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Long Q, Wang Y, Li H. Homologous recombination deficiency reflects the heterogeneity and monitoring treatment response for patients with breast cancer. J Gene Med 2024; 26:e3637. [PMID: 37994492 DOI: 10.1002/jgm.3637] [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: 09/06/2023] [Revised: 10/17/2023] [Accepted: 10/31/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND In breast cancer (BC), homologous recombination defect (HRD) is a common carcinogenic mechanism. It is meaningful to classify BC according to HRD biomarkers and to develop a platform for identifying BC molecular features, pathological features and therapeutic responses. METHODS In total, 109 HRD genes were collected and screened by univariate Cox regression analysis to determine the prognostic genes, which were used to construct a consensus matrix to identify BC subtype. Differentially expressed genes (DEGs) were filtered by the Limma package and screened by random forest analysis to build a model to analyze the immunotherapy response and sensitivity and prognosis of patients suffering from BC to different drugs. RESULTS Thirteen out of 109 HRD genes were prognostic genes of BC, and BC was classified into two subgroups based on their expression. Cluster 1 had a significantly backward survival outcome and a significantly higher adaptive immunity score relative to cluster 2. Six genes were identified by random forest analysis as factors for developing the model. The model provided a prediction called risk score, which showed a significant stratification effect on BC prognosis, immunotherapy response and IC50 values of 62 drugs. CONCLUSIONS In the present study, two HRD subtypes of BC were successfully identified, for which mutation and immunological features were determined. A model based on differential genes of HRD subtypes was established, which was a potential predictor of prognosis, immunotherapy response and drug sensitivity of BC.
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Affiliation(s)
- Quanyi Long
- Department of Breast Surgery, West China Hospital, Sichuan University, Chengdu, China
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yunfei Wang
- Hangzhou Shengting Medical Technology Co., Ltd, Hangzhou, China
| | - Hongjiang Li
- Department of Breast Surgery, West China Hospital, Sichuan University, Chengdu, China
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
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Shen HY, Xu JL, Zhu Z, Xu HP, Liang MX, Xu D, Chen WQ, Tang JH, Fang Z, Zhang J. Integration of bioinformatics and machine learning strategies identifies APM-related gene signatures to predict clinical outcomes and therapeutic responses for breast cancer patients. Neoplasia 2023; 45:100942. [PMID: 37839160 PMCID: PMC10587768 DOI: 10.1016/j.neo.2023.100942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/10/2023] [Indexed: 10/17/2023]
Abstract
BACKGROUND Tumor antigenicity and efficiency of antigen presentation jointly influence tumor immunogenicity, which largely determines the effectiveness of immune checkpoint blockade (ICB). However, the role of altered antigen processing and presentation machinery (APM) in breast cancer (BRCA) has not been fully elucidated. METHODS A series of bioinformatic analyses and machine learning strategies were performed to construct APM-related gene signatures to guide personalized treatment for BRCA patients. A single-sample gene set enrichment analysis (ssGSEA) algorithm and weighted gene co-expression network analysis (WGCNA) were combined to screen for BRCA-specific APM-related genes. The non-negative matrix factorization (NMF) algorithm was used to divide the cohort into different clusters and the fgsea algorithm was applied to investigate the altered signaling pathways. Random survival forest (RSF) and the least absolute shrinkage and selection operator (Lasso) Cox regression analysis were combined to construct an APM-related risk score (APMrs) signature to predict overall survival. Furthermore, a nomogram and decision tree were generated to improve predictive accuracy and risk stratification for individual patients. Based on Tumor Immune Dysfunction and Exclusion (TIDE) method, random forest (RF) and Lasso logistic regression model were combined to establish an APM-related immunotherapeutic response score (APMis). Finally, immune infiltration, immunomodulators, mutational patterns, and potentially applicable drugs were comprehensively analyzed in different APM-related risk groups. IHC staining was used to assess the expression of APM-related genes in clinical samples. RESULTS In this study, APMrs and APMis showed favorable performances in risk stratification and therapeutic prediction for BRCA patients. APMrs exhibited more powerful prognostic capacity and accurate survival prediction compared to conventional clinicopathological features. APMrs was closely associated with distinct mutational patterns, immune cell infiltration and immunomodulators expression. Furthermore, the two APM-related gene signatures were independently validated in external cohorts with prognosis or immunotherapeutic responses. Potential applicable drugs and targets were mined in the APMrs-high group. APM-related genes were further validated in our in-house samples. CONCLUSION The APM-related gene signatures established in our study could improve the personalized assessment of survival risk and guide ICB decision-making for BRCA patients.
