Integrated machine learning identifies epithelial cell marker genes for improving outcomes and immunotherapy in prostate cancer

作者：Weian Zhu, Hengda Zeng, Jiongduan Huang, Jianjie Wu, Yu Wang, Ziqiao Wang, Hua Wang, Yun Luo, Wenjie Lai · 发表于：Journal of Translational Medicine · 年份：2023 · DOI：10.1186/s12967-023-04633-2 · 被引用次数：32 · 研究领域：Ferroptosis and cancer prognosis、Single-cell and spatial transcriptomics、Cancer Immunotherapy and Biomarkers

BACKGROUND: Prostate cancer (PCa), a globally prevalent malignancy, displays intricate heterogeneity within its epithelial cells, closely linked with disease progression and immune modulation. However, the clinical significance of genes and biomarkers associated with these cells remains inadequately explored. To address this gap, this study aimed to comprehensively investigate the roles and clinical value of epithelial cell-related genes in PCa. METHODS: Leveraging single-cell sequencing data from GSE176031, we conducted an extensive analysis to identify epithelial cell marker genes (ECMGs). Employing consensus clustering analysis, we evaluated the correlations between ECMGs, prognosis, and immune responses in PCa. Subsequently, we developed and validated an optimal prognostic signature, termed the epithelial cell marker gene prognostic signature (ECMGPS), through synergistic analysis from 101 models employing 10 machine learning algorithms across five independent cohorts. Additionally, we collected clinical features and previously published signatures from the literature for comparative analysis. Furthermore, we explored the clinical utility of ECMGPS in immunotherapy and drug selection using multi-omics analysis and the IMvigor cohort. Finally, we investigated the biological functions of the hub gene, transmembrane p24 trafficking protein 3 (TMED3), in PCa using public databases and experiments. RESULTS: We identified a comprehensive set of 543 ECMGs and established a stron...