Identification and validation of a novel apoptosis-related prognostic risk score model for lung adenocarcinoma

TL;DR

This study identifies a new 10-gene model based on apoptosis-related genes to predict survival outcomes in lung adenocarcinoma patients.

Contribution

A novel 10-gene prognostic risk score model for lung adenocarcinoma based on apoptosis-related genes is developed and validated.

Findings

130 differentially expressed apoptosis-related genes were identified, splitting LUAD cases into two subtypes with distinct survival outcomes.

A 10-gene signature showed significant survival differences between high- and low-risk groups in both TCGA and GEO datasets.

ARGs were enriched in cancer-related pathways, and high-risk groups exhibited lower immune status.

Abstract

The prognostic roles of apoptosis-related genes (ARGs) in lung adenocarcinoma (LUAD) have not been fully elucidated. In this study, differentially expressed genes (DEGs) associated with apoptosis and the hub genes were further identified. The prognostic values of the ARGs were evaluated using the LASSO Cox regression method. Prognostic values were determined using Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves in the TCGA and GEO datasets. The correlations, mutation data, and protein expression of the 10 ARGs predictive models were also analyzed. We identified 130 differentially expressed ARGs. DEGs were used to split LUAD cases into two subtypes whose overall survival (OS) were significantly different (P = 0.025). We developed a novel 10-gene signature using LASSO Cox regression. In both TCGA and GEO datasets, the results of the K-M curve and log-rank test showed significant difference in the survival rate of patients in the high-risk group and low-risk group (P < 0.0001). According to the GO and KEGG analyses, ARGs were enriched in cancer-related terms. In both cohorts, the immune status of the high-risk group was significantly lower than that of the low-risk group. Based on the differential expression of the ARGs, we established a new risk model to predict the prognosis of patients with LUAD.

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