Molecular mechanisms of colorectal cancer development based on butyrate metabolism-related genes


Abstract

Background. Unlike normal colon cells, which primarily use butyrate as an energy source, cancerous colon cells preferentially metabolize glucose. However, the modulatory role of butyrate metabolism in the pathophysiology of colorectal cancer (CRC) remains insufficiently explored.

Methods. This study conducted an integrated analysis of four datasets (TCGA-COAD, TCGA-READ, GSE41258, and GSE39582) and gene sets related to butyrate metabolism genes (BMGs). Differentially expressed BMGs (DE-BMGs) were identified by overlapping BMGs with differentially expressed genes (DEGs) from both TCGA and GEO datasets, comparing CRC and normal tissues. These DE-BMGs were subjected to enrichment analysis. Hub genes were identified through protein-protein interaction (PPI) network analysis. Biomarker selection was refined using the least absolute shrinkage and selection operator (LASSO) and receiver operating characteristic (ROC) curve analysis. Subgroup survival analyses were performed based on clinical phenotypes. A competitive endogenous RNA (ceRNA) regulatory network was constructed. Finally, differential expression of biomarkers was validated using quantitative real-time PCR (qRT-PCR) in normal colon epithelial cells (NCM-460) and CRC cell lines (LOVO, HCT116, LS174T, and LS513).

Results. A total of 63 DE-BMGs were identified. Enrichment analysis revealed significant associations with signaling receptor activator activity and peroxisome proliferator-activated receptor (PPAR)-related pathways. Six biomarkers (CCND1, CXCL8, MMP3, MYC, TIMP1, and VEGFA) were selected through PPI, LASSO, and ROC validation. Survival analysis showed significant differences across clinical subgroups. Ingenuity Pathway Analysis (IPA) indicated that pathways involving these biomarkers were disrupted, particularly those related to the tumor microenvironment. A computational model predicted 156 pharmacological agents targeting five key biomarkers. qRT-PCR results confirmed that CCND1, CXCL8, MYC, and VEGFA were upregulated in CRC cell lines, consistent with public database findings.

Conclusions. Six butyrate metabolism-related biomarkers (CCND1, CXCL8, MMP3, MYC, TIMP1, and VEGFA) were identified, providing a foundation for improving CRC diagnostic prediction and understanding the metabolic mechanisms underlying tumor progression.

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