m6A-related lncRNAs predict prognosis and indicate cell cycle in gastric cancer - Wan, Dong; He, Lingnan; Guo, Cheng; Zhong, Zishao; Yan, Xiaohan; Cao, Jia; Xu, Qinwei; Zhang, Haibin; Duan, Bensong (Frontiers in Genetics, 2023)
Abstract Background: N6-methyladenosine (m6A) modification is a common epigenetic methylation modification of RNA, which plays an important role in gastric carcinogenesis and progression by regulating long non-coding RNA (lncRNA). This study is aimed to investigate the potential prognostic signatures of m6A -related lncRNAs in STAD. Methods: The m6A-related lncRNAs with the most significant impact on gastric cancer prognosis in the TCGA database were identified by bioinformatics and machine learning methods. The m6A-related lncRNA prognostic model (m6A-LPS) and nomogram was constructed by Cox regression analysis with the minimum absolute contraction and selection operator (LASSO) algorithm. The functional enrichment analysis of m6A-related lncRNAs was also investigated. The miRTarBase, miRDB and TargetScan databases were utilized to establish a prognosis-related network of competing endogenous RNA (ceRNA) by bioinformatics methods. The correlation of AL391152.1 expressions and cell cycle were experimentally testified by qRT-PCR and flow cytometry. Results: In total, 697 lncRNAs that were identified as m6A-related lncRNAs in GC samples. The survival analysis showed that 18 lncRNAs demonstrated prognostic values. A risk model with 11 lncRNAs was established by Lasso Cox regression, and can predict the prognosis of GC patients. Cox regression analysis and ROC curve indicated that this lncRNA prediction model was an independent risk factor for survival rates. Functional enrichment analysis and ceRNA network revealed that the nomogram was notably associated with cell cycle. qRT-PCR and flow cytometry revealed that downregulation of GC m6A-related lncRNA AL391152.1 could decrease cyclins expression in SGC7901 cells. Conclusions: A m6A-related lncRNAs prognostic model was established in this study, which can be applied to predict prognosis and cell cycle in gastric cancer.