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Identification of an autophagy-related gene marker for predicting the prognosis in lung adenocarcinoma patients


 BackgroundLung adenocarcinoma (LUAD) is a tumor with high incidence rate and high mortality rate. Previous studies have found that autophagy plays a vital role in tumorigenesis and biological progression. The aim of our research is to screen and analyze autophagy-related genes (ATGs) using bioinformatics technology, then establish a gene expression model for predicting the prognosis of LUAD patients. MethodsDifferentially expressed ATGs in LUAD and normal tissues were screened by The Cancer Genome Atlas (TCGA) dataset. We applied Go and KEGG enrichment analysis of ATGs for identifying the relevant signaling pathways. Finally, the ATGs related with survival were assessed using univariate and multivariate COX regression analyses. This project was analyzed by R software. ResultsA total of 232 ATGs were obtained in LUAD. Subsequently, 30 ATGs were screened out as genes associated with prognosis. GO analysis revealed that the 30 differently expressed ATGs (DE-ATGs) were enriched in intrinsic apoptotic signaling pathway, macroautophagy, and neuron death. In addition, KEGG analysis revealed that the DE-ATGs were associated with autophagy (animal), ErbB signaling pathway and IL-17 signaling pathway. Then, the risk score model was established by the 8 ATGs (ATG4A, CCR2, MBTPS2, APOL1, ERO1A, SPHK1, ST13 and ITGA6), and LUAD patients were divided into high-risk and low-risk groups with the risk score. The risk score was significantly related to overall survival and prognosis by univariate and multivariate analysis (P < 0.001). The survival time of low-risk score patients was showed by the cumulative curve that was obviously longer than high-risk score patients (P< 0.001). Finally, the correlation of the autophagy-related risk characteristics and multiple clinical parameters was analyzed. ConclusionA prognostic signature and nomogram based on 8 ATGs was constructed. This research provides a new direction for the prognosis evaluation and guides the potential treatment strategies for LUAD.