The development of a novel signature based on the m6A RNA methylation regulator-related ceRNA network to predict prognosis and therapy response in sarcomas - Li, Huling; Lin, Dandan; Wang, Xiaoyan; Feng, Zhiwei; Zhang, Jing; Wang, Kai (Frontiers in Genetics, 2022)
Background: N6-methyladenosine-related noncoding RNAs (m6A-related lncRNAs and miRNAs) are closely related to the development of cancer. However, the gene signature and prognostic value of m6A regulators and m6A-associated RNAs in regulating sarcoma (SARC) development and progres-sion remain largely unexplored. Methods: We obtained expression data for RNA sequencing (RNA-seq) and miRNAs of SARC from The Cancer Genome Atlas (TCGA) datasets. Correlation analysis and two target gene prediction databases were used to deduce m6A-related miRNAs and lncRNAs, and the Cytoscape soft-ware was used to construct ceRNA regulating networks. Based on univariate Cox regression and least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, an m6A-associated RNAs risk signature (m6Ascore) model was established. Prognostic differences between subgroups were explored using Kaplan-Meier (K-M) analysis. Risk score-related biological phenotypes were analyzed in terms of functional enrichment, tumor immune signature, and tumor mutation signature. Finally, potential immunotherapy features and drug sensitivity predictions for this model were also discussed. Results: The risk score were obtained based on RP11-283I3.6, hsa-miR-455-3p and CBLL1. Patients were divided into two risk groups by risk score, with patients in the low-risk group having longer overall survival (OS) than those in the high-risk group. The receiver operating characteristic (ROC) curves indicated that risk characteristics per-formed well in predicting the prognosis of patients with SARC. In addition, lower m6A score was also positively correlated with the abundance of immune cells such as activated monocytes and mast cells, and several immune check-point genes were highly expressed in the low m6A score group. According to our analysis, lower m6A scores may lead to better immunotherapy response and OS outcomes. The risk signature was significantly associated with the chemosensitivity of SARC. Finally, a nomogram was con-structed to predict the OS in patients with SARC. The concordance index (C-index) for the nomogram was 0.744 (95% CI: 0.707-0.784). The decision curve analysis (DCA), calibration plot and ROC curve all showed that this nomogram had good predictive performance. Conclusions: This m6Ascore risk model based on m6A RNA methylation regulator-related RNAs may be promising for clinical prediction of prognosis and might contain potential biomarkers for treatment response prediction for SARC patients.