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Copyright (c) 2023 Xiaomin Wu, Yi Wang, Leilei Tao, Xiaojing Zhang, Li Wang, Yingchun Zhang, Xiaomin Zhang, Xiankai Wang, Jixian Yu
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The undersigned hereby assign all rights, included but not limited to copyright, for this manuscript to CMB Association upon its submission for consideration to publication on Cellular and Molecular Biology. The rights assigned include, but are not limited to, the sole and exclusive rights to license, sell, subsequently assign, derive, distribute, display and reproduce this manuscript, in whole or in part, in any format, electronic or otherwise, including those in existence at the time this agreement was signed. The authors hereby warrant that they have not granted or assigned, and shall not grant or assign, the aforementioned rights to any other person, firm, organization, or other entity. All rights are automatically restored to authors if this manuscript is not accepted for publication.Comprehensive molecular analyses of a 7-m6A-related lncRNAs signature for prognosis, tumor immunity and therapeutic effect in patients with hepatocellular carcinoma
Corresponding Author(s) : Jixian Yu
Cellular and Molecular Biology,
Vol. 70 No. 1: Issue 1
Abstract
Hepatocellular carcinoma is the most common form of liver tumor. m6A modification and noncoding RNA show indispensable roles in HCC. We sought to establish and verify an appropriate m6A-related long noncoding RNA prognostic tool for predicting hepatocellular carcinoma progression. We extracted the RNA expression levels and the clinicopathologic data from GTEx and TCGA databases. Multivariate Cox regression analysis and receiver operating characteristic curves were performed to test the model's predictive ability. We further built a nomogram for overall survival according to the risk score and clinical features. A competing endogenous RNA network and Gene Ontology assessment were implemented to identify related biological mechanisms and processes. By bioinformatics analysis, a risk model comprising GABPB1-AS1, AC025580.1, LINC01358, AC026356.1, AC009005.1, HCG15, and AC026368.1 was built to offer a prognostic prediction for hepatocellular carcinoma independently. The prognostic tool could better prognosticate hepatocellular carcinoma patients’ survival than other clinical characteristics. Then, a nomogram with risk score and clinical characteristics was created, which had strong power to calculate the survival probability in hepatocellular carcinoma. The immune-associated processes involving the differentially expressed genes between the two subgroups were displayed. Analyses of prognosis, clinicopathological characteristics, tumor mutation burden, immune checkpoint molecules, and drug response showed significant differences among the two risk subtypes, hinting that the model could appraise the efficacy of immunotherapy and chemotherapy. The tool can independently predict the prognosis in patients with hepatocellular carcinoma, which benefits drug selection in hepatocellular carcinoma patients.
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