Prognostic analysis of m6A-related lncRNAs as potential biomarkers in intrahepatic cholangiocarcinom - Shi, Guodong; Wang, Junjie; Wang, Weiqi; Chen, Min; Liu, Xiaoxuan; Zheng, Yufan; Fu, Yi; Wang, Minghua; Zhang, Xiaojie (Frontiers in Genetics, 2022)
Intrahepatic cholangiocarcinoma(iCCA) is a primary malignant tumor of the liver. iCCA patients have no obvious symptoms in the early stage, and the postoperative survival is poor. Therefore, the establishment of an iCCA prognostic prediction model to carry out refined management of iCCA patients is expected to improve the survival of the iCCA patient population. N6-methyladenosine (m6A) is the most abundant internal modification in mRNAs and lncRNAs in most eukaryotes and plays a key role in a variety of cancers by multiple mechanisms. In this paper, we analyzed the expression profiling data of patients from iCCA tissues and paracancerous tissues in The Cancer Genome Atlas (TCGA) database. Perl software was used to separate M6A-related genes and lncRNAs from expression matrix files obtained from the TCGA database. The differentially expressed lncRNAs in the iCCA samples and the normal samples were screened out by differential analysis using the R package limma, and the m6A-related lncRNAs were further screened by Pearson correlation analysis. WGCNA clustering analysis constructs a random network to extract the module genes most related to iCCA, and take the intersection of differentially expressed lncRNAs related to m6A. Univariate Cox screening was performed for the intersection lncRNAs that had significant influence on the prognosis of iCCA patients, and further screening was performed by LASSO method and multivariate Cox regression analysis. Risk model was constructed and prognostic ability was evaluated according to risk score. In conclusion, we identified four m6A-related lncRNAs with potential prognostic value in iCCA, and established a novel m6A-related lncRNA-based prognostic model, which can be used as an independent prognostic factor to predict the prognosis of clinical patients.