Abstract
BACKGROUND: Metabolism and stemness-related genes (msRGs) are critical in the development and progression of lung adenocarcinoma (LUAD). Nevertheless, reliable prognostic risk signatures derived from msRGs have yet to be established. METHODS: In this study, we downloaded and analyzed RNA-sequencing and clinical data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. We employed univariate and multivariate Cox regression analyses, along with least absolute shrinkage and selection operator (LASSO) regression analysis, to identify msRGs that are linked to the prognosis of LUAD and to develop the prognostic risk signature. The prognostic value was evaluated using Kaplan-Meier analysis and log-rank tests. We generated receiver operating characteristic (ROC) curves to evaluate the predictive capability of the prognostic signature. To estimate the relative proportions of infiltrating immune cells, we utilized the CIBERSORT algorithm and the MCPCOUNTER method. The prediction of the half-maximal inhibitory concentration (IC50) for commonly used chemotherapy drugs was conducted through ridge regression employing the "pRRophetic" R package. The validation of our analytical findings was performed through both in vivo and in vitro studies. RESULTS: A novel five-gene prognostic risk signature consisting of S100P, GPX2, PRC1, ARNTL2, and RGS20 was developed based on the msRGs. A risk score derived from this gene signature was utilized to stratify LUAD patients into high- and low-risk groups, with the former exhibiting significantly poorer overall survival (OS). A nomogram was constructed incorporating the risk score and other clinical characteristics, showcasing strong capabilities in estimating the OS rates for LUAD patients. Furthermore, we observed notable differences in the infiltration of various immune cell subtypes, as well as in responses to immunotherapy and chemotherapy, between the low-risk and high-risk groups. Results from gene set enrichment analysis (GSEA) and in vitro studies indicated that the prognostic signature gene ARNTL2 influenced the prognosis of LUAD patients, primarily through the activation of the PI3K/AKT/mTOR signaling pathway. CONCLUSIONS: Utilizing this gene signature for risk stratification could help with clinical treatment management and improve the prognosis of LUAD patients.