
- Department of Psychiatry, First Hospital, First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Liang Lan
- Department of Communication Studies, School of Communication, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
| | - Chaochao Zheng
- Department of Mathematics, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
| | - Daogang Guan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Guangzhou, China
| | - Kei F. Lam
- Department of Mathematics, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
| | - Hailong Zhu
- Computational Medicine Laboratory, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
- Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
| | - Xuecheng Tai
- Department of Mathematics, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
| | - Aiping Lu
- Computational Medicine Laboratory, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
- Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China
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da Luz FAC, Araújo BJ, de Araújo RA. The current staging and classification systems of breast cancer and their pitfalls: Is it possible to integrate the complexity of this neoplasm into a unified staging system? Crit Rev Oncol Hematol 2022; 178:103781. [PMID: 35953011 DOI: 10.1016/j.critrevonc.2022.103781] [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: 03/30/2022] [Revised: 06/21/2022] [Accepted: 08/06/2022] [Indexed: 11/29/2022] Open
Abstract
Breast cancer is one of the leading causes of cancer death in women worldwide due to its variable aggressiveness and high propensity to develop distant metastases. The staging can be performed clinically or pathologically, generating the stage stratification by the TNM (T - tumor size; N- lymph node metastasis; M - distant organ metastasis) system. However, cancers with virtually identical TNM characteristics can present highly contrasting behaviors due to the divergence of molecular profiles. This review focuses on the histopathological nuances and molecular understanding of breast cancer through the profiling of gene and protein expression, culminating in improvements promoted by the integration of this information into the traditional staging system. As a culminating point, it will highlight predictive statistical tools for genomic risks and decision algorithms as a possible solution to integrate the various systems because they have the potential to reduce the indications for such tests, serving as a funnel in association with staging and previous classification.
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Affiliation(s)
- Felipe Andrés Cordero da Luz
- Center for Cancer Prevention and Research, Uberlandia Cancer Hospital, Av Amazonas nº 1996, Umuarama, Uberlândia, Minas Gerais, MG 38405-302, Brazil
| | - Breno Jeha Araújo
- São Paulo State Cancer Institute of the Medical School of the University of São Paulo, Av. Dr. Arnaldo 251, São Paulo, São Paulo, SP 01246-000, Brazil
| | - Rogério Agenor de Araújo
- Medical Faculty, Federal University of Uberlandia, Av Pará nº 1720, Bloco 2U, Umuarama, Uberlândia, Minas Gerais, MG 38400-902, Brazil.
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Basseville A, Cordier C, Ben Azzouz F, Gouraud W, Lasla H, Panloup F, Campone M, Jézéquel P. Brain Neural Progenitors are New Predictive Biomarkers for Breast Cancer Hormonotherapy. CANCER RESEARCH COMMUNICATIONS 2022; 2:857-869. [PMID: 36923306 PMCID: PMC10010318 DOI: 10.1158/2767-9764.crc-21-0090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 03/28/2022] [Accepted: 07/27/2022] [Indexed: 11/16/2022]
Abstract
Heterogeneity of the tumor microenvironment (TME) is one of the major causes of treatment resistance in breast cancer. Among TME components, nervous system role in clinical outcome has been underestimated. Identifying neuronal signatures associated with treatment response will help to characterize neuronal influence on tumor progression and identify new treatment targets. The search for hormonotherapy-predictive biomarkers was implemented by supervised machine learning (ML) analysis on merged transcriptomics datasets from public databases. ML-derived genes were investigated by pathway enrichment analysis, and potential gene signatures were curated by removing the variables that were not strictly nervous system specific. The predictive and prognostic abilities of the generated signatures were examined by Cox models, in the initial cohort and seven external cohorts. Generated signature performances were compared with 14 other published signatures, in both the initial and external cohorts. Underlying biological mechanisms were explored using deconvolution tools (CIBERSORTx and xCell). Our pipeline generated two nervous system-related signatures of 24 genes and 97 genes (NervSign24 and NervSign97). These signatures were prognostic and hormonotherapy-predictive, but not chemotherapy-predictive. When comparing their predictive performance with 14 published risk signatures in six hormonotherapy-treated cohorts, NervSign97 and NervSign24 were the two best performers. Pathway enrichment score and deconvolution analysis identified brain neural progenitor presence and perineural invasion as nervous system-related mechanisms positively associated with NervSign97 and poor clinical prognosis in hormonotherapy-treated patients. Transcriptomic profiling has identified two nervous system-related signatures that were validated in clinical samples as hormonotherapy-predictive signatures, meriting further exploration of neuronal component involvement in tumor progression. Significance The development of personalized and precision medicine is the future of cancer therapy. With only two gene expression signatures approved by FDA for breast cancer, we are in need of new ones that can reliably stratify patients for optimal treatment. This study provides two hormonotherapy-predictive and prognostic signatures that are related to nervous system in TME. It highlights tumor neuronal components as potential new targets for breast cancer therapy.
