Molecular Mechanism of colorectal cancer occurrence and Development based on the mechanism of butyrate metabolism related genes

Abstract

Abstract Background Unlike normal colon cells with butyrate acid as the main energy source, cancerous colon cells are more inclined to use glucose. However, the mechanisms of the investigation into the modulatory role of butyrate metabolism within the pathophysiology of colorectal cancer (CRC) remains insufficiently explored. Methods In this study, an integrative analysis was conducted four datasets (TCGA-COAD, TCGA-READ, GSE41258, and GSE39582) and a gene set pertinent to butyrate metabolism genes (BMGs). Then, differentially expressed-BMGs (DE-BMGs) were selected by overlapping BMGs, TCGA-DEGs between the CRC and normal groups and GEO-DEGs between the CRC and normal groups, and DE-BMGs were analyzed for enrichment. Then hub genes were screened via protein-protein interaction (PPI) network analysis. Biomarker selection was refined through the application of the least absolute shrinkage and selection operator (LASSO) coupled with receiver operating characteristic (ROC) curve analytics. Subgroup survival analysis was stratified based on distinctive clinical phenotypes. This was followed by the construction of a regulatory network modeled on competing endogenous RNAs (ceRNAs). Conclusively, a rigorous validation process was undertaken to corroborate the expression patterns of the postulated biomarkers. Results 63 DE-BMGs was obtained. The enrichment analysis posited a pronounced correlation between DE-BMGs and both the signaling receptor activators activity and the pathways governed by peroxisome proliferator-activated receptors (PPAR). Subsequently, a total of 6 biomarkers (CCND1, CXCL8, MMP3, MYC, TIMP1, and VEGFA) were acquired by PPI analysis, LASSO regression and ROC curve validation. The survival analysis elucidated notable variances in survival metrics among distinct clinical cohorts. Ingenious pathway analysis (IPA) illuminated that the pathways associated with the identified biomarkers, particularly those implicated in the tumor microenvironment, were perturbed. A comprehensive ceRNA regulatory interaction network was then constructed. Lastly, a computational prediction model was developed for 156 pharmacological agents targeting five key biomarkers: CCND1, CXCL8, MMP3, MYC, and VEGFA. Validation experiments substantiated the upregulation of CCND1, CXCL8, MYC, and VEGFA in CRC cell lines, an observation that is congruent with existing public database records. Conclusion Six butyrate metabolism-related biomarkers (CCND1, CXCL8, MMP3, MYC, TIMP1, and VEGFA) were screened out to provide a basis for exploring the prediction of diagnosis of CRC.