An ensemble method integrated with miRNA expression data for predicting miRNA targets in stomach adenocarcinoma.

Objective: It is crucially important to discover the relationships between genes and microRNAs (miRNAs) in cancer. Thus, we proposed a combined bioinformatics method integrating Pearson's correlation coefficient (PCC), Lasso, and causal inference method (IDA) to identify the potential miRNA targets for stomach adenocarcinoma (STAD) using Borda count election.
Materials and Methods: Firstly, the ensemble method integrating PCC, IDA, and Lasso was used to predict miRNA targets. Subsequently, to validate the performance ability of this ensemble method, comparisons between verified database and predicted miRNA targets were implemented. Pathway analysis for target genes in the top 1000 miRNA-mRNA interactions was implemented to discover significant pathways. Finally, the top 10 target genes were identified based on predicted times > 3.
Results: The ensemble approach was confirmed to be a feasible method to predict miRNA targets The 527 target genes of the top 1000 miRNA-mRNA interactions were enriched in 21 pathways. Of note, cell adhesion molecules (CAMs) was the most significant one. The top 10 target genes were identified based on predicted times > 3, such as GABRA3, CSAG1 and PTPN7. These targets were all predicted by 4 times. Moreover, GABRA3 and CSAG1 were simultaneously targeted by miRNA-105-1, miRNA-105-2, and miRNA-767. Significantly, among these top 10 targets, PTPN7 and GABRA3-miRNA interactions owned the highest correlation with 691.
Conclusion: The combined bioinformatics method integrating PCC, IDA, and Lasso might be a valuable method for miRNA target prediction, and dys-regulated expression of miRNAs and their potential targets might be prominently involved in the pathogenesis of STAD.