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Affiliation(s)
- Agnes Basseville
- Omics Data Science Unit, Institut de Cancérologie de l'Ouest (ICO), Angers-Nantes, France.,SIRIC ILIAD, Angers-Nantes, France
| | - Chiara Cordier
- Omics Data Science Unit, Institut de Cancérologie de l'Ouest (ICO), Angers-Nantes, France.,SIRIC ILIAD, Angers-Nantes, France.,Laboratoire Angevin de Recherche en Mathématiques (LAREMA), Université d'Angers, Angers, France
| | - Fadoua Ben Azzouz
- Omics Data Science Unit, Institut de Cancérologie de l'Ouest (ICO), Angers-Nantes, France.,SIRIC ILIAD, Angers-Nantes, France
| | - Wilfried Gouraud
- Omics Data Science Unit, Institut de Cancérologie de l'Ouest (ICO), Angers-Nantes, France.,SIRIC ILIAD, Angers-Nantes, France
| | - Hamza Lasla
- Omics Data Science Unit, Institut de Cancérologie de l'Ouest (ICO), Angers-Nantes, France.,SIRIC ILIAD, Angers-Nantes, France
| | - Fabien Panloup
- Laboratoire Angevin de Recherche en Mathématiques (LAREMA), Université d'Angers, Angers, France
| | - Mario Campone
- SIRIC ILIAD, Angers-Nantes, France.,Institut de Cancérologie de l'Ouest (ICO), Angers-Nantes, France
| | - Pascal Jézéquel
- Omics Data Science Unit, Institut de Cancérologie de l'Ouest (ICO), Angers-Nantes, France.,SIRIC ILIAD, Angers-Nantes, France.,CRCI2NA, Inserm UMR1307/CNRS UMR 6075/Université de Nantes, Nantes, France
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Shen Y, Zhou H, Dong S, Dong W, Zhang L. Smoking patients with laryngeal cancer screened with a novel immunogenomics-based prognostic signature. Front Genet 2022; 13:961764. [PMID: 35910213 PMCID: PMC9333188 DOI: 10.3389/fgene.2022.961764] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 06/28/2022] [Indexed: 11/24/2022] Open
Abstract
The immune system greatly affects the prognosis of various malignancies. Studies on differentially expressed immune-related genes (IRGs) in the immune microenvironment of laryngeal squamous cell carcinoma (LSCC) have rarely been reported. In this paper, the prognostic potentials of IRGs were explored in LSCC patients with smoking use. The RNA-seq data containing IRGs and corresponding clinical information of smoking LSCC patients was obtained from The Cancer Genome Atlas (TCGA). Differentially expressed IRGs were identified and functional enrichment analysis was used to reveal the pathway of IRGs. Then, IRGs with prognostic potentials in smoking LSCC patients were screened out by univariate Cox regression analysis. Finally, multivariate Cox regression analysis was conducted to assess the prognostic signature of 5 IRGs after adjustment of clinical factors and patients were classified into two subgroups based on different IRGs expression. The prognostic capacity of the model was verified by another independent cohort from Gene Expression Omnibus (GEO) database. Nomogram including the prognostic signature was established and shown some clinical net benefit. These findings may contribute to the development of potential therapeutic targets and biomarkers for the new-immunotherapy of LSCC patients with smoking use.
