Eight key long non-coding RNAs predict hepatitis virus positive hepatocellular carcinoma as prognostic targets

Abstract

BACKGROUNDHepatitis B virus, together with hepatitis C virus, has been recognized as the leading causes of hepatocellular carcinoma (HCC). Long non-coding RNAs (lncRNAs) have been suggested in increasing studies to be the potential prognostic factors for HCC. However, the role of combined application of lncRNAs in estimating overall survival (OS) for hepatitis virus positive HCC (VHCC) is uncertain.AIMTo construct an lncRNA signature related to the OS of VHCC patients to enhance the accuracy of prognosis prediction.METHODSThe expression patterns of lncRNAs, as well as related clinical data were collected from 149 VHCC patients from The Cancer Genome Atlas database. The R package was adopted to obtain the differentially expressed lncRNAs (DElncRNAs). LncRNAs significantly associated with OS were screened by means of univariate Cox regression analysis, so as to construct a least absolute shrinkage and selection operator (LASSO) model. Subsequently, the constructed lncRNA signature was developed and validated. Afterwards, the prognostic nomogram was established, which combined the as-established lncRNA signature as well as the clinical features. Meanwhile, subgroup analysis stratified by the virus type was also performed. Finally, the above-mentioned lncRNAs were enriched to corresponding pathways according to the markedly co-expressed genes.RESULTSA total of 1420 DElncRNAs were identified, among which 406 were significant in univariate Cox regression analysis. LASSO regression confirmed 8 out of the 406 lncRNAs, including AC005722.2, AC107959.3, AL353803.1, AL589182.1, AP000844.2, AP002478.1, FLJ36000, and NPSR1-AS1. Then, the prognostic risk score was calculated. Our results displayed a significant association between the risk model and the OS of VHCC [hazard ratio = 1.94, 95% confidence interval (CI): 1.61-2.34, log-rank P = 2e-10]. The inference tree suggested that the established lncRNA signature was useful in the risk stratification of VHCC. Furthermore, a nomogram was plotted, and the concordance index of internal validation was 0.763 (95%CI: 0.700-0.826). Moreover, the subgroup analysis regarding etiology confirmed this risk model. In addition, the Wnt signaling pathway, angiogenesis, the p53 pathway, and the PI3 kinase pathway were the remarkably enriched pathways.CONCLUSIONAn eight-lncRNA signature has been established to predict the prognosis for VHCC, which contributes to providing a novel foundation for the targeted therapy of VHCC.

Identification and Validation of Immune-Related LncRNA Prognostic Signature for Lung Adenocarcinoma

“…By constructing ceRNA networks in hepatocellular carcinoma, AP002478.1 was screened for many times as a potential prognostic marker (Liao X. et al, 2019;Yue et al, 2019). In hepatitis virus-positive hepatocellular carcinoma (Huang et al, 2019) and Helicobacter pylori-positive gastric cancer (Liu et al, 2019), AP002478.1 was also screened and found to be a prognostic marker to participate in the construction of prognostic models. In addition, AP002478.1 in the models constructed in the above studies is a negative prognostic factor, which is consistent with the results of this study.…”

Aberrant expression of WDR4 affects the clinical significance of cancer immunity in pan-cancer

“…Pan-cancer analysis reveals the similarities and differences between the genomes and cell changes of numerous carcinoma types that can distinguish several mutual characteristics or heterogeneities in crucial biological processes [ 3 ]. Pan-cancer analysis resources, such as The Cancer Genome Atlas (TCGA), have been used for exome, transcriptome, and DNA methylome data analysis to draw a comprehensive picture of commonalities, differences, and emerging themes among tumour types [ 6 – 8 ]. Pan-cancer analysis has been applied to identify pathway genes, which permits the acquisition of a wide-range of in-depth knowledge of the molecular mechanisms linked to malignancy [ 9 – 11 ].…”

Identifying Dendritic Cell–Related Genes Through a Co-Expression Network to Construct a 12-Gene Risk-Scoring Model for Predicting Hepatocellular Carcinoma Prognosis

“…High-level heterogeneity of HCC adds to the difficulty in predicting prognosis of HCC ( Huang et al, 2019 ). Immune-related parameters have been reported to predict the prognosis of patients with HCC, elucidating that the significance of immune status for determining the outcomes of HCC ( Long et al, 2019 ).…”