PeerJ · 2026
Identification and validation of butyrate metabolism-related biomarkers for colorectal cancer diagnosis.
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Mitchell Kate M, Prudden Holly J, Washington Reynold, Isac Shajy, Rajaram Subramanian P, Foss Anna M, Terris-Prestholt Fern, Boily Marie-Claude, Vickerman Peter
Аннотация
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: The study analyzed four datasets (The Cancer Genome Atlas (TCGA)-COAD, TCGA-READ, GSE41258, and GSE39582) and gene sets related to butyrate metabolism-related genes (BMGs) in an integrated manner. Differentially expressed BMGs (DE-BMGs) were screened by overlapping BMGs, TCGA-DEGs between CRC and normal groups, and Gene Expression Omnibus (GEO)-differentially expressed genes (DEGs) between CRC and normal groups and were subjected to enrichment analysis. Hub genes were then screened via protein-protein interaction (PPI) network analysis. Biomarker selection was improved by applying the least absolute shrinkage and selection operator (LASSO) and receiver operating characteristic (ROC) curve analyses. Subgroup survival analyses were stratified according to different clinical phenotypes. A regulatory network modeled on competitive endogenous RNA was subsequently constructed. Finally, based on normal colon epithelial cells (NCM-460) and colon cancer cells (LOVO, HCT116, LS174T, and LS513), we detected the differential expression of biomarkers between the two groups using quantitative real-time polymerase chain reaction (qRT-PCR) methods.
RESULTS: Sixty-three DE-BMGs were obtained. Enrichment analysis showed significant correlations between DE-BMGs and signaling receptor activator activity and peroxisome proliferator-activated receptor-dominated pathways. Subsequently, six total biomarkers (CCND1, CXCL8, MMP3, MYC, TIMP1, and VEGFA) were obtained via PPI, LASSO, and ROC curve validation analyses. Survival analysis revealed significant differences in survival metrics between different clinical cohorts. Ingenuity pathway analysis demonstrated that pathways associated with identified biomarkers were disrupted, especially those associated with the tumor microenvironment. Finally, a computational prediction model was developed for 156 pharmacological agents targeting five key biomarkers: CCND1, CXCL8, MMP3, MYC, and VEGFA. The results of the qRT-PCR study indicated that CCND1, CXCL8, MYC, and VEGFA were upregulated in CRC cell lines, an observation consistent with existing public database records.
CONCLUSIONS: Six butyrate metabolism-related biomarkers (CCND1, CXCL8, MMP3, MYC, TIMP1, and VEGFA) were screened out to provide a basis for exploring the prediction of CRC diagnosis.
METHODS: The study analyzed four datasets (The Cancer Genome Atlas (TCGA)-COAD, TCGA-READ, GSE41258, and GSE39582) and gene sets related to butyrate metabolism-related genes (BMGs) in an integrated manner. Differentially expressed BMGs (DE-BMGs) were screened by overlapping BMGs, TCGA-DEGs between CRC and normal groups, and Gene Expression Omnibus (GEO)-differentially expressed genes (DEGs) between CRC and normal groups and were subjected to enrichment analysis. Hub genes were then screened via protein-protein interaction (PPI) network analysis. Biomarker selection was improved by applying the least absolute shrinkage and selection operator (LASSO) and receiver operating characteristic (ROC) curve analyses. Subgroup survival analyses were stratified according to different clinical phenotypes. A regulatory network modeled on competitive endogenous RNA was subsequently constructed. Finally, based on normal colon epithelial cells (NCM-460) and colon cancer cells (LOVO, HCT116, LS174T, and LS513), we detected the differential expression of biomarkers between the two groups using quantitative real-time polymerase chain reaction (qRT-PCR) methods.
RESULTS: Sixty-three DE-BMGs were obtained. Enrichment analysis showed significant correlations between DE-BMGs and signaling receptor activator activity and peroxisome proliferator-activated receptor-dominated pathways. Subsequently, six total biomarkers (CCND1, CXCL8, MMP3, MYC, TIMP1, and VEGFA) were obtained via PPI, LASSO, and ROC curve validation analyses. Survival analysis revealed significant differences in survival metrics between different clinical cohorts. Ingenuity pathway analysis demonstrated that pathways associated with identified biomarkers were disrupted, especially those associated with the tumor microenvironment. Finally, a computational prediction model was developed for 156 pharmacological agents targeting five key biomarkers: CCND1, CXCL8, MMP3, MYC, and VEGFA. The results of the qRT-PCR study indicated that CCND1, CXCL8, MYC, and VEGFA were upregulated in CRC cell lines, an observation consistent with existing public database records.
CONCLUSIONS: Six butyrate metabolism-related biomarkers (CCND1, CXCL8, MMP3, MYC, TIMP1, and VEGFA) were screened out to provide a basis for exploring the prediction of CRC diagnosis.