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Affiliation(s)
- Yujie Shen
- Department of Otorhinolaryngology Head and Neck Surgery, Eye, Ear, Nose and Throat Hospital, Fudan University, Shanghai, China
| | - Han Zhou
- Department of Otorhinolaryngology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shikun Dong
- Department of Otolaryngology Head and Neck Surgery, Zhongda Hospital, Southeast University, Nanjing, China
| | - Weida Dong
- Department of Otorhinolaryngology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Liqing Zhang
- Department of Otorhinolaryngology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Liqing Zhang,
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Zhang H, Zhang X, Wang X, Sun H, Hou C, Yu Y, Wang S, Yin F, Yang Z. Comprehensive Analysis of TRP Channel–Related Genes in Patients With Triple-Negative Breast Cancer for Guiding Prognostic Prediction. Front Oncol 2022; 12:941283. [PMID: 35875096 PMCID: PMC9300844 DOI: 10.3389/fonc.2022.941283] [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: 05/11/2022] [Accepted: 05/25/2022] [Indexed: 11/30/2022] Open
Abstract
Background Triple-negative breast cancer (TNBC) is a special subtype of breast cancer. Transient Receptor Potential (TRP) channel superfamily has emerged as a novel and interesting target in a variety of tumors. However, the association of TRP channel–related genes with TNBC is still unclear. Methods The The Cancer Genome Atlas (TCGA)-TNBC and GSE58812 datasets were downloaded from the public database. The differentially expressed TRP channel–related genes (DETGs) were screened by limma package, and mutations of the above genes were analyzed. Subsequently, new molecular subtypes in TNBC-based DETGs were explored by consensus clustering analysis. In addition, Lasso–Cox regression analysis was used to divide it into two robust risk subtypes: high-risk group and low-risk group. The accuracy and distinguishing ability of above models were verified by a variety of methods, including Kaplan–Meier survival analysis, ROC analysis, calibration curve, and PCA analysis. Meanwhile, CIBERSORT algorithm was used to excavate status of immune-infiltrating cells in TNBC tissues. Last, we explored the therapeutic effect of drugs and underlying mechanisms of risk subgroups by pRRophetic package and GSEA algorithm, respectively. Results A total of 19 DETGs were identified in 115 TNBC and 113 normal samples from TCGA database. In addition, missense mutation and SNP were the most common variant classification. According to Lasso–Cox regression analysis, the risky formula performed best when nine genes were used: TRPM5, TRPV2, HTR2B, HRH1, P2RY2, MAP2K6, NTRK1, ADCY6, and PRKACB. Subsequently, Kaplan–Meier survival analysis, ROC analysis, calibration curve, and Principal Components Analysis (PCA) analysis showed an excellent accuracy for predicting OS using risky formula in each cohort (P < 0.05). Specifically, high-risk group had a shorter OS compared with low-risk group. In addition, T-cell CD4 memory activated and macrophages M1 were enriched in normal tissues, whereas Tregs were increased in tumor tissues. Note that the low-risk group was better therapeutic effect to docetaxel, doxorubicin, cisplatin, paclitaxel, and gemcitabine than the high-risk group (P < 0.05). Last, in vitro assays, Quantitative Real-time PCR (qRT-PCR) indicated that TRPM5 was significantly highly expressed in MDA-MB-231 and MDA-MB-468 cells compared with that in MCF-10A cells (P < 0.01). Conclusion We identified a risky formula based on expression of TRP channel–related genes that can predict prognosis, therapeutic effect, and status of tumor microenvironment for patients with TNBC.
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Affiliation(s)
- Haojie Zhang
- The Second Medical College, Binzhou Medical University, Yantai, China
- Department of Thyroid and Breast Surgery, Binzhou Medical University Hospital, Binzhou, China
| | - Xiangsheng Zhang
- Department of Thyroid and Breast Surgery, Binzhou Medical University Hospital, Binzhou, China
| | - Xiaohong Wang
- Department of Thyroid and Breast Surgery, Binzhou Medical University Hospital, Binzhou, China
- *Correspondence: Xiaohong Wang, ; Zhenlin Yang,
| | - Hongguang Sun
- Department of Thyroid and Breast Surgery, Binzhou Medical University Hospital, Binzhou, China
| | - Changran Hou
- Department of Thyroid and Breast Surgery, Binzhou Medical University Hospital, Binzhou, China
| | - Yue Yu
- Department of Thyroid and Breast Surgery, Binzhou Medical University Hospital, Binzhou, China
| | - Song Wang
- Department of Thyroid and Breast Surgery, Binzhou Medical University Hospital, Binzhou, China
| | - Fangxu Yin
- Department of Thyroid and Breast Surgery, Binzhou Medical University Hospital, Binzhou, China
| | - Zhenlin Yang
- Department of Thyroid and Breast Surgery, Binzhou Medical University Hospital, Binzhou, China
- *Correspondence: Xiaohong Wang, ; Zhenlin Yang,
