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Integrated machine learning and multi-dataset analysis identify SDS as a diagnostic and immune-related hub gene in
Dan Chen1, Baixue Zhang2, Yanlong Zhang3
1Department of Pediatric Hematology and Oncology, Xingtai People's Hospital, Xingtai, China.
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.