Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach.

Abstract

Background: Endometrial cancer represents a significant health challenge, with rising incidence and complex prognostic challenges. This study aimed to develop a robust predictive model integrating programmed cell death-related genes and advanced machine learning techniques.

Methods: Utilizing transcriptomic data from TCGA-UCEC and GSE119041 datasets, we employed a comprehensive approach involving 117 machine learning algorithms. Key methodologies included differential gene expression analysis, weighted gene co-expression network analysis, functional enrichment studies, immune landscape evaluation, and multi-dimensional risk stratification.

Results: We identified 10 critical genes (PTGIS, TIMP3, SRPX, SNCA, HIC1, BAK1, STXBP2, TRIB3, RTKN2, E2F1) and constructed a prognostic model with superior predictive performance. The StepCox[forward] + plsRcox algorithm combination demonstrated excellent predictive accuracy (AUC > 0.8). Kaplan-Meier analysis revealed significant survival differences between high- and low-risk groups in both training (HR = 3.37, p < 0.001) and validation cohorts (HR = 2.05, p = 0.021). The model showed strong correlations with clinical characteristics, immune cell infiltration patterns, and potential therapeutic responses.

Conclusions: This study presents a novel, comprehensive approach to endometrial cancer prognosis, integrating machine learning and molecular insights to provide a more precise risk stratification tool with potential clinical translation.