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Affiliation(s)
- Hong-Yu Shen
- Gusu School, The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University, Suzhou, China; Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jia-Lin Xu
- Gusu School, The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University, Suzhou, China
| | - Zhen Zhu
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hai-Ping Xu
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ming-Xing Liang
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Di Xu
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wen-Quan Chen
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jin-Hai Tang
- Gusu School, The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University, Suzhou, China; Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
| | - Zheng Fang
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
| | - Jian Zhang
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
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Chen Y, Wu W, Jin C, Cui J, Diao Y, Wang R, Xu R, Yao Z, Li X. Integrating Single-Cell RNA-Seq and Bulk RNA-Seq Data to Explore the Key Role of Fatty Acid Metabolism in Breast Cancer. Int J Mol Sci 2023; 24:13209. [PMID: 37686016 PMCID: PMC10487665 DOI: 10.3390/ijms241713209] [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: 06/12/2023] [Revised: 08/18/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
Cancer immune escape is associated with the metabolic reprogramming of the various infiltrating cells in the tumor microenvironment (TME), and combining metabolic targets with immunotherapy shows great promise for improving clinical outcomes. Among all metabolic processes, lipid metabolism, especially fatty acid metabolism (FAM), plays a major role in cancer cell survival, migration, and proliferation. However, the mechanisms and functions of FAM in the tumor immune microenvironment remain poorly understood. We screened 309 fatty acid metabolism-related genes (FMGs) for differential expression, identifying 121 differentially expressed genes. Univariate Cox regression models in The Cancer Genome Atlas (TCGA) database were then utilized to identify the 15 FMGs associated with overall survival. We systematically evaluated the correlation between FMGs' modification patterns and the TME, prognosis, and immunotherapy. The FMGsScore was constructed to quantify the FMG modification patterns using principal component analysis. Three clusters based on FMGs were demonstrated in breast cancer, with three patterns of distinct immune cell infiltration and biological behavior. An FMGsScore signature was constructed to reveal that patients with a low FMGsScore had higher immune checkpoint expression, higher immune checkpoint inhibitor (ICI) scores, increased immune microenvironment infiltration, better survival advantage, and were more sensitive to immunotherapy than those with a high FMGsScore. Finally, the expression and function of the signature key gene NDUFAB1 were examined by in vitro experiments. This study significantly demonstrates the substantial impact of FMGs on the immune microenvironment of breast cancer, and that FMGsScores can be used to guide the prediction of immunotherapy efficacy in breast cancer patients. In vitro experiments, knockdown of the NDUFAB1 gene resulted in reduced proliferation and migration of MCF-7 and MDA-MB-231 cell lines.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Xiaofeng Li
- Department of Epidemiology and Health Statistics, Dalian Medical University, Dalian 116044, China
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Southekal S, Shakyawar SK, Bajpai P, Elkholy A, Manne U, Mishra NK, Guda C. Molecular Subtyping and Survival Analysis of Osteosarcoma Reveals Prognostic Biomarkers and Key Canonical Pathways. Cancers (Basel) 2023; 15:2134. [PMID: 37046795 PMCID: PMC10093233 DOI: 10.3390/cancers15072134] [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: 03/06/2023] [Revised: 03/28/2023] [Accepted: 03/30/2023] [Indexed: 04/07/2023] Open
Abstract
Osteosarcoma (OS) is a common bone malignancy in children and adolescents. Although histological subtyping followed by improved OS treatment regimens have helped achieve favorable outcomes, a lack of understanding of the molecular subtypes remains a challenge to characterize its genetic heterogeneity and subsequently to identify diagnostic and prognostic biomarkers for developing effective treatments. In the present study, global analysis of DNA methylation, and mRNA and miRNA gene expression in OS patient samples were correlated with their clinical characteristics. The mucin family of genes, MUC6, MUC12, and MUC4, were found to be highly mutated in the OS patients. Results revealed the enrichment of molecular pathways including Wnt signaling, Calcium signaling, and PI3K-Akt signaling in the OS tumors. Survival analyses showed that the expression levels of several genes such as RAMP1, CRIP1, CORT, CHST13, and DDX60L, miRNAs and lncRNAs were associated with survival of OS patients. Molecular subtyping using Cluster-Of-Clusters Analysis (COCA) for mRNA, lncRNA, and miRNA expression; DNA methylation; and mutation data from the TARGET dataset revealed two distinct molecular subtypes, each with a distinctive gene expression profile. Between the two subtypes, three upregulated genes, POP4, HEY1, CERKL, and seven downregulated genes, CEACAM1, ABLIM1, LTBP2, ISLR, LRRC32, PTPRF, and GPX3, associated with OS metastasis were found to be differentially regulated. Thus, the molecular subtyping results provide a strong basis for classification of OS patients that could be used to develop better prognostic treatment strategies.
