Identification of Long Noncoding RNA Biomarkers for Hepatocellular Carcinoma Using Single-Sample Networks.

Abstract

OBJECTIVE:

Many studies have found that long noncoding RNAs (lncRNAs) are differentially expressed in hepatocellular carcinoma (HCC) and closely associated with the occurrence and prognosis of HCC. Since patients with HCC are usually diagnosed in late stages, more effective biomarkers for early diagnosis and prognostic prediction are in urgent need.

METHODS:

The RNA-seq data of liver hepatocellular carcinoma (LIHC) were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs and mRNAs were obtained using the edgeR package. The single-sample networks of the 371 tumor samples were constructed to identify the candidate lncRNA biomarkers. Univariate Cox regression analysis was performed to further select the potential lncRNA biomarkers. By multivariate Cox regression analysis, a 3-lncRNA-based risk score model was established on the training set. Then, the survival prediction ability of the 3-lncRNA-based risk score model was evaluated on the testing set and the entire set. Function enrichment analyses were performed using Metascape.

RESULTS:

Three lncRNAs (RP11-150O12.3, RP11-187E13.1, and RP13-143G15.4) were identified as the potential lncRNA biomarkers for LIHC. The 3-lncRNA-based risk model had a good survival prediction ability for the patients with LIHC. Multivariate Cox regression analysis proved that the 3-lncRNA-based risk score was an independent predictor for the survival prediction of patients with LIHC. Function enrichment analysis indicated that the three lncRNAs may be associated with LIHC via their involvement in many known cancer-associated biological functions.

CONCLUSION:

This study could provide novel insights to identify lncRNA biomarkers for LIHC at a molecular network level.

• Authors: Xiaoqing Yu, Jingsong Zhang, Rui Yang, Chun Li
• Journal: BioMed research international (Biomed Res Int) Vol. 2020 Pg. 8579651 ( 2020) ISSN: 2314-6141 [Electronic] United States
• PMID: 33299877 (Publication Type: Journal Article)
• Copyright: Copyright © 2020 Xiaoqing Yu et al.

Chemical References

• Biomarkers
• Biomarkers, Tumor
• RNA, Long Noncoding

Topics

• Algorithms
• Biomarkers
• Biomarkers, Tumor (genetics)
• Carcinoma, Hepatocellular (genetics, mortality)
• Female
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic
• Humans
• Kaplan-Meier Estimate
• Liver Neoplasms (genetics, mortality)
• Male
• Prognosis
• Proportional Hazards Models
• RNA, Long Noncoding (genetics)
• RNA-Seq
• ROC Curve
• Regression Analysis
• Risk