Integrative meta-analysis and experimental validation reveal the oncogenic role of SACS and its therapeutic targeting potential in colorectal cancer
Abstract
This study aims to identify core genes closely associated with the diagnosis and prognosis of colorectal cancer (CRC) using transcriptome-based meta-analysis approach and machine learning algorithms. Nine CRC datasets from the GEO database were integrated for differential gene expression analysis and WGCNA to identify key genes. Ninety-six combinations of machine learning algorithms were employed to further refine the selection of core genes and validate their diagnostic performance. Functional enrichment, molecular pathways, and associations with the immune microenvironment of core genes were analyzed using GSEA, CIBERSORT, and ssGSEA. Drug sensitivity predictions were performed to evaluate the impact of core genes on CRC drug response, and molecular docking simulations were used to identify candidate compounds targeting the core genes. A total of 26 core genes were identified, among which the high expression of the SACS gene was significantly associated with poor prognosis, advanced stage, and specific pathological subtypes in CRC patients. GSEA revealed that high SACS expression prominently activates cell cycle regulatory pathways and immune pathways while suppressing metabolic pathways. Furthermore, in vitro experiments demonstrated that SACS is highly expressed in CRC cells and that its knockdown significantly inhibits CRC cell proliferation, suggesting its functional role in tumor growth. Immune analysis showed that high SACS expression was positively correlated with…
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Evidence Hub reads this as a meta-analysis, done in cell cultures (in vitro) and computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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