Classification of Hepatocellular Carcinoma Based on N6-Methylandenosine-Related lncRNAs Profiling

FRONTIERS IN MOLECULAR BIOSCIENCES(2022)

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Abstract
HCC is one of the most common types of malignancies worldwide and the fourth-leading cause of cancer deaths. Thus, there is an urgent need to search for novel targeted therapies in HCC. 186 m6a-related lncRNAs were screened for subsequent analysis. Two distinct m6A modification clusters were identified to be associated with the overall prognosis in TCGA-LIHC based on the m6A-related lncRNAs profiling, followed by univariate Cox regression analysis. In addition, four m6A-related lncRNAs prognostic signatures were developed and validated that could predict the OS of HCC patients, followed by univariate Cox regression, LASSO regression, and multivariate Cox regression analysis. Moreover, four m6A-related lncRNAs were identified to be related to HCC prognosis. ESTIMATE was used to evaluate the stromal score, immune score, ESTIMATE score, and tumor purity of each HCC sample. ssGSEA was performed to identify the enrichment levels of 29 immune signatures in each sample. Finally, quantitative real-time polymerase chain reaction shown that KDM4A-AS1, BACE1-AS, and NRAV expressions were upregulated in HCC patients. We proved that our m6A-related lncRNAs signature had powerful and robust ability for predicting OS of different HCC subgroups.
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Key words
hepatocellular carcinoma, m6A-related lncRNA, tumor microenvironment, classification, prognosis, biomarkers, machine learning
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