Identification and Validation of an Autophagy-Related Gene Signature for Prognostic Prediction and Immunotherapy Response in Esophageal Squamous Cell Carcinoma

Purpose: Despite existing signatures, there remains a lack of robust autophagy-based biomarkers validated across multi-omics datasets and independent clinical cohorts in ESCC. The aim of our study was to develop an autophagy-related prognostic model for ESCC. Methods: Using transcriptomic data of ESCC from GEO/TCGA and autophagy-related genes (ARGs) from five autophagy-specific datasets, we identified the intersection between ARGs and tumor-normal differentially expressed genes (DEGs). We constructed a prognostic model using stepwise multivariate Cox regression based on these genes in GSE53625 (n = 179), validated in TCGA-ESCC (n = 94) through survival analysis and ROC curve, and analyzed the prognostic value of candidate genes in in-house ESCC samples. Results: We successfully established a robust prognostic 4-ARGs model comprising NBEA, CLOCK, NLRX1, and MAGEA3 (training: p < 0.0001, validation: p = 0.013). In the in-house ESCC cohort (n = 14), NLRX1 was verified as a reliable prognostic factor for disease-free survival (p = 0.043). Significant correlations were observed between signatures and the immune microenvironment, and the model effectively predicted patients’ responses to immunotherapy. Conclusion: We developed a novel 4-ARGs prognostic model and identified NLRX1 as a potential autophagy-dependent biomarker. These findings underscore its utility as a valuable tool for prognosis, risk stratification, and therapy guidance in ESCC.