Integrated Single-Cell RNA-Seq and Machine Learning to Construct an EMT Infiltration Scoring Model for Prostate Cancer

Prostate cancer (PCa) remains a major global health concern, with a subset of patients progressing to aggressive disease despite advances in diagnosis and treatment. Epithelial-mesenchymal transition (EMT) plays a pivotal role in tumor invasion, metastasis, and immune evasion; however, its cellular heterogeneity and clinical relevance in PCa remain incompletely understood. We analyzed single-cell transcriptomic data to characterize EMT dynamics in malignant epithelial cells. Malignant cells were identified based on aberrant copy number variation patterns, and EMT activity was quantified using AUCell. Gene expression profiling and gene set enrichment analysis identified key EMT-associated genes. By integrating bulk transcriptomic data with LASSO regression analysis, we identified five pivotal genes and constructed an EMT infiltration scoring model. The model demonstrated robust predictive performance in an external Gene Expression Omnibus validation cohort and effectively predicted early biochemical recurrence. Further analyses revealed significant associations between EMT scores, clinicopathological features, immune cell infiltration, genomic instability, and tumor immune dysfunction and exclusion scores. Pathway enrichment analysis highlighted distinct molecular characteristics between high- and low-score groups. Additionally, molecular docking using AutoDock identified potential targeted therapeutic agents for key EMT genes. Overall, this study systematically delineates EMT heterogeneity at the single-cell level and establishes a robust EMT infiltration model for prognostic prediction and therapeutic guidance in PCa, providing novel insights for precision risk stratification and individualized treatment strategies.