Integrated machine learning and multi-dataset analysis identify SDS as a diagnostic and immune-related hub gene in hepatoblastoma
4 affiliations
- 1Department of Pediatric Hematology and Oncology, Xingtai People’s Hospital , ,
- 2Department of Oncology, Second Affiliated Hospital of Xingtai Medical College , ,
- 3Department of Cardiology, Xingtai People’s Hospital , ,
- 4Department of Public Health, Jining Medical University , ,
Abstract
Background: Current clinical diagnostic models lack sufficient sensitivity and specificity for Hepatoblastoma (HB) individualized prediction. This study aimed to identify robust hub genes for HB diagnosis and molecular subtyping by integrating multiple transcriptomic datasets and machine learning algorithms.
Methods: Four merged GEO hepatoblastoma datasets (GSE131329, GSE132037, GSE104766, GSE133039) were analyzed by differential expression and WGCNA to identify HB-related genes. Further use three algorithms (LASSO, SVM-RFE, Random Forest) to identify central genes. In addition, consensus clustering was used for molecular typing, and four deconvolution algorithms (TIMER, MCPcounter, xCell, quanTIseq) were used to characterize the tumor microenvironment. The biological function of SDS in hepatoblastoma was elucidated through in vitro experiments (CCK-8 detection, colony formation assay, Transwell assay), as well as in vivo using a xenograft mouse model.
Results: A total of 268 differentially expressed genes (77 up, 191 down) were identified. WGCNA revealed the blue module as most correlated with HB (correlation coefficient 0.66). Three machine learning algorithms jointly selected four hub genes (CLEC1B, ITGA6, SDS, TTC36). Consensus clustering based on these four genes stratified HB into two subtypes (Clust1, Clust2). Clust1 exhibited enhanced immune infiltration and activation of complement, IFN-α, and IL6-JAK-STAT3 pathways. Low SDS expression associated with cell cycle pathways, whereas high SDS expression associated with immune-related pathways. Functionally, SDS overexpression significantly suppressed HB cell proliferation, migration, invasion, and colony formation, while promoting apoptosis in vitro , and markedly reduced tumor growth in vivo .
Conclusion: Integrated machine learning identified CLEC1B, ITGA6, SDS, and TTC36 as robust diagnostic genes for hepatoblastoma. SDS, in particular, exerts a tumor-suppressive role and is closely linked to the immune microenvironment, making it a promising diagnostic biomarker and therapeutic target in HB.
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BibTeX
@article{chen2026integrated,
title = {{Integrated machine learning and multi-dataset analysis identify SDS as a diagnostic and immune-related hub gene in hepatoblastoma}},
author = {Dan Chen and Baixue Zhang and Yanlong Zhang and Na Li and Ang Li and Wei Gao},
journal = {Frontiers in oncology},
year = {2026},
month = sep,
doi = {10.3389/fonc.2026.1895612},
url = {https://europepmc.org/article/PMC/PMC13662425},
note = {CC BY},
}
RIS
TY - JOUR
TI - Integrated machine learning and multi-dataset analysis identify SDS as a diagnostic and immune-related hub gene in hepatoblastoma
AU - Dan Chen
AU - Baixue Zhang
AU - Yanlong Zhang
AU - Na Li
AU - Ang Li
AU - Wei Gao
PY - 2026
DA - 2026/09/30
JO - Frontiers in oncology
DO - 10.3389/fonc.2026.1895612
UR - https://europepmc.org/article/PMC/PMC13662425
AB - Background: Current clinical diagnostic models lack sufficient sensitivity and specificity for Hepatoblastoma (HB) individualized prediction. This study aimed to identify robust hub genes for HB diagnosis and molecular subtyping by integrating multiple transcriptomic datasets and machine learning algorithms. Methods: Four merged GEO hepatoblastoma datasets (GSE131329, GSE132037, GSE104766, GSE133039) were analyzed by differential expression and WGCNA to identify HB-related genes. Further use three algorithms (LASSO, SVM-RFE, Random Forest) to identify central genes. In addition, consensus clustering was used for molecular typing, and four deconvolution algorithms (TIMER, MCPcounter, xCell, quanTIseq) were used to characterize the tumor microenvironment. The biological function of SDS in hepatoblastoma was elucidated through in vitro experiments (CCK-8 detection, colony formation assay, Transwell assay), as well as in vivo using a xenograft mouse model. Results: A total of 268 differentially expressed genes (77 up, 191 down) were identified. WGCNA revealed the blue module as most correlated with HB (correlation coefficient 0.66). Three machine learning algorithms jointly selected four hub genes (CLEC1B, ITGA6, SDS, TTC36). Consensus clustering based on these four genes stratified HB into two subtypes (Clust1, Clust2). Clust1 exhibited enhanced immune infiltration and activation of complement, IFN-α, and IL6-JAK-STAT3 pathways. Low SDS expression associated with cell cycle pathways, whereas high SDS expression associated with immune-related pathways. Functionally, SDS overexpression significantly suppressed HB cell proliferation, migration, invasion, and colony formation, while promoting apoptosis in vitro , and markedly reduced tumor growth in vivo . Conclusion: Integrated machine learning identified CLEC1B, ITGA6, SDS, and TTC36 as robust diagnostic genes for hepatoblastoma. SDS, in particular, exerts a tumor-suppressive role and is closely linked to the immune microenvironment, making it a promising diagnostic biomarker and therapeutic target in HB.
SN - 2234-943X
ER -