Comprehensive analysis of an immune-related ceRNA network in identifying a novel lncRNA signature as a prognostic biomarker for hepatocellular carcinoma

Abstract

The function of competitive endogenous RNA (ceRNA) network in the immune regulation of hepatocellular carcinoma (HCC) is unclear. Our study aimed to construct an immune-related ceRNA network and develop an immune-related long noncoding RNA (lncRNA) signature to assess the prognosis of HCC patients and to optimize the treatment methods. We firstly constructed a ceRNA regulatory network for HCC using differentially expressed lncRNAs, mRNAs and microRNAs (miRNAs) from the Cancer Genome Atlas. A signature was constructed by 11 immune-related prognostic lncRNAs from the ceRNA network. The survival analysis and receiver operating characteristic analysis validated the reliability of the signature. Multivariate Cox regression analysis revealed that the signature could act an independent prognostic indicator. This signature also showed high association with immune cell infiltration and immune check blockades. LINC00491 was identified as the hub lncRNA in the signature. In vitro and in vivo evidence demonstrated that silencing of LINC00491 significantly inhibited HCC growth. Finally, 59 lncRNAs, 21 miRNAs, and 26 mRNAs were obtained to build the immune-related ceRNA network for HCC. In conclusion, our novel immune-related lncRNA prognostic signature and the immune-related ceRNA network might provide in-depth insights into tumor-immune interaction of HCC and promote better individual treatment strategies in HCC patients.

Identification of a pyroptosis-related lncRNA risk model for predicting prognosis and immune response in colon adenocarcinoma

“…The expression level of ICB-related genes was closely involved with the outcome of immunotherapy. Therefore, the Spearman correlation between the 11 ICB-related genes and risk score was analyzed [ 25 ].…”

Prognostic Roles of ceRNA Network-Based Signatures in Gastrointestinal Cancers

“…Among them, lncRNA was the most reported type, so the present study takes it as an example to introduce signature construction and verification methods. First, the prognostic value of lncRNAs involved in the cancer-specific ceRNA network can be evaluated by univariate Cox regression analysis of the association between lncRNA expression level and patient survival time ( 69 ). Then, lncRNA-related prognostic signature was commonly established by performing LASSO Cox regression analysis or multivariate Cox regression analysis ( 70 ).…”

“…Based on GSEA results, Chen et al. ( 69 ) found that the constructed eleven-lncRNA prognostic signature was involved in immune-related processes of hepatocellular carcinoma.…”

“…Furthermore, given the close link between immune and cancer pathogenesis, single-sample Gene Set Enrichment Analysis (ssGSEA) could be conducted to investigate the relationship between prognostic signature and immune status by calculating infiltration scores of distinct immune cell types based on the abundance of immune-related marker genes ( Figure 2D ) ( 75 ). Besides, immune infiltration correlation analyses, such as correlation between signature-based risk score and immune score, correlation between prognostic gene expression level and immune cell infiltration, and correlation between signature-based risk score and immune checkpoint inhibitor expression level, can be used to investigate the biological role of the established signature ( Figure 2D ) ( 43 , 69 , 76 ).…”

Comprehensive analysis of a ceRNA network associated with TP53 mutations in liver hepatocellular carcinoma

“…Increasing evidence have shown that the ceRNA crosstalk participates in indispensable regulatory processes and its perturbation will scramble the balance of cell regulatory system, resulting in disease initiation and tumor occurrence. In HCC, ceRNA networks aided the prioritization of RNA biomarkers including miRNA 12 and lncRNA 13 by comparing normal controls with tumor patients. As previously mentioned, the regulatory peculiarity of TP53 granted its capability in orchestrating complicated biological processes.…”