Aging
Navigate
Submit an Article
Research Paper|Volume 12, Issue 13|pp 13502—13517

Weighted correlation gene network analysis reveals a new stemness index-related survival model for prognostic prediction in hepatocellular carcinoma

Qiujing Zhang1, Jia Wang1,2, Menghan Liu3, Qingqing Zhu1, Qiang Li4, Chao Xie1, Congcong Han1, Yali Wang1, Min Gao5, Jie Liu1
  • 1Department of Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, Shandong, China
  • 2Department of Oncology, Zibo Maternal and Child Health Hospital, Zibo 255000, Shandong, China
  • 3Basic Medicine College, Shandong First Medical University, Taian 271016, Shandong, China
  • 4Department of Oncology, Mengyin County Hospital, Linyi 276299, Shandong, China
  • 5Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, Shandong, China
* Equal contribution
Received: February 13, 2020Accepted: May 27, 2020Published: July 9, 2020
https://doi.org/10.18632/aging.103454

Copyright © 2020 Zhang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

In this study, we constructed a new survival model using mRNA expression-based stemness index (mRNAsi) for prognostic prediction in hepatocellular carcinoma (HCC). Weighted correlation network analysis (WGCNA) of HCC transcriptome data (374 HCC and 50 normal liver tissue samples) from the TCGA database revealed 7498 differentially expressed genes (DEGs) that clustered into seven gene modules. LASSO regression analysis of the top two gene modules identified ANGPT2, EMCN, GLDN, USHBP1 and ZNF532 as the top five mRNAsi-related genes. We constructed our survival model with these five genes and tested its performance using 243 HCC and 202 normal liver samples from the ICGC database. Kaplan-Meier survival curve and receive operating characteristic curve analyses showed that the survival model accurately predicted the prognosis and survival of high- and low-risk HCC patients with high sensitivity and specificity. The expression of these five genes was significantly higher in the HCC tissues from the TCGA, ICGC, and GEO datasets (GSE25097 and GSE14520) than in normal liver tissues. These findings demonstrate that a new survival model derived from five strongly correlating mRNAsi-related genes provides highly accurate prognoses for HCC patients.