Skip to main content
NCBI home page
Molecular Medicine Reports logoLink to Molecular Medicine Reports
. 2016 Nov 7;14(6):5451–5460. doi: 10.3892/mmr.2016.5928

Identfication of key miRNAs in pancreatitis using bioinformatics analysis of microarray data

Dadong Wang 1, Zi-Man Zhu 1, Yu-Liang Tu 1, Chun-Qing Dou 1, Yong Xu 2, Xiang-Long Tan 2, Ming-Ming Han 1, Zhuang-Jie Yang 1, Xin Jin 1, Bao Zhang 1, Shouwang Cai 3,✉, Zhi-Wei Liu 3,✉
PMCID: PMC5355656  PMID: 27840954

Abstract

Pancreatitis is a type of inflammation in the pancreas, which frequently occurs due to alcohol and gallstones. The present study aimed to identify pancreatitis-associated microRNAs (miRNAs) by analyzing the microarray of GSE24279. GSE24279 was downloaded from the Gene Expression Omnibus, composed of a collective of 27 pancreatitis and 22 normal control samples. The differentially expressed miRNAs (DE-miRNAs) in pancreatitis samples were screened using the Limma package in Bioconductor. Subsequently, target genes of the DE-miRNAs were predicted using the miRecords and miRWalk databases. Their potential functions were analyzed by functional and pathway enrichment analysis using the Database for Annotation, Visualization and Integrated Discovery online tool. Finally, pancreatitis-associated genes among the target genes identified were searched using the Comparative Toxicogenomics Database, and a regulatory network of pancreatitis-associated genes and their target miRNAs were constructed using Cytoscape software. A total 14 upregulated and 39 downregulated miRNAs were identified in pancreatitis samples compared with control samples and 290 target genes of DE-miRNAs were determined. Cyclin D1 (CCND1), v-akt murine thymoma viral oncogene homolog 2 (AKT2), cyclin-dependent kinase 6 (CDK6) and SMAD family member 2 (SMAD2) were involved in the pathway of pancreatic cancer. Among the target genes, 279 genes were pancreatitis-associated genes, which in turn were targeted by 37 miRNAs in the regulatory network. Hsa-miR-15a, hsa-miR-16, hsa-miR-155, hsa-miR-375 and hsa-miR-429 in particular may be involved in pancreatitis by targeting genes in the regulatory network, including hsa-miR-15a→CCND1, hsa-miR-16→CCND1, hsa-miR-155→CCND1/SMAD2, hsa-miR-375→AKT2/CDK6 and hsa-miR-429→CCND1. The above miRNAs and their targets may contribute to the pathogenesis of pancreatitis; therefore, they may be potential therapeutic targets.

Keywords: pancreatitis, differentially expressed miRNAs, target genes, enrichment analysis, regulatory network

Introduction

As a type of inflammation in the pancreas, pancreatitis is frequently due to excessive alcohol consumption and gallstones (1–3). Patients with pancreatitis often suffer from upper abdominal pain, nausea and vomiting (4,5) and may develop complications, including diabetes, infection and bleeding (6–8). Globally, the pancreatitis-associated deaths in 2013 were 123,000, up from 83,000 deaths in 1990 (9). Therefore, it is urgent to investigate the underlying molecular mechanisms that lead to pancreatitis.

