WGCNA and machine learning identify AURKA, CDK1, and other hub genes associated with immune infiltration as therapeutic targets in GIST: An integrative bioinformatics analysis

Gastrointestinal stromal tumors (GISTs) are the primary mesenchymal tumors in the gastrointestinal (GI) tract. Surgical treatments and tyrosine kinase inhibitors significantly improve patient outcomes. However, many patients develop resistance within 18-24 months, mainly due to mutations in the KIT and PDGFRA genes. Therefore, there is an urgent need for new biomarkers and therapeutic targets beyond KIT and PDGFRA to enhance diagnosis, monitor disease progression, and develop innovative treatment strategies. Gene expression data from GSE136755 were analyzed using R. Weighted gene co-expression network analysis identified co-expression modules, which were subsequently followed by GO/KEGG enrichment for functional insights. PPI networks were constructed using STRING, and hub genes were screened with CytoHubba. LASSO and ROC analyses evaluated the diagnostic value, while qRT-PCR and Western blot validated gene expression in GIST-T1 cells. Immune infiltration correlations were assessed using ssGSEA. Weighted gene co-expression network analysis identified 1323 genes in the MEblue module. Ten hub genes were recognized through PPI network analysis: CDK1, CCNB1, CCNA2, TOP2A, AURKA, AURKB, CDCA8, CHEK1, BUB1, and RAD51. Among these, 5 core signature genes were identified and validated through LASSO regression and ROC analysis, exhibiting strong diagnostic performance with AUCs ranging from 78.1% to 89.1%. Western blot and qRT-PCR tests validated these genes in GIST-T1 cells, and ssGSEA analysis indicated a significant relationship between these hub genes and immune cell infiltration. This study revealed a set of novel signature genes with high diagnostic value, offering promising targets for the diagnosis and treatment of GIST.