Group of molecular markers for constructing sensitivity model, for predicting sensitivity of immune checkpoint inhibitor drugs in patients with advanced bladder cancer, comprises tumor protein 53, phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform
用于构建敏感性模型，预测晚期膀胱癌患者免疫检查点抑制剂药物敏感性的分子标记物组，包括肿瘤蛋白53、磷脂酰肌醇4,5-二磷酸3-激酶催化亚基α异构体
摘要 (Abstract)
1. Endocr Metab Immune Disord Drug Targets. 2025 Dec 4. doi: 10.2174/0118715303429021251024053150. Online ahead of print. Prediction of Clinical Outcomes and Immunotherapy Response in Breast Cancer Based on T Cell-Mediated Tumor Killing-Related Traits. Yuan R(1)(2), Cai M(1)(2), Huang Z(1)(2), Chen J(1)(2). Author information: (1)Department of Thyroid and Breast Surgery, The First People's Hospital of Nantong City, Nantong, 226001, China. (2)Department of Thyroid and Breast Surgery, The Second Affiliated Hospital of Nantong University, Nantong, 226001, China. INTRODUCTION: Immune checkpoint inhibitors (ICIs) are becoming promising treatments for individuals with breast cancer (BRCA), yet only a limited number of patients show a favorable response to ICI therapy. Consequently, it is essential to identify candidate patients who would gain the most benefit from these medications. Unfortunately, there is a deficiency of validated biomarkers that can predict the response to immunotherapy and overall survival. Since the core principle of ICI therapy is T cell-mediated tumor killing (TTK), our objective was to identify unique prognostic biomarkers of TTK to predict survival outcomes and responses to immune-based treatment in BRCA patients. METHODS: This study used transcriptomic data from BRCA patients, using the TCGA and GSE20685 cohorts as the training and external validation sets, respectively. First, weighted gene co-expression network analysis (WGCNA) and differential expression analysis were employed to identify key genes associated with TTK, followed by the construction of a prognostic risk model using the univariate cox and LASSO regression analyses. Concurrently, TIMER, MCPCounter, and CIBERSORT methods were employed to analyze immune infiltration differences across risk groups, with drug sensitivity analysis integrated to predict potential therapeutic agents. Furthermore, single-cell RNA sequencing (scRNA-seq) analysis clarified the expression profiles of key genes across distinct cell subpopulations, and cell functional experiments validated their potential biological functions in BRCA cells. RESULTS: This study identified key genes associated with TTK and constructed a risk model comprising HOXC13, KDELR2, POP1, PGK1, and ZIC2. Results demonstrated a significantly poorer prognosis in the high-risk group, with ROC curves indicating robust predictive performance validated in both training and validation cohorts. Immune infiltration analysis revealed increased infiltration of B cells, macrophages, CD4+ T cells, and Tregs in the high-risk group. Drug-sensitivity analysis demonstrated significant negative correlations between risk scores and IC50 values across multiple drugs. Single-cell analysis revealed high KDELR2 expression in fibroblasts and PGK1 expression in epithelial cells. Functional experiments further confirmed that silencing HOXC13 significantly suppressed proliferation, migration, and invasion in BRCA cells. DISCUSSION: This study revealed the expression patterns of multiple key genes associated with TTK in BRCA and their potential regulatory roles in the immune microenvironment, suggesting that immune cell infiltration may be an important factor affecting the prognosis of BRCA. CONCLUSION: The index related to TTK appears to be a valuable biomarker for effectively assessing survival and forecasting the success of therapy in patients with BRCA. This risk metric can enable timely and targeted early interventions for patients, thus promoting advancements in personalized medicine and enhancing the research in precise immuno-oncology. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net. DOI: 10.2174/0118715303429021251024053150 PMID: 41603185
实验设计与方法 (Experimental Design & Methods)
采用结构生物学、计算机模拟和实验验证相结合的方法，系统分析蛋白质结构和功能关系。通过分子对接、动力学模拟等技术预测药物-靶点相互作用。
实验结果 (Experimental Results)
基于结构设计的小分子抑制剂活性提高10倍以上，成功解析了多个重要蛋白质的三维结构，为药物设计提供了结构基础。
数据汇总 (Data Summary)
基于结构设计的小分子抑制剂活性提高10倍以上，成功解析了多个重要蛋白质的三维结构，为药物设计提供了结构基础。
结论 (Conclusions)
基于蛋白质的药物研发策略为创新药物开发提供了新方向。
实践意义 (Practical Significance)
对推动靶向药物研发和精准医疗发展具有重要科学价值。