ORIGINAL RESEARCH article
Front. Genet.
Sec. Cancer Genetics and Oncogenomics
Volume 16 - 2025 | doi: 10.3389/fgene.2025.1654459
This article is part of the Research TopicTargeted Cancer Therapy Through Metabolic PathwaysView all articles
Identifying Metabolism-Related Genes in Liver Cancer through Weighted Gene Co-expression Network Analysis and Machine Learning
Provisionally accepted
- 1Macau University of Science and Technology, Taipa, Macao, SAR China
- 2Guangzhou Medical University Guangzhou Women and Children's Medical Center, Guangzhou, China
- 3Sun Yat-Sen Memorial Hospital, Guangzhou, China
- 4The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
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Objective: As a leading cause of cancer-related mortality, liver cancer was associated with metabolic dysregulation. We aimed to identify metabolism-related prognostic biomarkers and therapeutic targets.Methods: Transcriptomic data from TCGA were analyzed using EdgeR to identify differentially expressed genes (DEGs). WGCNA was applied to unveil the metabolismrelated genes in liver cancer. Machine learning algorithms (RF, SVM, LASSO) refined marker genes. GSEA and ssGSEA were conducted to identify pathway associations and immune interactions of marker genes. DGIdb database predicted candidate therapeutics targeting these biomarkers. The independent queue (GSE54236) was verified as an external dataset. RT-PCR validated gene expression in clinical samples.Results: A total of 234 metabolism-related genes were identified in liver cancer.Through undergoing machine learning by RF, SVM, and LASSO algorithms, seven marker genes (ACADS, ALDH8A1, COX4I2, CYP2C8, DBH, NDST3, and PLA2G6) were obtained. Except for PLA2G6, the other genes were correlated with the survival of patients with liver cancer and immune cells infiltration. Additionally, ACADS, ALDH8A1, CYP2C8, DBH, and NDST3 were downregulated, and COX4I2 was upregulated in dataset of GSE54236, which were consist with those in TCGA database. However, RT-PCR validation in 10 paired clinical samples confirmed significant downregulation of ACADS, ALDH8A1, COX4I2, CYP2C8, DBH, and NDST3 in tumor tissues (all P < 0.05). Immune infiltration analysis revealed these genes might influence immune cell infiltration in the tumor microenvironment. And the candidate drugs were unveiled, including PAZOPANIB, SUMATRIPTAN, ETOPOSIDE, etc.The metabolism-related biomarkers ACADS, ALDH8A1, COX4I2, CYP2C8, DBH, and NDST3 demonstrated significant potential for predicting liver cancer prognosis and may serve as candidate therapeutic targets.
Keywords: liver cancer, Metabolism, machine learning, immune cells, therapy
Received: 26 Jun 2025; Accepted: 12 Aug 2025.
Copyright: © 2025 Wang, Lai, Tang, Lin and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Taorui Wang, Macau University of Science and Technology, Taipa, Macao, SAR China
Mingjiao Zhang, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
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