{"@context":"https://schema.org","@type":"ScholarlyArticle","headline":"Prediction of Clinical Outcomes and Immunotherapy Response in Breast Cancer Based on T Cell-Mediated Tumor Killing-Related Traits.","author":[{"@type":"Person","name":"Ronghua Yuan"},{"@type":"Person","name":"Ming Cai"},{"@type":"Person","name":"Zhiqi Huang"},{"@type":"Person","name":"Jinpeng Chen"}],"datePublished":"2025","isPartOf":{"@type":"Periodical","name":"Endocrine, metabolic & immune disorders drug targets"},"identifier":"https://doi.org/10.2174/0118715303429021251024053150","description":"\n Introduction:\n Immune checkpoint inhibitors (ICIs) are becoming promising treatments\nfor individuals with breast cancer (BRCA), yet only a limited number of patients show a favorable\nresponse to ICI therapy. Consequently, it is essential to identify candidate patients who would\ngain the most benefit from these medications. Unfortunately, there is a deficiency of validated biomarkers\nthat can predict the response to immunotherapy and overall survival. Since the core principle\nof ICI therapy is T cell-mediated tumor killing (TTK), our objective was to identify unique\nprognostic biomarkers of TTK to predict survival outcomes and responses to immune-based treatment\nin BRCA patients.\n \n \n Methods:\n This study used transcriptomic data from BRCA patients, using the TCGA and\nGSE20685 cohorts as the training and external validation sets, respectively. First, weighted gene\nco-expression network analysis (WGCNA) and differential expression analysis were employed to\nidentify key genes associated with TTK, followed by the construction of a prognostic risk model\nusing the univariate cox and LASSO regression analyses. Concurrently, TIMER, MCPCounter,\nand CIBERSORT methods were employed to analyze immune infiltration differences across risk\ngroups, with drug sensitivity analysis integrated to predict potential therapeutic agents. Furthermore,\nsingle-cell RNA sequencing (scRNA-seq) analysis clarified the expression profiles of key\ngenes across distinct cell subpopulations, and cell functional experiments validated their potential\nbiological functions in BRCA cells.\n \n \n Results:\n This study identified key genes associated with TTK and constructed a risk model comprising\nHOXC13, KDELR2, POP1, PGK1, and ZIC2. Results demonstrated a significantly poorer\nprognosis in the high-risk group, with ROC curves indicating robust predictive performance validated\nin both training and validation cohorts. Immune infiltration analysis revealed increased infiltration\nof B cells, macrophages, CD4+ T cells, and Tregs in the high-risk group. Drug-sensitivity\nanalysis demonstrated significant negative correlations between risk scores and IC50 values\nacross multiple drugs. Single-cell analysis revealed high KDELR2 expression in fibroblasts and\nPGK1 expression in epithelial cells. Functional experiments further confirmed that silencing HOXC13\nsignificantly suppressed proliferation, migration, and invasion in BRCA cells.\n \n \n Discussion:\n This study revealed the expression patterns of multiple key genes associated with\nTTK in BRCA and their potential regulatory roles in the immune microenvironment, suggesting\nthat immune cell infiltration may be an important factor affecting the prognosis of BRCA.\n \n \n Conclusion:\n The index related to TTK appears to be a valuable biomarker for effectively assessing\nsurvival and forecasting the success of therapy in patients with BRCA. This risk metric can enable\ntimely and targeted early interventions for patients, thus promoting advancements in personalized\nmedicine and enhancing the research in precise immuno-oncology.\n ","url":"https://innscience.ca/es/community/literature/doi_10_2174_0118715303429021251024053150"}