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Zhong ME, Huang ZP, Wang X, Cai D, Li CH, Gao F, Wu XJ, Wang W. A Transcription Factor Signature Can Identify the CMS4 Subtype and Stratify the Prognostic Risk of Colorectal Cancer. Front Oncol 2022; 12:902974. [PMID: 35847938 PMCID: PMC9280271 DOI: 10.3389/fonc.2022.902974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 05/26/2022] [Indexed: 12/24/2022] Open
Abstract
BackgroundColorectal cancer (CRC) is a heterogeneous disease, and current classification systems are insufficient for stratifying patients with different risks. This study aims to develop a generalized, individualized prognostic consensus molecular subtype (CMS)-transcription factors (TFs)-based signature that can predict the prognosis of CRC.MethodsWe obtained differentially expressed TF signature and target genes between the CMS4 and other CMS subtypes of CRC from The Cancer Genome Atlas (TCGA) database. A multi-dimensional network inference integrative analysis was conducted to identify the master genes and establish a CMS4-TFs-based signature. For validation, an in-house clinical cohort (n = 351) and another independent public CRC cohort (n = 565) were applied. Gene set enrichment analysis (GSEA) and prediction of immune cell infiltration were performed to interpret the biological significance of the model.ResultsA CMS4-TFs-based signature termed TF-9 that includes nine TF master genes was developed. Patients in the TF-9 high-risk group have significantly worse survival, regardless of clinical characteristics. The TF-9 achieved the highest mean C-index (0.65) compared to all other signatures reported (0.51 to 0.57). Immune infiltration revealed that the microenvironment in the high-risk group was highly immune suppressed, as evidenced by the overexpression of TIM3, CD39, and CD40, suggesting that high-risk patients may not directly benefit from the immune checkpoint inhibitors.ConclusionsThe TF-9 signature allows a more precise categorization of patients with relevant clinical and biological implications, which may be a valuable tool for improving the tailoring of therapeutic interventions in CRC patients.
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Affiliation(s)
- Min-Er Zhong
- Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ze-Ping Huang
- Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xun Wang
- Department of Biomedical Engineering, School of Basic Medical Science, Central South University, Changsha, China
| | - Du Cai
- Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Cheng-Hang Li
- Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Feng Gao
- Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Wei Wang, ; Xiao-Jian Wu, ; Feng Gao,
| | - Xiao-Jian Wu
- Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Wei Wang, ; Xiao-Jian Wu, ; Feng Gao,
| | - Wei Wang
- Biomedical Big Data Centre, Department of Gynaecology, Huzhou Maternity & Child Health Care Hospital, Huzhou, China
- *Correspondence: Wei Wang, ; Xiao-Jian Wu, ; Feng Gao,
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Breast Cancer Prognosis Prediction and Immune Pathway Molecular Analysis Based on Mitochondria-Related Genes. Genet Res (Camb) 2022; 2022:2249909. [PMID: 35707265 PMCID: PMC9174003 DOI: 10.1155/2022/2249909] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 05/18/2022] [Indexed: 11/30/2022] Open
Abstract
Background Mitochondria play an important role in breast cancer (BRCA). We aimed to build a prognostic model based on mitochondria-related genes. Method Univariate Cox regression analysis, random forest, and the LASSO method were performed in sequence on pretreated TCGA BRCA datasets to screen out genes from a Gene Set Enrichment Analysis, Gene Ontology: biological process gene set to build a prognosis risk score model. Survival analyses and ROC curves were performed to verify the model by using the GSE103091 dataset. The BRCA datasets were equally divided into high- and low-risk score groups. Comparisons between clinical features and immune infiltration related to different risk scores and gene mutation analysis and drug sensitivity prediction were performed for different groups. Result Four genes, MRPL36, FEZ1, BMF, and AFG1L, were screened to construct our risk score model in which the higher the risk score, the poorer the prognosis. Univariate and multivariate analyses showed that the risk score was significantly associated with age, M stage, and N stage. The gene mutation probability in the high-risk score group was significantly higher than that in the low-risk score group. Patients with higher risk scores were more likely to die. Drug sensitivity prediction in different groups indicated that PF-562271 and AS601245 might be new inhibitors of BRCA. Conclusion We developed a new workable risk score model based on mitochondria-related genes for BRCA prognosis and identified new targets and drugs for BRCA research.
