Research Paper Volume 13, Issue 5 pp 7481—7498
Identification and validation of a glycolysis-associated multiomics prognostic model for hepatocellular carcinoma
- 1 Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- 2 Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- 3 Department of Clinical Laboratory, Wenzhou People's Hospital, The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou, China
- 4 Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
- 5 Department of Oncology, The First School of Clinical Medicine, Wenzhou Medical University, Wenzhou, Zhejiang, China
Received: November 13, 2020 Accepted: January 14, 2021 Published: March 3, 2021
https://doi.org/10.18632/aging.202613How to Cite
Copyright: © 2021 Deng 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
Increased glycolysis has been reported as a major metabolic hallmark in many cancers, and is closely related to malignant behavior of tumors. However, the potential mechanism of glycolysis in hepatocellular carcinoma (HCC) and its prognostic value are not well understood. To address this, we investigated glycolysis-related gene expression data of patients with HCC from TCGA and ICGC. Patients were categorized into three different glycolysis-associated subgroups: Glycolysis-M, Glycolysis-H, and Glycolysis-L. We found that Glycolysis-H combined with Glycolysis-M (Glycolysis-H+M) subgroup was associated with poor overall survival and distinct cancer stem cell characteristics and immune infiltrate patterns. Additionally, multiomics-based analyses were conducted to evaluate genomic patterns of glycolysis subgroups, including their gene mutations, copy number variations, and RNA-sequencing data. Finally, a glycolysis-associated multiomics prognostic model (GMPM) consisting of 19 glycolysis-associated genes was developed. The capability of GMPM in categorizing patients with HCC into high- and low-risk groups was validated with independent HCC datasets. Finally, GMPM was confirmed as an independent risk factor for the prognosis of patients with HCC. We believe that our findings provide new insights into the mechanism of glycolysis and highlight the potential clinical value of GMPM in predicting the prognosis of patients with HCC.
Abbreviations
HCC: Hepatocellular carcinoma; TCGA: The Cancer Genome Atlas; ICGC: International Cancer Genome Consortium; GMPM: Glycolysis-associated multi-Omics prognostic model; CNV: Copy number variation; MOG-DEGs: Multi-omics glycolysis-associated differentially expressed genes; GO: Gene ontology; BP: Biological process; MF: Molecular function term; CC: Cell component; LCSCs: Liver cancer stem cells; KEGG: The Kyoto Encyclopedia of Genes and Genomes; OS: Overall survival; OCLR: One-class logistic regression; TMF: Tumor mutation frequency; FDR: The false discovery rate; DEGs: Differentially expressed genes; LASSO: The Least Absolute Shrinkage and Selection Operator.