J Yang1, Y Zeng. 1. Department of Gastroenterology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, P. R. China. yang_jian688@163.com.
Abstract
OBJECTIVE: Pancreatic cancer is one of the most lethal diseases, and the pathogenesis remains largely unknown. To this end, we performed an integrated analysis of miRNA and mRNA expression data to explore the deregulation of miRNA and mRNA and regulatory processes underlying pancreatic cancer. MATERIALS AND METHODS: We combined mRNA and miRNA expression data with miRNA target predictions to infer new miRNA regulation activities in pancreatic cancer. We first integrated miRNA and mRNA expression profiling separately to identify differently expressed miRNA and mRNA in pancreatic cancer. Then we adopted miRWalk databases prediction to obtain potential target genes of differently expressed miRNA, and compared these target genes to the gene list of integrated mRNA expression profiling to select differentially expressed miRNA-target gene whose expression was reversely correlated with that of corresponding miRNAs. Gene Ontology (GO) classification analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were employed to understand the functions and pathways of miRNA target genes. Finally we construct a miRNA-target gene regulatory network. RESULTS: 42 differentially expressed miRNAs, 1376 differentially expressed mRNAs were identified by combining three expression profiles of miRNA and mRNA separately in pancreatic cancer, 146 miRNA target genes were found in the gene list of integrated mRNA expression profiling based on bioinformatics prediction. Functional annotation was performed to understand the functions and pathways of miRNA target genes. Finally, we constructed a miRNA-target gene regulatory network including 206 miRNA-target gene pairs. Five miRNAs (hsa-miR-130b, hsa-miR-106b, hsa-miR-181c, hsa-miR-153 and hsa-miR-125a-5p) demonstrated the highest connectivities, whereas three miRNAs (MYC, E2F1 and IL6) were the mRNAs with the highest connectivities. CONCLUSIONS: Our findings may provide new insights into the knowledge of molecular mechanisms of pancreatic cancer and development of novel targeting therapies.
OBJECTIVE:Pancreatic cancer is one of the most lethal diseases, and the pathogenesis remains largely unknown. To this end, we performed an integrated analysis of miRNA and mRNA expression data to explore the deregulation of miRNA and mRNA and regulatory processes underlying pancreatic cancer. MATERIALS AND METHODS: We combined mRNA and miRNA expression data with miRNA target predictions to infer new miRNA regulation activities in pancreatic cancer. We first integrated miRNA and mRNA expression profiling separately to identify differently expressed miRNA and mRNA in pancreatic cancer. Then we adopted miRWalk databases prediction to obtain potential target genes of differently expressed miRNA, and compared these target genes to the gene list of integrated mRNA expression profiling to select differentially expressed miRNA-target gene whose expression was reversely correlated with that of corresponding miRNAs. Gene Ontology (GO) classification analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were employed to understand the functions and pathways of miRNA target genes. Finally we construct a miRNA-target gene regulatory network. RESULTS: 42 differentially expressed miRNAs, 1376 differentially expressed mRNAs were identified by combining three expression profiles of miRNA and mRNA separately in pancreatic cancer, 146 miRNA target genes were found in the gene list of integrated mRNA expression profiling based on bioinformatics prediction. Functional annotation was performed to understand the functions and pathways of miRNA target genes. Finally, we constructed a miRNA-target gene regulatory network including 206 miRNA-target gene pairs. Five miRNAs (hsa-miR-130b, hsa-miR-106b, hsa-miR-181c, hsa-miR-153 and hsa-miR-125a-5p) demonstrated the highest connectivities, whereas three miRNAs (MYC, E2F1 and IL6) were the mRNAs with the highest connectivities. CONCLUSIONS: Our findings may provide new insights into the knowledge of molecular mechanisms of pancreatic cancer and development of novel targeting therapies.
Authors: Gabriel A Cipolla; Jong Kook Park; Liana A de Oliveira; Sara Cristina Lobo-Alves; Rodrigo C de Almeida; Ticiana D J Farias; Débora de S Lemos; Danielle Malheiros; Robert M Lavker; Maria Luiza Petzl-Erler Journal: Biochim Biophys Acta Date: 2016-07-14