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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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Wang S, Zhang X, Ning H, Dong S, Wang G, Sun R. B7 homolog 3 induces lung metastasis of breast cancer through Raf/MEK/ERK axis. Breast Cancer Res Treat 2022; 193:405-416. [PMID: 35312883 DOI: 10.1007/s10549-022-06520-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 01/05/2022] [Indexed: 12/22/2022]
Abstract
PURPOSE The essential action of B7 homolog 3 (B7-H3) in different diseases and cancers has been documented. We here focused on its role in breast cancer through the Raf/MEK/ERK axis regarding lung metastasis. METHODS Expression pattern of B7-H3 was determined in breast cancer tissues and cells with its correlation with prognosis analyzed. Then, through transfection of lentivirus vector expressing B7-H3-shRNA, overexpression vector of B7-H3 (B7-H3-LV), U0126 (small molecule inhibitor of MEK), or PD98059 (small molecule inhibitor of ERK), the in vitro and in vivo effects of B7-H3 in breast cancer cell biological processes, and lung metastasis were analyzed in relation to the Raf/MEK/ERK axis. RESULTS We discovered elevated B7-H3 in breast cancer and its elevation associated with poor prognosis. B7-H3 promoted the malignant properties of breast cancer cells, accompanied with increased N-cadherin and vimentin and reduced E-cadherin. Additionally, overexpression of B7-H3 accelerated the lung metastasis in breast cancer in vivo. All the above promoting action of B7-H3 was achieved through activation of the Raf/MEK/ERK signaling pathway. CONCLUSION Taken together, B7-H3 can promote lung metastasis in breast cancer through activation of the Raf/MEK/ERK axis.
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Affiliation(s)
- Shuai Wang
- Department of Radiotherapy, Affiliated Hospital of Weifang Medical University, No. 2428, Yuhe Road, Weifang, 261031, Shandong Province, China
| | - Xinyan Zhang
- Department of Intervention, The Affiliated Weihai Second Municipal Hospital of Qingdao University, Weihai, 264200, China
| | - Houfa Ning
- School of Medical Imaging, Weifang Medical University, No. 7166, Baotong West Street, Weifang, 261053, Shandong Province, China
| | - Senyi Dong
- School of Medical Imaging, Weifang Medical University, No. 7166, Baotong West Street, Weifang, 261053, Shandong Province, China
| | - Guangzhi Wang
- School of Medical Imaging, Weifang Medical University, No. 7166, Baotong West Street, Weifang, 261053, Shandong Province, China.
| | - Ruimei Sun
- Department of Radiotherapy, Affiliated Hospital of Weifang Medical University, No. 2428, Yuhe Road, Weifang, 261031, Shandong Province, China.
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Cell-Free DNA Variables including Gene Mutations in CA15-3 Normal Breast Cancer Reflect Prognosis. DISEASE MARKERS 2022; 2022:5470166. [PMID: 35251373 PMCID: PMC8894049 DOI: 10.1155/2022/5470166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 01/16/2022] [Accepted: 01/31/2022] [Indexed: 11/22/2022]
Abstract
Background Cell-free DNA (cfDNA) has attracted considerable attention in precision medicine. However, few data are available regarding to the prognostic value of cfDNA variables in CA15-3 normal breast cancer (BC) patients. Here, we aimed at investigating the prognostic value of cfDNA variables including gene mutations in CA15-3 normal BC patients. Methods A total of 68 BC patients with normal CA15-3 levels were enrolled. cfDNA concentration and integrity were assessed based on qPCR. cfDNA gene mutations were conducted by using next gene sequencing (NGS). The association between cfDNA variables and the prognosis of patients was analyzed. Results cfDNA concentration was related to tumor stage (P = 0.002), metastases (P = 0.001), and distant metastases (P < 0.001). The elevated copy number variants (CNV) were found in distant metastasis patients compared with patients without distant metastases (P = 0.008). Nineteen mutant genes were validated in enrolled CA15-3 normal BC patients. Thirty-two patients (47.0%) had single nucleotide variants (SNV), and 13 (19.1%) patients had TP53 mutations (TP53mut). SNV (P = 0.033) was related to tumor stage, and TP53mut was related to metastases (P = 0.016) and distant metastases (P = 0.006). In multivariate logistic analysis, cfDNA concentration was associated with metastases (OR = 3.404, 95% CI: 1.074-10.788, P = 0.037) and distant metastases (OR = 13.750, 95% CI: 1.473-128.358, P = 0.021). Cases with high cfDNA levels (>15.6 ng/ml), SNV, and TP53mut showed worse DFS compared with patients with low cfDNA levels (P < 0.001), without SNV (P = 0.002) and with TP53 wildtype (P < 0.001), respectively. In the multivariate Cox proportional hazard model, cfDNA concentration was an independent predictor of poor survival (HR = 5.786, 95% CI: 1.101-30.407, P = 0.038). Conclusions Assessment of cfDNA concentration, CNV, SNV, and TP53mut could be useful in predicting prognosis for CA15-3 normal BC patients. The cfDNA concentration was an independent predictor prognostic factor in CA15-3 normal BC patients.
