{"title":"Identification of Molecular Subtypes and Prognostic Features of Breast Cancer Based on TGF-β Signaling-related Genes.","authors":"Jia Qu, Mei-Huan Wang, Yue-Hua Gao, Hua-Wei Zhang","doi":"10.1177/11769351251316398","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>The TGF-β signaling pathway is widely acknowledged for its role in various aspects of cancer progression, including cellular invasion, epithelial-mesenchymal transition, and immunosuppression. Immune checkpoint inhibitors (ICIs) and pharmacological agents that target TGF-β offer significant potential as therapeutic options for cancer. However, the specific role of TGF-β in prognostic assessment and treatment strategies for breast cancer (BC) remains unclear.</p><p><strong>Methods: </strong>The Cancer Genome Atlas (TCGA) database was utilized to develop a predictive model incorporating five TGF-β signaling-related genes (TSRGs). The GSE161529 dataset from the Gene Expression Omnibus was employed to conduct single-cell analyses aimed at further elucidating the characteristics of these TSRGs. Additionally, an unsupervised clustering algorithm was applied to categorize BC patients into two distinct groups based on the five TSRGs, with a focus on immune response and overall survival (OS). Further investigations were conducted to explore variations in pharmacotherapy and the tumor microenvironment across different patient cohorts and clusters.</p><p><strong>Results: </strong>The predictive model for BC identified five TSRGs: FUT8, IFNG, ID3, KLF10, and PARD6A. Single-cell analysis revealed that IFNG is predominantly expressed in CD8+ T cells. Consensus clustering effectively categorized BC patients into two distinct clusters, with cluster B demonstrating a longer OS and a more favorable prognosis. Immunological assessments indicated a higher presence of immune checkpoints and immune cells in cluster B, suggesting a greater likelihood of responsiveness to ICIs.</p><p><strong>Conclusion: </strong>The findings of this study highlight the potential of the TGF-β signaling pathway for prognostic classification and the development of personalized treatment strategies for BC patients, thereby enhancing our understanding of its significance in BC prognosis.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"24 ","pages":"11769351251316398"},"PeriodicalIF":2.5000,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11789128/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/11769351251316398","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives: The TGF-β signaling pathway is widely acknowledged for its role in various aspects of cancer progression, including cellular invasion, epithelial-mesenchymal transition, and immunosuppression. Immune checkpoint inhibitors (ICIs) and pharmacological agents that target TGF-β offer significant potential as therapeutic options for cancer. However, the specific role of TGF-β in prognostic assessment and treatment strategies for breast cancer (BC) remains unclear.
Methods: The Cancer Genome Atlas (TCGA) database was utilized to develop a predictive model incorporating five TGF-β signaling-related genes (TSRGs). The GSE161529 dataset from the Gene Expression Omnibus was employed to conduct single-cell analyses aimed at further elucidating the characteristics of these TSRGs. Additionally, an unsupervised clustering algorithm was applied to categorize BC patients into two distinct groups based on the five TSRGs, with a focus on immune response and overall survival (OS). Further investigations were conducted to explore variations in pharmacotherapy and the tumor microenvironment across different patient cohorts and clusters.
Results: The predictive model for BC identified five TSRGs: FUT8, IFNG, ID3, KLF10, and PARD6A. Single-cell analysis revealed that IFNG is predominantly expressed in CD8+ T cells. Consensus clustering effectively categorized BC patients into two distinct clusters, with cluster B demonstrating a longer OS and a more favorable prognosis. Immunological assessments indicated a higher presence of immune checkpoints and immune cells in cluster B, suggesting a greater likelihood of responsiveness to ICIs.
Conclusion: The findings of this study highlight the potential of the TGF-β signaling pathway for prognostic classification and the development of personalized treatment strategies for BC patients, thereby enhancing our understanding of its significance in BC prognosis.
期刊介绍：
The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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