MicroRNAs (miRNAs) are 22-nucleotide single-stranded and small non-coding RNAs that regulate post-transcriptional expression of genes in eukaryotes and function in diverse biological processes (10,11). miRNAs, which are more stable compared with mRNAs, which is essential for the robustness of diagnostic assays (12). In comparison with normal pancreatic tissue, several miRNAs, including miR-99, miR-100, miR-125a, miR-199a-1, miR-199a-2 have been previously identified to be overexpressed in pancreatic cancer and in chronic pancreatitis (13). In pancreatic cancer cells, tumor-suppressing miR-96 may act as a novel therapeutic marker for the treatment of pancreatic cancer via targeting the Kirsten rat sarcoma viral oncogene homolog oncogene (14). CD40 targeted by miR-224 and miR-486 may be of importance to the progression of pancreatic ductal adenocarcinomas (PDACs) and may have diagnostic and therapeutic potential in PDACs (15). Cell division cycle 25B, which may contribute to proliferation of PDACs, has been identified as a novel target of miR-148a (16). A previous study indicated that miR-10a was upregulated in various pancreatic cancers and correlated with the invasive potential of pancreatic cancer cells by inhibiting homeobox A1 (17). In the present study, differentially expressed miRNAs (DE-miRNAs) in pancreatitis were screened using miRNA expression profile of GSE24279. The genes targeted by DE-miRNAs were predicted and their potential functions were analyzed by functional and pathway enrichment analysis. Subsequently, pancreatitis-associated genes were identified from the target genes and the regulatory networks of pancreatitis-associated genes and miRNAs that targeted them were constructed.

Materials and methods

Microarray data

The miRNA expression profile of GSE24279 was downloaded from the Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo/), which was based on the platform of GPL10944 febit human miRBase version 11. A collective of 27 pancreatitis and 22 normal control samples were selected from GSE24279 to investigate the mechanisms of pancreatitis.

Screening for DE-miRNAs

Using the vsn package (18) in Bioconductor, version 2.7, microarray data was preprocessed using robust estimation of variance-stabilizing and calibrating transformations to obtain an expression matrix of miRNAs. The limma package (19) in Bioconductor was used to screen the DE-miRNAs between pancreatitis samples and control samples. The P-values of miRNAs were calculated using the t-test function in the limma package and adjusted for multiple comparisons using the Benjamini & Hochberg method (20). The adjusted P<0.05 and |log2 fold-change (FC)|≥0.58 (corresponded to 1.5-fold) were used as the cut-off criteria.

Analysis of genes targeted by DE-miRNAs

The genes targeted by DE-miRNAs were predicted using the miRecords (http://c1.accurascience.com/miRecords/) (21) and miRWalk (www.umm.uni-heidelberg.de/apps/zmf/mirwalk/) (22) databases. A DE-miRNA-gene pair was required to appear in at least one database as a filtering condition.

Functional and pathway enrichment analysis

Gene Ontology (GO) analysis was used to predict the potential functions of the gene products, including molecular function (MF), biological process (BP) and cellular component (CC) categories (23). The Kyoto Encyclopedia of Genes and Genomes (KEGG), which consists of the PATHWAY, GENES and LIGAND databases was used to identify molecular functions (24). Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) (25) online tool, GO and KEGG pathway enrichment analyses were performed for the genes targeted by the DE-miRNAs. Parameters of EASE ≤0.05 and count ≥2 were used as the thresholds. The remaining parameters were set to defaults.

Regulatory network construction

There may be pancreatitis-associated genes among the target genes of DE-miRNAs, and it is suggested that miRNAs that targeted them may have been involved in pancreatitis progression. Therefore, the Comparative Toxicogenomics Database (CTD; ctd.mdibl.org) (26) was used to identify pancreatitis-associated genes. Next, the regulatory network of pancreatitis-associated genes and miRNAs that targeted them were visualized using Cytoscape software, version 2.8 (www.cytoscape.org) (27).

Results

Analysis of DE-miRNAs and their target genes

There were 14 upregulated and 39 downregulated miRNAs screened in pancreatitis samples compared with the control samples. There were more downregulated miRNAs compared with upregulated miRNAs. The heat map of DE-miRNAs is presented in Fig. 1. A total 424 DE-miRNA-gene pairs were identified in the miRecords and miRWalk databases and 290 target genes of DE-miRNAs were obtained.

Figure 1.

Figure 1.