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Liu C, Zhou J, Chang C, Zhi W. Feasibility of Shear Wave Elastography Imaging for Evaluating the Biological Behavior of Breast Cancer. Front Oncol 2022; 11:820102. [PMID: 35155209 PMCID: PMC8830494 DOI: 10.3389/fonc.2021.820102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 12/30/2021] [Indexed: 12/27/2022] Open
Abstract
Objective To explore the feasibility of shear wave elastography (SWE) parameters for assessing the biological behavior of breast cancer. Materials and Methods In this prospective study, 224 breast cancer lesions in 216 female patients were examined by B-mode ultrasound and shear wave elastography in sequence. The maximum size (Smax) of the lesion was measured by B-mode ultrasound, and then shear wave elastography was performed on this section to obtain relevant parameters, including maximum elasticity (Emax), mean elasticity (Emean), standard deviation of elasticity (SD), and the area ratio of shear wave elastography to B-mode ultrasound (AR). The relationship between SWE parameters and pathological type, histopathological classification, histological grade, lymphovascular invasion status (LVI), axillary lymph node status (ALN), and immunohistochemistry of breast cancer lesions was performed according to postoperative pathology. Results In the univariate analysis, the pathological type and histopathological classification of breast cancer were not significantly associated with SWE parameters; with an increase in the histological grade of invasive ductal carcinoma (IDC), SD (p = 0.016) and Smax (p = 0.000) values increased. In the ALN-positive group, Smax (p = 0.004) was significantly greater than in the ALN-negative group; Smax (p = 0.003), Emax (p = 0.034), and SD (p = 0.045) were significantly higher in the LVI-positive group than in the LVI-negative group; SD (p = 0.043, p = 0.047) and Smax (p = 0.000, p = 0.000) were significantly lower in the ER+ and PR+ groups than in the ER- and PR- groups, respectively; AR (p = 0.032) was significantly higher in the ER+ groups than in the ER- groups, and Smax (p = 0.002) of the HER2+ group showed higher values than that of the HER2- group; Smax (p = 0.000), SD (p = 0.006), and Emax (p = 0.004) of the Ki-67 high-expression group showed significantly higher values than those of the Ki-67 low-expression group. In the multivariate analysis, Ki-67 was an independent factor of Smax (p = 0.005), Emax (p = 0.004), and SD (p = 0.006); ER was an independent influencing factor of Smax (p = 0.000) and AR (p = 0.032). LVI independently influences Smax (p = 0.006). Conclusions The SWE parameters Emax, SD, and AR can be used to evaluate the biological behavior of breast cancer.
