A comprehensive analysis identified an autophagy-related risk model for predicting recurrence and immunotherapy response in stage I lung adenocarcinoma - Zheng, Hongmei; Fan, Songqing; Zang, Hongjing; Luo, Jiadi; Shu, Long; Peng, Jinwu (PeerJ, 2025)
Background Lung adenocarcinoma (LUAD) is characterized by early recurrence and poor prognosis. Autophagy is a double-edged sword in tumor development and anti-tumor therapy resistance. However, the prediction of relapse and therapeutic response in LUAD patients with stage I based on the signature of autophagy remains unclear. Methods Gene expression data were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. Autophagy-associated genes were extracted from the Human Autophagy Moderator Database. The autophagy score was established by Least Absolute Shrinkage and Selection Operator (LASSO) regression. Real-time PCR was used to detect gene expression of hub genes in LUAD patients. Protein-protein interaction (PPI) was analyzed to identify crucial genes. Gene set enrichment analysis (GSEA) was used to reveal the molecular features of patients. ESTIMATE algorithm was applied to estimate the tumor immune infiltration. TIDE score and Genomics of Drug Sensitivity in Cancer (GDSC) database were used to assess therapeutic response. Results We established an autophagy score based on 19 autophagy genes. Among these genes, MAP1LC3B played a crucial role in PPI network and was down-regulated in tumor tissues both in TCGA and local cohort. Receiver operating characteristic (ROC) curve showed that the risk model effectively predict RFS of stage I LUAD (area under the curve (AUC) at 1, 2, 3 years = 0.701, 0.836, and 0.818, respectively). Multivariate regression analysis indicated that the autophagy score was an independent predictor for relapse (P < 0.001, HR = 4.8, 95% CI [3.25–7.2]). The autophagy score also showed great predictive efficacy in the external validation GEO cohorts. GSEA revealed gene sets significantly enriched in immunity, cell cycle, and adhesion, etc. Meanwhile, we found the autophagy score was negatively related to KRAS mutation (P = 0.017) but positively associated with TP53 mutation (P = 6.4e−11). The autophagy score had a negative relationship with CD8+, CD4+ T cell, and dendritic cell, and positively correlated with immune checkpoint molecule CD276. Patients with a high autophagy score were sensitive to chemotherapy and targeted therapy, while resistant to immune checkpoint inhibitors. Conclusion We constructed an effective recurrence risk predictive model for stage I LUAD patients based on autophagy related genes. High autophagy score predicted a higher recurrence risk and suppressing tumor immune microenvironment.
Keywords: Lung adenocarcinoma; Autophagy; Recurrence; Tumor immune microenvironment