Autophagy-centered gene networks reveal prognostic biomarkers and therapeutic targets in esophageal cancer.

Esophageal cancer (EC) remains an extremely lethal cancer with few prognostic biomarkers and specific therapies. Although autophagy is increasingly recognized as a key force behind tumor adaptation and resistance to therapy, its systematic role at the EC progression level for prognostics, as well as for drug target prediction, remains unclear. Here, we attempted to build a systems-wide map linking autophagy- and signaling-related genes and transcriptional dysregulation, patient survival, and druggability in EC. Hub genes were found using an integrative bioinformatics pipeline based on protein-protein interaction networks and further explored using enrichment, expression, survival, and molecular docking analyses. Functional enrichment highlighted autophagy, mitophagy, ferroptosis, and immune signaling as central processes, converging with stress- and metabolism-associated pathways. Expression profile of TCGA-ESCA data demonstrated substantial overexpression of autophagy initiators and elongation factors (ATG3, ATG5, ATG7, ATG12, ATG13), upstream regulators (AMBRA1, UVRAG), and stress/metabolic mediators (TP53, MYD88, GAPDH). Kaplan-Meier analysis indicated three genes, including ATG4A, GABARAPL2, and GAPDH, that exhibited significant expression levels correlating with less survival and emphasizing their prognostic capacities. Screening for drugs also revealed AKT1, TP53, and PIK3R4 as druggable hubs, and many drugs (e.g., Everolimus, Dabrafenib, Trabectedin) showing high-affinity interactions. These findings collectively demonstrate that the progression of EC is supported by a coordinated program that integrates autophagy and metabolic reprogramming with stress and immunological signaling. The study discovers new prognostic markers (ATG4A, GABARAPL2, GAPDH) and druggable targets, which could lead to better risk stratification and smarter drug repurposing. While restricted to in silico analyses, the integrative approach provides a basis for subsequent laboratory confirmation and translational development.