Identification and Validation of a Prognostic Prediction Model of m6A Regulator-Related LncRNAs in Hepatocellular Carcinoma - Jin, Chen; Li, Rui; Deng, Tuo; Li, Jialiang; Yang, Yan; Li, Haoqi; Chen, Kaiyu; Xiong, Huihua; Chen, Gang; Wang, Yi (Frontiers in Molecular Biosciences, 2021)
Hepatocellular Carcinoma (HCC) is a highly invasive malignancy and prone to recurrence, with a low 5-year survival rate. Long non-coding RNA (lncRNA) plays a vital role in the occurrence and development of HCC. Meanwhile, N6-methyladenosine methylation (m6A) is the most common modification that affects cancer development. Here, we used the transcriptome of m6A regulators and lncRNA along with corresponding complete clinical information of HCC patients obtained from The Cancer Genome Atlas (TCGA) to explore the role of m6A regulators-related lncRNA (m6ARlnc) as a prognostic biomarker for HCC patients. The prognostic m6ARlnc were selected by Pearson correlation analysis and univariate Cox regression analysis. Moreover, three clusters were obtained by consensus clustering analysis and further investigated for differences in immune infiltration, immune microenvironment and prognosis. Subsequently, nine m6ARlncs were identified by Lasso-Cox regression analysis to construct the prognostic signature (m6A-9LPS) for the HCC patients in training cohort (n=226). Based on this m6A-9LPS, the risk score of each case was calculated. Then, HCC patients were divided into high- and low-risk subgroups by the cut-off value set by X-tile software. The m6A-9LPS showed a strong prognosis prediction ability in the validation cohort (n=116) and the whole cohort (n=342) and even clinicopathological stratified survival analysis. Combined with the risk score and the clinical characteristics, we established a nomogram for the overall survival (OS) prediction of HCC patients. To further understand the mechanism underlying the m6A-9LPS classified prognosis difference, KEGG and GO enrichment analysis, chemotherapeutic agents sensibility, and immune checkpoint expression level were investigated. Taken together, m6A-9LPS can be used as a precise prediction model for the prognosis of HCC patients which helps the individualized treatment in HCC.