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

## TL;DR

This study identifies a 4-gene model linked to autophagy that predicts survival and immunotherapy response in esophageal cancer patients.

## Contribution

A novel 4-ARGs prognostic model and the identification of NLRX1 as a potential biomarker for ESCC prognosis and immunotherapy response.

## Key findings

- A 4-ARG model (NBEA, CLOCK, NLRX1, MAGEA3) showed significant survival differences in ESCC patients.
- NLRX1 was validated as a reliable prognostic factor for disease-free survival in an in-house cohort.
- The model correlates with the immune microenvironment and predicts immunotherapy response.

## Abstract

Esophageal squamous cell carcinoma (ESCC) accounts for >90% of all esophageal cancer cases in China. While autophagy exerts dual roles in ESCC, its gene-level prognostic value remains undefined. The aim of our study was to develop an autophagy-related prognostic model for ESCC. We integrated autophagy-related genes (ARGs) with ESCC transcriptomic data to construct a robust 4-ARG model with multi-cohort validation (comprising NBEA, CLOCK, NLRX1, and MAGEA3) via stepwise multivariate Cox regression. Significant survival differences were observed between high- and low-risk groups stratified by the model (p < 0.001). Additionally, the signature correlated significantly with the immune microenvironment and predicted patients’ responses to immunotherapy. In an in-house ESCC cohort (n = 14), NLRX1 was verified as a reliable prognostic factor for disease-free survival (p = 0.043). Our findings demonstrate that the 4-ARG model enables ESCC prognosis prediction and identifies NLRX1 as a stable biomarker, providing a theoretical basis for personalized risk stratification and therapeutic decision-making in ESCC patients.

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.