Identification of Potential Prognostic Long Non-Coding RNA Biomarkers for Predicting Survival in Patients with Hepatocellular Carcinoma

Abstract

Background/Aims: The aim of the current study was to identify potential prognostic long non-coding RNA (lncRNA) biomarkers for predicting survival in patients with hepatocellular carcinoma (HCC) using The Cancer Genome Atlas (TCGA) dataset and bioinformatics analysis. Methods: RNA sequencing and clinical data of HCC patients from TCGA were used for prognostic association assessment by univariate Cox analysis. A prognostic signature was built using stepwise multivariable Cox analysis, and a comprehensive analysis was performed to evaluate its prognostic value. The prognostic signature was further evaluated by functional assessment and bioinformatics analysis. Results: Thirteen differentially expressed lncRNAs (DELs) were identified and used to construct a single prognostic signature. Patients with high risk scores showed a significantly increased risk of death (adjusted P < 0.0001, adjusted hazard ratio = 3.522, 95% confidence interval = 2.307–5.376). In the time-dependent receiver operating characteristic analysis, the prognostic signature performed well for HCC survival prediction with an area under curve of 0.809, 0.782 and 0.79 for 1-, 3- and 5-year survival, respectively. Comprehensive survival analysis of the 13-DEL prognostic signature suggested that it serves as an independent factor in HCC, showing a better performance for prognosis prediction than traditional clinical indicators. Functional assessment and bioinformatics analysis suggested that the prognostic signature was associated with the cell cycle and peroxisome proliferator-activated receptor signaling pathway. Conclusions: The novel lncRNA expression signature identified in the present study may be a potential biomarker for predicting the prognosis of HCC patients.

Risk scoring based on expression of long non‑coding RNAs can effectively predict survival in hepatocellular carcinoma patients with or without fibrosis

“…Several previous studies have also constructed risk scoring systems to predict the prognosis of patients with HCC (24)(25)(26)(27)(28)(29)(30)(31). However, those risk scoring systems were based on DElncRNAs between HCC and normal samples, while the risk scoring systems in the present study are based on the DElncRNAs between HCC Table IX.…”

Identify potential clinical significance of long noncoding RNA forkhead box P4 antisense RNA 1 in patients with early stage pancreatic ductal adenocarcinoma

“…In this study, we used the Pearson correlation coefficient ( r ) to screen and identify FOXP4‐AS1‐related PCGs. Protein coding genes with a P < .05 and | r | > .2 were identified as FOXP4‐AS1‐related PCGs . Functional evaluation of these FOXP4‐AS1‐related PCGs was done by means of the Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8 .…”

“…Protein coding genes with a P < .05 and |r| > .2 were identified as FOXP4-AS1related PCGs. 17,18 Functional evaluation of these FOXP4-AS1-related PCGs was done by means of the Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8. 19,20 Biological Networks Gene Ontology tool (BiNGO) 21 was used to reveal the biological function of FOXP4-AS1 in PDAC tumor tissues.…”

A TP53 -associated gene signature for prediction of prognosis and therapeutic responses in lung squamous cell carcinoma

“…in which N represents the number of prognostic genes, Exp i represents the expression of gene i profile and C i represents the estimated regression coefficient of gene i determined by multivariable Cox regression analysis. [26][27][28] Patients with LUSC with available survival data were separated into high-and low-risk groups using the median score as a cutoff. Survival analysis was performed using the Kaplan-Meier method and the log-rank test was applied to evaluate the statistical significance of the differences.…”