Identification of diagnostic biomarkers via weighted correlation network analysis in colorectal cancer using a system biology approach

Colorectal cancer (CRC) is the third most frequent cancer to be diagnosed in both females and males necessitating identification of effective biomarkers. An in-silico system biology approach called weighted gene co-expression network analysis (WGCNA) can be used to examine gene expression in a complicated network of regulatory genes. In the current study, the co-expression network of DEGs connected to CRC and their target genes was built using the WGCNA algorithm. GO and KEGG pathway analysis were carried out to learn more about the biological role of the DEmRNAs. These findings revealed that the genes were mostly enriched in the biological processes that were involved in the regulation of hormone levels, extracellular matrix organization, and extracellular structure organization. The intersection of genes between hub genes and DEmRNAs showed that DKC1, PA2G4, LYAR and NOLC1 were the clinically final hub genes of CRC.

Automated summary

A co-expression network of differentially expressed genes (DEGs) related to colorectal cancer (CRC) and their target genes was constructed using the weighted gene co-expression network analysis (WGCNA) algorithm. GO and KEGG pathway analysis revealed that these genes were mainly involved in the regulation of hormone levels, extracellular matrix organization, and extracellular structure organization. The study also identified DKC1, PA2G4, LYAR and NOLC1 as the key hub genes of CRC.