Identification of modules and functional analysis in CRC subtypes by integrated bioinformatics analysis.

ABSTRACT: Colorectal cancer is one of the top three causes of cancer-related mortality globally, but no predictive molecular biomarkers are currently available for identifying the disease stage of colorectal cancer patients. Common molecular patterns in the disease, beyond superficial manifestations, can be significant in determining treatment choices. In this study, we used microarray data from colorectal cancer and adjacent normal tissue from the GEO database. These data were categorized into four consensus molecular subtypes based on distinct gene expression signatures. Weighted gene-based protein-protein interaction network analysis was performed for each subtype. NUSAP1, CD44, and COL4A1 modules were found to be statistically significant and present among all the subtypes and displayed though s

SUBMITTER: Chen R 

PROVIDER: S-EPMC6716647 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature