Identification of MAD2L1 as a Novel Biomarker for Hepatoblastoma through Bioinformatics and Machine Learning Approaches

He, Ying; Hao, Xiwei; Hu, Bin; Xia, Nan; Wang, Chaojin; Chen, Xin; Zhang, Huanyu; Duan, Yuhe; Ying, Qinglong; Dong, Qian

doi:10.3389/fonc.2025.1524714

ORIGINAL RESEARCH article

Front. Oncol.

Sec. Cancer Molecular Targets and Therapeutics

Volume 15 - 2025 | doi: 10.3389/fonc.2025.1524714

This article is part of the Research Topic Novel Molecular Targets in Cancer Therapy View all 15 articles

Identification of MAD2L1 as a Novel Biomarker for Hepatoblastoma through Bioinformatics and Machine Learning Approaches

Provisionally accepted

Ying He

Xiwei Hao

Bin Hu

Nan Xia

Chaojin Wang

Xin Chen

Huanyu Zhang

Yuhe Duan

Qinglong Ying

Qian Dong ^*

The Affiliated Hospital of Qingdao University, Qingdao, China

The final, formatted version of the article will be published soon.

Objective: This study aims to identify potential biomarkers for Hepatoblastoma (HB) using bioinformatics and machine learning, and to explore their underlying mechanisms of action. Methods: We analyzed the datasets GSE131329 and GSE133039 to perform differential gene expression analysis. Single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were utilized to identify gene modules linked to gene set activity. Protein-protein interaction (PPI) networks were constructed to identify hub genes, while random forest and support vector machine models were employed to screen for key diagnostic genes. Survival and immune infiltration analyses were conducted to assess the prognostic significance of these genes. Additionally, the expression levels, biological functions, and mechanisms of action of the selected genes were validated in HB cells through relevant experimental assays. Results: We identified 1,377 and 1,216 differentially expressed genes in datasets GSE131329 and GSE133039, respectively. ssGSEA and WGCNA analyses identified 234 genes significantly linked to gene set activity. PPI analysis identified 20 core Hub genes. Machine learning highlighted three key diagnostic genes: CDK1, CCNA2, and MAD2L1. Studies have demonstrated that MAD2L1 is significantly overexpressed in HB and is associated with prognosis. WGCNA revealed that MAD2L1 is enriched in gene sets related to E2F_ TARGETS and G2M_CHECKPOINT. Experimental assays demonstrated that MAD2L1 knockdown significantly inhibits the proliferation, migration, and invasion of HB cell lines, and that MAD2L1 promotes cell cycle progression through the regulation of E2F. Conclusion: Our study identifies MAD2L1 as a novel potential biomarker for HB, providing new strategies for early diagnosis and targeted therapy in HB.

Keywords: Hepatoblastoma, biomarkers, WGCNA, machine learning, MAD2L1

Received: 08 Nov 2024; Accepted: 12 Mar 2025.

Copyright: © 2025 He, Hao, Hu, Xia, Wang, Chen, Zhang, Duan, Ying and Dong. 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: Qian Dong, The Affiliated Hospital of Qingdao University, Qingdao, China

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