Screening and identifying a novel M-MDSCs-related gene signature for predicting prognostic risk and immunotherapeutic responses in patients with lung adenocarcinoma - Wang, Geng-Chong; Zhou, Mi; Zhang, Yan; Cai, Hua-Man; Chiang, Seok-Theng; Chen, Qi; Han, Tian-Zhen; Li, Rong-Xiu (Frontiers in Genetics, 2023)
Background: Lung adenocarcinoma (LUAD) is a type of non-small cell lung cancer (NSCLC) and shows intratumoral heterogeneity, a highly complex phenomena that known to be a challenge during cancer treatment. Considering the key role of monocytic myeloid-derived suppressor cells (M-MDSCs) in the tumor microenvironment (TME), we aimed to build a prognostic risk model using M-MDSC-related genes. Methods: M-MDSCs-related genes were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Univariate survival analysis and random forest algorithm were utilized to obtain candidate genes. A least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted to construct the risk model and the LUAD patients were classified into high-risk and low-risk groups according to the median risk score. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis along with immune cell infiltration estimation and tumor mutation burden (TMB) analysis were performed for the two risk groups. Nomogram was built following univariate and multivariate Cox regression analyses. Results: Based on the TCGA training set, 8 prognostic signature genes were identified and then validated in the GEO dataset. The two risk groups showed significant survival differences, where low-risk LUAD patients experienced better prognosis. Enrichment level of cell cycle-related genes and lower content of CD8 (+) T cells in the high-risk group, indicating an enhanced immunosuppressive ability relative to the low-risk group. The subtype of patients in both higher-TMB and low-risk groups showed the best prognosis and may be sensitive to immune-checkpoint-inhibitor (ICI) therapy. The nomogram demonstrated consistency with practical outcomes in predicting the survival rate over 1, 3, and 5 years. Conclusions: The risk model demonstrated good prognostic predictive ability and generalization effect. Understanding the mechanism underlying the differences between the high-risk and low-risk groups may provide theoretical support for immunotherapeutic strategies.