An N6-methyladenosine and target genes-based study on subtypes and prognosis of lung adenocarcinoma

Purpose: Lung adenocarcinoma (LUAD) is a highly lethal subtype of primary lung cancer with a poor prognosis. N6-methyladenosine (m(6)A), the most predominant form of RNA modification, regulates biological processes and has critical prognostic implications for LUAD. Our study aimed to mine potential target genes of m(6)A regulators to explore their biological significance in subtyping LUAD and predicting survival. Methods: Using gene expression data from TCGA database, candidate target genes of m(6)A were predicted from differentially expressed genes (DEGs) in tumor based on M(6)A2 Target database. The survival-related target DEGs identified by Cox-regression analysis was used for consensus clustering analysis to subtype LUAD. Uni-and multi-variable Cox regression analysis and LASSO Cox-PH regression analysis were used to select the optimal prognostic genes for constructing prognostic score (PS) model. Nomogram encompassing PS score and independent prognostic factors was built to predict 3-year and 5-year survival probability. Results: We obtained 2429 DEGs in tumor tissue, within which, 1267 were predicted to m(6)A target genes. A prognostic m(6)A-DEGs network of 224 survival-related target DEGs was established. We classified LUAD into 2 subtypes, which were significantly different in OS time, clinicopathological characteristics, and fractions of 12 immune cell types. A PS model of five genes (C1QTNF6, THSD1, GRIK2, E2F7 and SLCO1B3) successfully split the training set or an independent GEO dataset into two subgroups with significantly different OS time (p < 0.001, AUC = 0.723; p = 0.017, AUC = 0.705).A nomogram model combining PS status, pathologic stage, and recurrence was built, showing good performance in predicting 3-year and 5-year survival probability (C-index = 0.708, 0.723, p-value = 0). Conclusion: Using candidate m(6)A target genes, we obtained two molecular subtypes and designed a reliable five-gene PS score model for survival prediction in LUAD.