Prognostic and predictive utility of GPD1L in human hepatocellular carcinoma
File(s)
Published version
Author(s)
Leung, Philip Kwan Hung
Das, Bibek
Cheng, Xiaoyu
Tarazi, Munir
Type
Journal Article
Abstract
Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths worldwide. GPD1L, a member of the glycerol-3-phosphate dehydrogenase family, has emerged as a potential tumour suppressor gene, with high expression associated with a favourable prognosis in various cancers. Despite an intriguing inverse relationship observed with HCC, the precise role and underlying function of GPD1L in HCC remain poorly understood. Here, we aimed to investigate the prognostic significance, molecular characteristics, and predictive potential of GPD1L overexpression in HCC. Analysis of independent datasets revealed a significant correlation between high GPD1L expression and poor survival in HCC patients. Spatial and single cell transcriptome datasets confirmed elevated GDP1L expression in tumour tissue compared to adjacent normal tissue. GPD1L exhibited increased expression and promoter demethylation with advancing tumour stage, confirming positive selection during tumorigeneses. GPD1L overexpression was associated with metabolic dysregulation and enrichment of gene sets related to cell cycle control, epithelial-mesenchymal transition, and E2F targets. Moreover, we demonstrated an inverse correlation between GPD1L expression and therapeutic response for three therapeutic agents (PF-562271, Linsitinib, and BMS-754807), highlighting its potential as a predictive biomarker for HCC treatment outcomes. These data provide insights into the prognostic significance, molecular characteristics, and predictive potential of GPD1L in HCC.
Date Issued
2023-08-23
Date Acceptance
2023-08-21
Citation
International Journal of Molecular Sciences, 2023, 24 (17), pp.1-13
ISSN
1422-0067
Publisher
MDPI AG
Start Page
1
End Page
13
Journal / Book Title
International Journal of Molecular Sciences
Volume
24
Issue
17
Copyright Statement
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.mdpi.com/1422-0067/24/17/13113
Publication Status
Published
Article Number
13113
Date Publish Online
2023-08-23