Characterization of butyrate-metabolism in colorectal cancer to guide clinical treatment

Abstract

AbstractColorectal cancer (CRC) is the third most prevalent one in the world among the most common malignant tumors. Numerous studies have shown that butyrate has demonstrated promise as an antitumor agent in a variety of human cancer types. However, butyrate remains understudied in CRC tumorigenesis and progression. In this study, we explored therapeutic strategies to treat CRC by examining the role of butyrate metabolism. First, from the Molecular Signature Database (MSigDB), we identified 348 butyrate metabolism-related genes (BMRGs). Next, we downloaded 473 CRC and 41 standard colorectal tissue samples from The Cancer Genome Atlas (TCGA) database and the transcriptome data of GSE39582 dataset from Gene Expression Omnibus (GEO) database. Then we evaluated the expression patterns of butyrate metabolism-related genes with difference analysis in CRC. Through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, a prognostic model was constructed based on differentially expressed BMRGs. In addition, we discovered an independent prognostic marker for CRC patients. According to the expression levels and coefficients of identified BMRGs, the risk scores of all CRC samples were calculated. Utilizing differentially expressed genes in the high- and low-risk groups, we also constructed a Protein–Protein Interaction (PPI) network to visualize the interactions between proteins. Through the results of PPI network, we screened out differentially expressed target butyrate metabolism-related genes from ten hub genes. Finally, we performed clinical correlation analysis, immune cell infiltration analysis, and mutation analysis for these target genes. One hundred and seventy three differentially expressed butyrate metabolism-related genes were screened out in all the CRC samples. The prognostic model was established with univariate Cox regression and LASSO regression analysis. CRC patients’ overall survival was significantly lower in the high-risk group than in the low-risk group for both training and validation set. Among the ten hub genes identified from the PPI network, four target butyrate metabolism-related genes were identified containing FN1, SERPINE1, THBS2, and COMP, which might provide novel markers or targets for treating CRC patients. Eighteen butyrate metabolism-related genes were used to develop a risk prognostic model that could be helpful for doctors to predict CRC patients’ survival rate. Using this model, it is beneficial to forecast the response of CRC patients to immunotherapy and chemotherapy, thus making it easier to custom tailor cancer chemotherapy and immunotherapy to the individual patient.