Complex biological systems analysis and deep learning for prognostic prediction of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) prognosis remains poor, and traditional models often fail to capture complex nonlinear interactions between clinical and molecular features. We integrated transcriptomic data from public datasets and an independent clinical cohort to identify prognostic biomarkers. Using weighted gene co-expression network analysis (WGCNA) and Lasso-Cox regression, we identified 16 key genes to construct a deep learning-based survival model, DeepSurv, integrating clinical and genetic features. DeepSurv demonstrated superior predictive performance compared to conventional machine learning models in both internal and external validation cohorts. SHAP value analysis highlighted the contribution of specific genes, including FAM155B and CFHR4, alongside TNM stages. This study establishes a robust, multidimensional prognostic tool that enhances risk stratification and offers insights into the molecular mechanisms driving ESCC progression.