Comprehensive Analysis of Differentially Expressed circRNAs Reveals a Colorectal Cancer-Related ceRNA Network

Comput Math Methods Med. 2020 Sep 1:2020:7159340. doi: 10.1155/2020/7159340. eCollection 2020.

Abstract

The morbidity and mortality of colorectal cancer (CRC) remained to be very high worldwide. Recently, circRNAs had been revealed to have a crucial role in cancer prognosis and progression. Numerous researches have shown that RNA sequencing technology and in silico method were widely used to identify pathogenic mechanisms and uncover promising targets for diagnosis and therapy. In this study, these methods were analyzed to obtain differentially expressed circRNAs (DECs). We identified upregulated 316 circRNAs and reduced 76 circRNAs in CRC samples, in comparison with those in normal tissues. In addition, a competitive endogenous network of circRNA-miRNA-mRNA was established to predict the mechanisms of circRNAs. Bioinformatics analysis revealed that these circRNAs participated in metabolism regulation and cell cycle progression. Of note, we observed the hub genes and miRNAs in this ceRNA network were associated with the survival time in CRC. We think this study could provide potential prognostic biomarkers and targets for CRC.

MeSH terms

  • Cell Cycle Checkpoints / genetics
  • Colorectal Neoplasms / genetics*
  • Colorectal Neoplasms / metabolism
  • Colorectal Neoplasms / pathology
  • Computational Biology
  • Gene Expression Profiling / statistics & numerical data
  • Gene Expression Regulation, Neoplastic
  • Gene Ontology
  • Gene Regulatory Networks*
  • Humans
  • Kaplan-Meier Estimate
  • Metabolic Networks and Pathways / genetics
  • MicroRNAs / genetics
  • Prognosis
  • RNA, Circular / genetics*
  • RNA, Messenger / genetics
  • Sequence Analysis, RNA / statistics & numerical data

Substances

  • MicroRNAs
  • RNA, Circular
  • RNA, Messenger