The Prediction of Survival in Hepatocellular Carcinoma Based on A Four Long Non-coding RNAs Expression Signature

Abstract

Prognostic stratification in hepatocellular carcinoma (HCC) patients is still challenging. Long non-coding RNAs (lncRNAs) have been proven to play a crucial role in tumorigenesis and progression of cancers. The aim of this study is to develop a useful prognostic index based on lncRNA signature to identify patients at high risk of disease progression. We obtained lncRNA expression profiles from three publicly available datasets from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). By the risk scoring method, we built an individualized four-lncRNA signature (HCCLnc-4) to predict survival of HCC patients in the discovery set (ROC curve, AUC: 0.83, 95% CI: 0.65-1.00, P < 0.05, Kaplan-Meier analysis and log-rank test, P < 0.01). Similar prognostic value of HCCLnc-4 has been further verified in two other independent sets. Stratified analysis and multivariate Cox regression analysis suggested the independence of HCCLnc-4 for prediction of HCC patient survival from traditional clinicopathological factors. Area under curve (AUC) analysis suggested that HCCLnc-4 could compete sufficiently with, or might be even better than classical pathological staging systems to predict HCC patient prognosis in the same data sets. Functional analysis and network analysis suggested the potential implication of lncRNA biomarkers. Our study developed and validated the lncRNA prognostic index of HCC patients, warranting further clinical evaluation and preventive interventions.

Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

“…(B) Disease-free survival (DFS) for the high- and low-risk patients. (C) The ROC analysis of OS outcomes for the five-AR-lncRNA signature (TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, CYTOR) by us; five-autophagy-related gene (ARG) signature (HDAC1, RHEB, ATIC, SPNS1, and SQSTM1) by Huo et al ( 2020 ); four-lncRNA signature (ENSG00000234608, ENSG00000242086, ENSG00000273032, ENSG00000228463) by Yang et al ( 2020 ) and four-lncRNA signature (RP11-322E11.5, RP11-150O12.3, AC093609.1, CTC-297N7.9) by Wang et al ( 2017 ) (D) Kaplan–Meier curves of patients stratified by different clinicopathological traits. Hepatitis B virus, HBV; hepatitis C virus, HCV.…”

“…As shown in Figure 1 , in the present study, after applying weighted correlation network analysis (WGCNA) and several kinds of Cox regression analysis on the database of HCC patients in The Cancer Genome Atlas (TCGA), five AR lncRNAs (TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) were identified to construct a prognostic signature for the overall survival (OS) outcomes of HCC patients. The sensitivity and specificity of the five-AR-lncRNA signature surpassed three recently published prognostic signatures for HCC (Wang et al, 2017 ; Huo et al, 2020 ; Yang et al, 2020 ). Furthermore, significant differences were found in the therapeutic outcomes, including immunotherapy and chemotherapy responses, between the high- and low-risk groups.…”

“…Area under the curve (AUC) of 3-year OS outcomes based on the time-dependent ROC curves was used to compare the prediction accuracy of our newly identified AR-lncRNA signature with other three recently published signatures. The concordance index (C-index) was calculated to compare the prediction accuracy of prognostic signatures (Wang et al, 2017 ; Huo et al, 2020 ; Yang et al, 2020 ) by “survcomp” package (Schroder et al, 2011 ).…”

Development of a Prognostic Gene Signature For Hepatocellular Carcinoma

“…It appears that such individual biomarkers are limited because they can be in uenced by many factors, rendering their predictive ability unstable; thus, other recent studies have shown that combined analysis of a panel of biomarkers is the most promising approach to change clinical management (40,41). With respect to HCC, since the accuracy of various prognostic models still varies, the eld currently warrants further research (19)(20)(21). In the present study, the LASSO Cox regression model was used to select the most useful prognostic markers from many genes related to HCC.…”

“…developed a four-long non-coding RNA signature as a novel candidate biomarker for prognosis in HCC patients (AUC: 0.689; 95% CI: 0.617-0.761; P < 0.01) (21). Nevertheless, due to the varying quality of prediction models, the construction of an optimal prognostic model remains a hot topic.…”