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Affiliation(s)
- Chaoxu Liu
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jin Zhou
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Cai Chang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wenxiang Zhi
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
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Li R, Wu X, Li A, Wang M. OUP accepted manuscript. Bioinformatics 2022; 38:2587-2594. [PMID: 35188177 PMCID: PMC9048674 DOI: 10.1093/bioinformatics/btac113] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 01/20/2022] [Accepted: 02/17/2022] [Indexed: 12/03/2022] Open
Abstract
Motivation Cancer survival prediction can greatly assist clinicians in planning patient treatments and improving their life quality. Recent evidence suggests the fusion of multimodal data, such as genomic data and pathological images, is crucial for understanding cancer heterogeneity and enhancing survival prediction. As a powerful multimodal fusion technique, Kronecker product has shown its superiority in predicting survival. However, this technique introduces a large number of parameters that may lead to high computational cost and a risk of overfitting, thus limiting its applicability and improvement in performance. Another limitation of existing approaches using Kronecker product is that they only mine relations for one single time to learn multimodal representation and therefore face significant challenges in deeply mining rich information from multimodal data for accurate survival prediction. Results To address the above limitations, we present a novel hierarchical multimodal fusion approach named HFBSurv by employing factorized bilinear model to fuse genomic and image features step by step. Specifically, with a multiple fusion strategy HFBSurv decomposes the fusion problem into different levels and each of them integrates and passes information progressively from the low level to the high level, thus leading to the more specialized fusion procedure and expressive multimodal representation. In this hierarchical framework, both modality-specific and cross-modality attentional factorized bilinear modules are designed to not only capture and quantify complex relations from multimodal data, but also dramatically reduce computational complexity. Extensive experiments demonstrate that our method performs an effective hierarchical fusion of multimodal data and achieves consistently better performance than other methods for survival prediction. Availability and implementation HFBSurv is freely available at https://github.com/Liruiqing-ustc/HFBSurv. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ruiqing Li
- School of Information Science and Technology, University of Science and Technology of China, Hefei AH230027, China
| | - Xingqi Wu
- School of Information Science and Technology, University of Science and Technology of China, Hefei AH230027, China
| | - Ao Li
- School of Information Science and Technology, University of Science and Technology of China, Hefei AH230027, China
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Lv W, Tan Y, Xiong M, Zhao C, Wang Y, Wu M, Wu Y, Zhang Q. Analysis and validation of m6A regulatory network: a novel circBACH2/has-miR-944/HNRNPC axis in breast cancer progression. J Transl Med 2021; 19:527. [PMID: 34952600 PMCID: PMC8709995 DOI: 10.1186/s12967-021-03196-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 12/14/2021] [Indexed: 12/04/2022] Open
Abstract
Background N6-methyladenosine (m6A), the most abundant and reversible modification of mRNAs in eukaryotes, plays pivotal role in breast cancer (BC) tumorigenesis and progression. Circular RNAs (circRNAs) can act as tumor promoters or suppressors by microRNA (miRNA) sponges in BC. However, the underlying mechanism of circRNAs in BC progression via regulating m6A modulators remains unclear. Methods Prognostic m6A RNA methylation regulators were identified in 1065 BC patients from The Cancer Genome Atlas (TCGA) project. Differentially expressed (DE) miRNAs and DE circRNAs were identified between BC and normal samples in TCGA and GSE101123, respectively. MiRNA-mRNA interactive pairs and circRNA-miRNA interactive pairs were verified by MiRDIP and Circular RNA Interactome. GSEA, KEGG, and ssGSEA were executed to explore the potential biological and immune functions between HNRNPC-high and HNRNPC-low expression groups. qRT-PCR and Western blot were used to quantify the expression of HNRNPC and circBACH2 in MCF-7 and MDA-MB-231 cells. The proliferation of BC cells was assessed by CCK-8 and EdU assay. Results 2 m6A RNA methylation regulators with prognostic value, including HNRNPC and YTHDF3, were identified in BC patients. Then, the regulatory network of circRNA-miRNA-m6A modulators was constructed, which consisted of 2 DE m6A modulators (HNRNPC and YTHDF3), 12 DE miRNAs, and 11 DE circRNAs. Notably, BC patients with high expression of HNRNPC and low expression of hsa-miR-944 were correlated with late clinical stages and shorter survival times. Besides, the results from the KEGG inferred that the DE HNRNPC was associated with the MAPK signaling pathway in BC. Moreover, the circBACH2 (hsa_circ_0001625) was confirmed to act as hsa-miR-944 sponge to stimulate HNRNPC expression to promote BC cell proliferation via MAPK signaling pathway, thus constructing a circBACH2/hsa-miR-944/HNRNPC axis in BC. Conclusions Our findings decipher a novel circRNA-based m6A regulatory mechanism involved in BC progression, thus providing attractive diagnostic and therapeutic strategies for combating BC. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-03196-4.
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Affiliation(s)
- Wenchang Lv
- Department of Plastic and Cosmetic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, Hubei, China
| | - Yufang Tan
- Department of Plastic and Cosmetic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, Hubei, China
| | - Mingchen Xiong
- Department of Plastic and Cosmetic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, Hubei, China
| | - Chongru Zhao
- Department of Plastic and Cosmetic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, Hubei, China
| | - Yichen Wang
- Department of Plastic and Cosmetic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, Hubei, China
| | - Min Wu
- Department of Plastic and Cosmetic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, Hubei, China
| | - Yiping Wu
- Department of Plastic and Cosmetic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, Hubei, China.
| | - Qi Zhang
- Department of Plastic and Cosmetic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, Hubei, China.
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