Bioinformatics analysis of colorectal cancer transcriptomic data reveals novel prognostic signature and potential biomarker genes.

Abstract

Objective: Colorectal cancer (CRC) is a type of digestive system cancer. At the molecular level, some factors, including genetic and epigenetic factors, as well as various signaling pathways such as oxidative stress and inflammation, play an active role in the onset of CRC. Genetic and epigenetic mutations, particularly in oncogenes and tumor suppressor genes, occur during colorectal adenocarcinoma development as a result of a change in gastrointestinal epithelial cell proliferation and self-renewal rates. This study aimed to determine the genes and molecular mechanisms that play a role in the emergence of this disease by analyzing the CRC data.

Material and methods: Microarray data selected for bioinformatics analysis is Gene Expression data stored with the code GSE110224 in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Gene expression analysis, functional clustering analysis, enrichment analysis, and pathway analysis were performed using this data set.

Results: Analysis of raw transcriptomic data revealed 1770 common DEGs in CRC. While the expression level of 769 of these genes increased, the expression level of 1001 genes decreased. A Protein-protein interaction (PPI) network was created from the first 25 genes with increased expression levels and 11 signature genes were identified. Increased expression of REG1A, MMP3, FOXQ1 and CEMIP genes and decreased expression of AQP8, CA1, CLDN8, PYY, CA4, CEACAM7 and SLC30A10 genes were observed.

Conclusions: This approach revealed a CRC-specific molecular profile and may provide some guidance for further investigation of potential biomarkers for diagnosis and prognosis prediction of CRC patients.