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Affiliation(s)
- Siddesh Southekal
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Sushil Kumar Shakyawar
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Prachi Bajpai
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Amr Elkholy
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Upender Manne
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Nitish Kumar Mishra
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Chittibabu Guda
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
- Center for Biomedical Informatics Research and Innovation, University of Nebraska Medical Center, Omaha, NE 68198, USA
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Huang CS, Tsai ML, Lu TP, Tu CC, Liu CY, Huang CJ, Ho YS, Tu SH, Chuang EY, Tseng LM, Huang CC. The extended concurrent genes signature for disease-free survival in breast cancer. J Formos Med Assoc 2022; 121:1945-1955. [PMID: 35181201 DOI: 10.1016/j.jfma.2022.01.022] [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: 08/20/2020] [Revised: 10/11/2021] [Accepted: 01/18/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND/PURPOSE Previously we had identified concurrent genes, which highlighted the interplay between copy number variation (CNV) and differential gene expression (GE) for Han Chinese breast cancers. The merit of the approach is to discovery biomarkers not identifiable by conventional GE only data, for which phenotype-correlation or gene variability is the criteria of gene selection. MATERIALS AND METHODS Thirty-one comparative genomic hybridization (CGH) and 83 GE microarrays were performed, with 29 breast cancers assayed from both platforms. Potential targets were revealed by Genomic Identification of Significant Targets in Cancer (GISTIC) from CGH arrays. Concurrent genes and genes with significant GISTIC scores were used to derive the extended concurrent genes signature, which was consensus from leading edge analysis across all studies and a supervised partial least square (PLS) regression predictive model of disease-free survival was constructed. RESULTS There were 1584 concurrent genes from 29 samples with both CGH and GE microarrays. Enriched concurrent genes sets for disease-free survival were identified independently from 83 GE arrays and another one with Han Chinese origin as well as three studies of Western origin. For five studies with disease-free survival follow up, prognostic discrepancy was observed between predicted high-risk and low-risk group patients. CONCLUSION We concluded that through parallel analyses of CGH and GE microarrays, the proposed extended concurrent gene expression signature can identify biomarkers with prognostic values.
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Affiliation(s)
- Ching-Shui Huang
- Division of General Surgery, Department of Surgery, Cathay General Hospital, Taipei, Taiwan; School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Ming-Lin Tsai
- Division of General Surgery, Department of Surgery, Cathay General Hospital, Taipei, Taiwan
| | - Tzu-Pin Lu
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Chao-Chiang Tu
- Department of Surgery, Fu-Jen Catholic University Hospital, New Taipei, Taiwan; School of Medicine, College of Medicine, Fu-Jen Catholic University, New Taipei, Taiwan
| | - Chih-Yi Liu
- Division of Pathology, Cathay General Hospital Sijhih, New Taipei, Taiwan
| | - Chi-Jung Huang
- Department of Medical Research, Cathay General Hospital, Taipei, Taiwan; Department of Biochemistry, National Defense Medical Center, Taipei, Taiwan
| | - Yuan-Soon Ho
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei, Taiwan; Taipei Cancer Center, Taipei Medical University, Taipei, Taiwan; Department of Medical Laboratory, Taipei Medical University Hospital, Taipei, Taiwan; School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Shih-Hsin Tu
- School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Taipei Cancer Center, Taipei Medical University, Taipei, Taiwan; Division of Breast Surgery, Department of Surgery, Taipei Medical University Hospital, Taipei, Taiwan
| | - Eric Y Chuang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Ling-Ming Tseng
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Comprehensive Breast Health Center, Taipei Veterans General Hospital, Taipei, Taiwan.
| | - Chi-Cheng Huang
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Comprehensive Breast Health Center, Taipei Veterans General Hospital, Taipei, Taiwan.
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