Abstract
Background: Increasing evidence supports that competing endogenous RNAs (ceRNAs) and tumor immune infiltration act as pivotal players in tumor progression of hepatocellular carcinoma (HCC). Nonetheless, comprehensive analysis focusing on ceRNAs and immune infiltration in HCC is lacking.Methods: RNA and miRNA sequencing information, corresponding clinical annotation, and mutation data of HCC downloaded from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) project were employed to identify significant differentially expressed mRNAs (DEMs), miRNAs (DEMis), and lncRNAs (DELs) to establish a ceRNA regulatory network. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO) enrichment pathways were analyzed to functionally annotate these DEMs. A multigene-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 were 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 coexpression network will facilitate prognostic prediction, delineate complexity of TIME, and contribute insight into precision therapy for HCC.
Highlights
Primary liver cancer is considered as one of the most aggressive and prevalent malignancies with increasing mortality globally (Bray et al, 2018; Forner et al, 2018; Yang et al, 2019)
RNA and miRNA sequencing information, corresponding clinical annotation, and mutation data of hepatocellular carcinoma (HCC) downloaded from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) project were employed to identify significant differentially expressed messenger RNAs (mRNAs) (DEMs), miRNAs (DEMis), and long non-coding RNAs (lncRNAs) (DELs) to establish a competing endogenous RNAs (ceRNAs) regulatory network
DElncRNAs, DEmiRNAs, and differentially expressed mRNAs (DEmRNAs) were analyzed between 374 HCC tissues and 50 adjacent normal liver samples in the TCGA database
Summary
Primary liver cancer is considered as one of the most aggressive and prevalent malignancies with increasing mortality globally (Bray et al, 2018; Forner et al, 2018; Yang et al, 2019). Based on conventional histopathological classification, hepatocellular carcinoma (HCC) almost take part in 75–85% of primary liver cancer patients (Bray et al, 2018) Such underlying pathogenic elements for HCC such as infections of aflatoxin exposure, hepatitis virus, heavy alcohol intake, type 2 diabetes, and obesity served as crucial players in hepatocarcinogenesis (Yang et al, 2019; Xu et al, 2021c). Since HCC has considerably high heterogeneity and sophisticated diversity of etiology, tumor–node–metastasis (TNM) staging has been difficult in the precise prognostic prediction of HCC patients (Edge and Compton, 2010; Marano et al, 2015) It is of great urgency, to construct a novel and reliable predictive indicator for clinical outcome prediction and therapeutic efficacy estimation, further advancing tailored strategy. Comprehensive analysis focusing on ceRNAs and immune infiltration in HCC is lacking
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