Identification and prediction of novel non-coding and coding RNA-associated competing endogenous RNA networks in colorectal cancer

AIM: To identify and predict the competing endogenous RNA (ceRNA) networks in colorectal cancer (CRC) by bioinformatics analysis. METHODS: In the present study, we obtained CRC tissue and normal tissue gene expression profiles from The Cancer Genome Atlas project. Differentially expressed (DE) genes (DEGs) were identified. Then, upregulated and downregulated miRNA-centered ceRNA networks were constructed by analyzing the DEGs using multiple bioinformatics approaches. DEmRNAs in the ceRNA networks were identified in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using KEGG Orthology Based Annotation System 3.0. The interactions between proteins were analyzed using the STRING database. Kaplan-Meier survival analysis was conducted for DEGs and real time quantitative polymerase chain reaction (RT-qPCR) was also performed to validate the prognosis-associated lncRNAs in CRC cell lines. RESULTS: results. CONCLUSION: CeRNA networks play an important role in CRC. Multiple DEGs are related with clinical prognosis, suggesting that they may be potential targets in tumor diagnosis and treatment.