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2842-2852-Microrna-Mrna and P-P Interaction Analysis in Pancreatic

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2842-2852-Microrna-Mrna and P-P Interaction Analysis in Pancreatic Eur opean Rev iew for Med ical and Pharmacol ogical Sci ences 2016; 20: 2842-2852 Integrative microRNA-mRNA and protein-protein interaction analysis in pancreatic neuroendocrine tumors H.-Q. ZHOU 1,2 , Q.-C. CHEN 2, Z.-T. QIU 2, W.-L. TAN 1, C.-Q. MO 3, S.-W. GAO 1 1Department of Anesthesia, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Chin a 2Sun Yat-sen University School of Medicine, Guangzhou, China 3Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China Abstract. – OBJECTIVE : Pancreatic neuroen - Key Words: docrine tumors are relatively rare pancreatic microRNA, mRNA, Pancreatic neuroendocrine tu - neoplasms over the world. Investigations of the mors, Protein-protein interaction network. molecular biology of PNETs are insufficient for nowadays. We aimed to explore the expression of microRNA and messenger RNA and regulato - ry processes underlying pancreatic neuroen - Introduction docrine tumors. MATERIALS AND METHODS: The messenger RNA and microRNA expression profile of Pancreatic neuroendocrine tumors (PNETs), GSE43796 were downloaded, including 6 sam - also called islet cell tumors, are relatively rare ples with pancreatic neuroendocrine tumors and pancreatic neoplasms, arising from the endocrine 5 healthy samples. First, the Limma package was tissues rather than glandular epithelium of the utilized to distinguish the differentially ex - pancreas. With secreting a variety of peptide hor - pressed messenger RNA and microRNA sepa - mones, PNETs are terminologically divided as rately. Then we used microRNA Walk databases to predict the target genes of the differentially insulinoma, gastrinoma, glucagonoma, somato - expressed microRNAs (DE-microRNAs), and se - statinoma and vasoactive intestinal peptide tu - lected differentially expressed target genes mors (VIPoma), resulting in numbers of clinical whose expression was reversely correlated with syndromes. However, more commonly, PNETs microRNAs. Gene Ontology classification and appear as nonfunctioning tumors 1. The incidence Kyoto Encyclopedia of Genes and Genomes of PNETs, which is less than 0.2 per 100,000 pathway enrichment analysis were performed to populations in 1973, has been remarkably in - explore the functions and pathways of target 2 genes. In addition, we constructed a regulatory creased during the recent decades . The outcome miRNAs-target genes network and a protein-pro - with PNETs differs among individuals. Some of tein interaction network. them are indolent, while others are quite aggres - RESULTS: There were 28 differentially ex - sive. The treatment strategy, tumor-size or TNM pressed microRNAs and 859 differentially ex - stage and histologic grade have been reported as pressed messenger RNAs, including 253 poten - prognosis factors in some studies 3-5 . The only cu - tial target genes. These target genes mainly en - riched in ABC transporters pathway. In this net - rative treatment of PNETs is radical surgery, work, hsa-miR-7-2-3p demonstrated the highest while somatostatin analogs and chemotherapy connectivities whereas KLF12 was the mRNAs are chosen in unresectable cases 6,7 . with the highest connectivities. CXCL12 was Though there are several studies for molecular identified as the hub of the protein-protein inter - biology of PNETs in the last decade 8-10 , its unclear action sub-network. pathogenesis and pathophysiology need more in - CONCLUSIONS: The genes involved in ABC vestigations. MicroRNAs (miRNAs) are a class of transporters and Type II diabetes mellitus path - way, KLF12 and CXCL12 may play an important non-coding RNAs with 21 and 25 nucleotides in role in the progression of pancreatic neuroen - length and function as regulators of gene expres - docrine tumors. However, more experimental sion 11 . As known, miRNAs affect in various hu - studies are required. man cancers as cancer suppressors or oncogenes 12 . 2842 Corresponding Author: Shaowei Gao, MD; e-mail: [email protected] microRNA-mRNA and P-P interaction analysis in pancreatic neuroendocrine tumors Not unexpectedly, several miRNAs alter PNETs’ Identification of Differentially Expressed proliferation and/or migration 13,14 . For example, miRNA Target Genes Roldo et al 14 suggested alteration in microRNA miRWalk databases (http://www.umm.uni-hei - expression is related to PNETs and Kang et al 13 re - delberg.de/apps/zmf/mirwalk/ ) were used to pre - ported that the miR-27b would be a prognostic dict the target genes of the DE -miRNAs 19 . We marker of recurrence in resected PNETs. In order predicted putative targets of differentially ex - to investigate the pathogenesis of solid pseudopap - pressed miRNAs by six bioinformatics algo - illary