Integrated analysis of intestinal microbiota and host gene expression in colorectal cancer patients

Introduction. Colorectal cancer (CRC) is one of the most common cancers and poses heavy burden on global health. The relationship between mucosal microbiome composition and colorectal gene expression are rarely studied. In this study, we integrated transcriptome data with microbiome data to investigate the relationship between them in colorectal cancer patients.Gap statement. Previous studies have identified the contribution of gut microbiota and DEGs to the pathogenesis of CRC, but the relationship between mucosal microbiome composition and colorectal gene expression are rarely studied.Aim. In this study, we integrated transcriptome data with microbiome data to investigate the relationship between mucosal microbiome composition and colorectal gene expression. Methodology. First, three independent CRC gene expression profiles (GSE184093, GSE156355 and GSE146587) from Gene Expression Omnibus (GEO) were used to identify differentially expressed genes (DEGs). Second, another dataset (GSE163366) was used to analyse gut mucosal microbiome differential abundance. GO (Gene Ontology) function and KEGG (Kyoto Encyclo-paedia of Genes and Genomes) pathway enrichment analyses of the DEGs were performed. Protein-protein interactions (PPIs) of the DEGs were constructed. The Spearman correlation analysis was computed between host DEGs and gut microbiome abundance data.Results. A total of 1036 upregulated DEGs and 1194 downregulated DEGs between noncancerous tissues and cancerous tissues were identified based on the analysis. One significant module with a score 37.65 was selected out via MCODE including 41 upregulated DEGs, which are were mostly enriched in two pathways, including microtubule binding and tubulin binding. In particular, significant negative correlations are prevalent between Fusobacterium and the 41 DEGs with the correlation ranging between -0.54 and -0.35, and there commonly exist significant positive correlations between Blautia and the 41 DEGs with the correlation ranging between 0.42 and 0.54, indicating that Fusobacterium and Blautia are two of the most important microbes interacting with the gene regulation. Conclusion. Our results demonstrate significant correlation between some gut microbes and DEGs, providing a comprehensive bioinformatics analysis of them for future investigation into the molecular mechanisms and biomarkers.