Bioinformatics method to predict two regulation mechanism: TF-miRNA-mRNA and lncRNA-miRNA-mRNA in pancreatic cancer.

Altered expressions of microRNAs (miRNAs) are reported in pancreatic cancer and associate with cancer pathogenesis, apoptosis, and cell growth, thereby functioning as either tumor suppressors or oncogenes. However, the majority of studies focus on defining the regulatory functions of miRNAs, whereas few investigations are directed toward assessing how the miRNA themselves are transcriptionally regulated. In this study, integration of published multi-level expression data and bioinformatics computational approach was used to predict two regulation mechanisms: transcription factors (TF)-miRNA-mRNA regulation and long non-coding RNA(lncRNA)-miRNA-mRNA regulation. To identify differentially expressed mRNAs, miRNAs, and lncRNAs, we integrated microarray expression data in pancreatic cancer tissues and normal tissues. Combination of differentially expressed mRNAs and miRNAs with miRNA-mRNA interactions based on crosslinking and immunoprecipitation followed by high-throughput sequencing (CLIP-Seq) data from StarBas, we constructed miRNA-mRNA regulatory network. Then we constructed two regulatory networks including TF-miRNA-mRNA and lncRNA-miRNA-mRNA based on chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq) data from ChIPBase and CLIP-Seq data. A total of 4385 mRNAs, 500 miRNAs, and 21 lncRNAs were differentially expressed, of which, 18 mRNAs and 54 miRNAs are with high confidence. In miRNA-mRNA regulatory network, interrelated miRNAs target 1701 differentially regulated mRNAs. By constructing regulatory network, 19miRNAs including hsa-miR-137, hsa-miR-206, hsa-miR-429, hsa-miR-320d, and hsa-miR-320c are predicted to participate in lncRNA-miRNA-mRNA regulation. Furthermore, 8 miRNAs including hsa-mir-137, hsa-mir-206, hsa-mir-429, hsa-mir-375, hsa-mir-326, hsa-mir-217, hsa-mir-301b, and hsa-mir-184 are predicted to participate in TF-miRNA-mRNA regulation. In an integrated data analysis, we reveal large-scale effects of interrelated miRNAs and provide a model for predicting the mechanism of miRNAs disorder. Our study provides a new insight into understanding the transcriptional regulation of pancreatic cancer.