neoplasm, Park et al 15 characterized the mR - rithms (DIANAmT, miRanda, miRDB, miR - NA and miRNA expression profiles of solid Walk, PICTAR and Targetscan) and recorded the pseudopapillary neoplasm through comparing miRNAs from at least 4 of 6 algorithms. We also them with other pancreatic tumors including selected miRNA target genes with experiment PNETs. However, the author did not described validation to compare with the gene list of DE- miRNA-mRNA crosstalk in PNETs specifically mRNAs. Because miRNAs usually down-regu - while Wang et al 16 only described the complete ex - late the expression of their target genes, we se - pression characteristics existing in PNETs. So we lected differentially expressed target genes whose revealed the miRNA-mRNA crosstalk in PNETs expression was reversely correlated with miR - by integrating transcriptome analysis, in order to NAs for subsequent investigation 20-23 . give more detailed overviews for PNETs. In our study, we mined a classical expression Function Annotation and Pathway profile of miRNAs genes to construct a novel Enrichment Analysis miRNA-mRNA regulatory network in PNETs. To understand the significance of these miR - We also conducted a protein-protein interaction NA target genes, we performed the Gene Ontol - (PPI) network to identify the interactive associa - ogy (GO) classification 24 . We also conducted the tions between the target genes. Our data may Kyoto Encyclopedia of Genes and Genomes contribute to future investigations for mecha - (KEGG) pathway enrichment analysis to search nisms of PNETs. the possible pathway of miRNA target genes 25 . The Database for Annotation, Visualization and Integrated Discovery (DAVID) was utilized in Materials and Methods these functional annotation 26 . Gene Expression Profiles Regulatory Network Between miRNAs We searched the Gene Expression Omnibus and Their Targets database (GEO, http://www.ncbi.nlm.nih.gov/geo) We construct a regulatory network among iden - for mRNA and miRNA expression profiling stud - tified miRNAs-target interacting pairs. In the net - ies in pancreatic neuroendocrine tumors 17 {Edgar, work, the expression of miRNA-target gene inter - 2002 #833}. The mRNA and miRNA expression acting pairs correlates reversely in PNETs. Cy - profile of GSE43796 [the Gene Expression Om - toscape software was utilized for visualization. nibus (GEO) accession number] , collected by Kim 15 , was downloaded. The expression profiles PPI Network Construction and in 6 patients with PNETs and 5 healthy control Sub-Network Mining subjects were available. STRING version 10.0 comprises of > 1,100 completely sequenced organisms 27 . To identify the Identification of DE-miRNAs interactive associations between the target genes, and DE-mRNAs the target genes of DE -miRNAs were input into The expression data were processed using lim - STRING. The Cytoscape software was used to vi - ma package in R software, which includes back - sualize these associations and the mined modules. ground correction, quantile normalization, and fi - nal probe summarization 18 . The average expres - sion value was used when several probes were Results corresponded to the same gene. We selected dif - ferentially expressed mRNA with criterion of p- DE-miRNAs and DE-mRNAs in PNETs value < 0.01 and |log2fold change (FC)| ≥ 2 and We performed differentially expressed analysis differentially expressed miRNA with criterion of between PNETs and normal control samples. 28 p-value < 0.0 1 and |log2fold change (FC)| ≥ 2. miRNAs were regarded as significantly differen - 2843 H.-Q. Zhou, Q.-C. Chen, Z.-T. Qiu, W.-L. Tan, C.-Q. Mo, S.-W. Gao tially expressed under the threshold of p-value < Function Annotation and 0.01 and |log2fold change (FC)| ≥ 2, with 18 up- Pathway Enrichment Analysis regulated and 10 down-regulated miRNAs, as Generation of a signal involved in cell-cell sig - shown in Table I and Figure 1. 485 up-regulated naling (GO:0003001, p = 3.58E-06) and neuron and 374 down-regulated genes were identified to projection morphogenesis (GO:0048812, p = be differentially expressed in PNETs under the 6.09E-05) were significantly enriched for biolog - threshold of p-value < 0.01 and |log2fold change ical processes, while for molecular functions (FC)| ≥ 2. were kinase regulator activity (GO:0019207, p = 4.54E-05) and protein kinase regulator activity Identification of Differentially Expressed (GO:0019887, p = 1.33E-04), and for cellular miRNA Target Genes component were synapse (GO:0045202, p = mRNA and miRNA expression data with miR - 3.95E-06) and neuron projection (GO:0043005, NA target predictions were combined to obtain p = 9.62E-06) (Table III) . genuine miRNA targets 20 . We input 28 miRNAs We used hypergeometric test with p value < into the miRWalk database, of which 7 miRNAs 0.05 as the criteria for KEGG pathway detection.
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