AUTHOR=Xu Qianhui , Chen Shaohuai , Hu Yuanbo , Huang Wen 

TITLE=Prognostic Role of ceRNA Network in Immune Infiltration of Hepatocellular Carcinoma

JOURNAL=Frontiers in Genetics

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.739975

DOI=10.3389/fgene.2021.739975

ISSN=1664-8021

ABSTRACT=Background: Increasing evidence supports that ceRNAs (competitive endogenous RNAs) and tumor immune-infiltrating act as pivotal players in tumor progression of hepatocellular carcinoma (HCC). Nonetheless, comprehensive analysis focusing on ceRNAs and immune infiltration in HCC was lack. 
Methods: The RNA and miRNA sequencing information, corresponding clinical annotation and mutation data of HCC downloaded from TCGA-LIHC project were employed to identify significant differentially expressed mRNAs (DEMs), miRNAs (DEMis) and lncRNAs (DELs) to establish a ceRNA regulatory network. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO) enrichment pathways was analyzed to functionally annotate these DEMs. A multigenes-based risk signature was developed utilizing least absolute shrinkage and selection operator method (LASSO) algorithm. Moreover, survival analysis and receiver operating characteristic (ROC) analysis were applied for prognostic value validation. Seven algorithms (TIMER, XCELL, MCPcounter, QUANTISEQ, CIBERSORT, EPIC, and CIBERSORT-ABS) were utilized to characterize tumor immune microenvironment (TIME). Finally, the mutation data was analyzed by employing “maftools” package. 
Results: In total, 136 DELs, 128 DEMis, and 2,028 DEMs were recognized in HCC.  A specific lncRNA-miRNA-mRNA network consisting of 3 lncRNAs, 12 miRNAs and 21 mRNAs was established. A ceRNA-based prognostic signature was established to classify samples into two risk subgroups, which presented excellent prognostic performance. In additional, prognostic risk-clinical nomogram was delineated to assess risk of individual sample quantitatively. Besides, risk score was significantly associated with contexture of TIME and immunotherapeutic targets. Finally, potential interaction between risk score with tumor mutation burden (TMB) was revealed.
Conclusion: In this work, comprehensive analyses of ceRNAs co-expression network will facilitate prognostic prediction, delineating complexity of TIME, and contribute insight into precision therapy for HCC.