Identification of Colon Cancer-Related RNAs Based on Heterogeneous Networks and Random Walk

SIMPLE SUMMARY: Colon cancer is a complex disease with high incidence rates and mortality worldwide. Although some medical methods have been used for screening, prevention and treatment, its molecular mechanism is still unclear. Among all dysfunctional factors, the change of mutual regulation relationship between RNAs is an important factor affecting the development of cancer. Therefore, the purpose of this study is to find RNAs related to colon cancer that have not been verified. We used differential expression analysis to screen mRNAs, miRNAs and lncRNAs and further constructed a heterogeneous interaction network among these three kinds of RNAs. The network propagation algorithm RW-DIR was then developed to mine the biological information contained in the network and to identify RNAs closely related to colon cancer. The research results have provided some theoretical support for disease research and provide a basis for narrowing the research scope of medical experiments. ABSTRACT: Colon cancer is considered as a complex disease that consists of metastatic seeding in early stages. Such disease is not simply caused by the action of a single RNA, but is associated with disorders of many kinds of RNAs and their regulation relationships. Hence, it is of great significance to study the complex regulatory roles among mRNAs, miRNAs and lncRNAs for further understanding the pathogenic mechanism of colon cancer. In this study, we constructed a heterogeneous network consisting of differentially expressed mRNAs, miRNAs and lncRNAs. This contains three kinds of vertices and six types of edges. All RNAs were re-divided into three categories, which were “related”, “irrelevant” and “unlabeled”. They were processed by dynamic excitation restart random walk (RW-DIR) for identifying colon cancer-related RNAs. Ten RNAs were finally obtained related to colon cancer, which were hsa-miR-2682-5p, hsa-miR-1277-3p, ANGPTL1, SLC22A18AS, FENDRR, PHLPP2, hsa-miR-302a-5p, APCDD1, MEX3A and hsa-miR-509-3-5p. Numerical experiments have indicated that the proposed network construction framework and the following RW-DIR algorithm are effective for identifying colon cancer-related RNAs, and this kind of analysis framework can also be easily extended to other diseases, effectively narrowing the scope of biological experimental research.