Evidence map›Paper›PMID 40231267›Full record

ArticleFrontiers in oncology2025

Identification of MAD2L1 as a novel biomarker for hepatoblastoma through bioinformatics and machine learning approaches.

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

Abstract read
In one paragraph

Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Ying HeDepartment of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
Xiwei HaoDepartment of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
Bin HuDepartment of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Nan XiaShandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
Chaojin WangDepartment of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
Xin ChenDepartment of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
Huanyu ZhangDepartment of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
Yuhe DuanDepartment of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
Qinglong YingDepartment of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
Qian DongDepartment of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

biomarkershepatoblastomamachine learningMAD2L1WGCNA

Identifiers

PMID40231267
PMCPMC11994420

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.