Open in a new tab

Heat map of differentially expressed miRNAs. Red indicates upregulated miRNAs. Green indicates downregulated miRNAs. miRNA, microRNA.

Functional and pathway enrichment analysis

The functions of target genes were predicted using the DAVID online tool, and the GO and KEGG pathway enrichment analyses. The enriched GO functions for the target genes are presented in Table I, including the positive regulation of macromolecule metabolic process (P=1.20×10−32) in the BP category, nuclear lumen (P=1.25×10−14) in the CC category and sequence-specific DNA binding (P=2.73×10−18) in the MF category. The enriched KEGG pathways for target genes included pathways in cancer (P=5.32×10−16), focal adhesion (P=1.70×10−7) and pancreatic cancer (P=3.08×10−5; Table II). Cyclin D1 (CCND1), v-akt murine thymoma viral oncogene homolog 2 (AKT2), cyclin-dependent kinase 6 (CDK6) and SMAD family member 2 (SMAD2) were specifically involved in the pancreatic cancer pathway.

Table I.

The enriched functions for target genes of differentially expressed microRNAs.

Category Term Description Gene number Gene symbol P-value
BP GO:0010604 Positive regulation of macromolecule metabolic process 80 MYOD1, PPARA, HNF1A, PDGFB, BTRC, MITF, PPARG, SNCA, SPI1, FOXO1, TLR4, PDX1, ZEB1, FOXO3, GLI2, GLI3, IL10, CTNNB1, TGFB2, CCNE1, GATA6, FOXF1, HMOX1, IFNG, YAP1, MKL1, MYC, IHH, AKT2, IRS2, EGR2, RARG, ESR1, RB1, PPARGC1A, HMGA1, GTF2H1, ACVR2A, CCND1, EP300, HNF4A, CCND3, CCND2, JUN, FOXC2, ZFPM2, CLOCK, CSF1, SOX2, SOX9, SRF, WT1, IGF1R, IL17A, HAND1, AGT, BCL2, POU3F1, RUNX1, DNMT3B, RUNX2, CEBPB, MAFB, NR4A2, CD276, SMAD3, SMAD2, HDAC4, NOTCH1, SP1, PLK1, ETS1, NOTCH4, FOXE1, DNMT1, JAK2, TNK2, MTOR, BMP7, KLF4 1.20×10−32
GO:0009891 Positive regulation of biosynthetic process 67 PPARA, MYOD1, HNF1A, PDGFB, PPARG, MITF, SPI1, FOXO1, TLR4, PDX1, ZEB1, FOXO3, GLI2, GLI3, IL10, CTNNB1, TGFB2, CCNE1, GATA6, FOXF1, HMOX1, IFNG, YAP1, MKL1, MYC, AKT2, IHH, IRS2, EGR2, RARG, RB1, PPARGC1A, HMGA1, GTF2H1, EP300, HNF4A, JUN, FOXC2, ZFPM2, CLOCK, SOX2, SOX9, SRF, WT1, IGF1R, IL17A, HAND1, AGT, PRKAA1, RUNX1, RUNX2, CEBPB, MAFB, NR4A2, CD276, SMAD3, SMAD2, HDAC4, NOTCH1, SP1, ETS1, NOTCH4, FOXE1, JAK2, MTOR, BMP7, KLF4 9.62×10−28
GO:0010557 Positive regulation of macromolecule biosynthetic process 64 PPARA, MYOD1, HNF1A, PDGFB, PPARG, MITF, SPI1, FOXO1, TLR4, PDX1, FOXO3, ZEB1, GLI2, GLI3, IL10, CTNNB1, TGFB2, CCNE1, GATA6, FOXF1, HMOX1, IFNG, YAP1, MKL1, MYC, AKT2, IHH, IRS2, EGR2, RARG, RB1, PPARGC1A, HMGA1, GTF2H1, EP300, HNF4A, JUN, FOXC2, ZFPM2, CLOCK, SOX2, SOX9, SRF, WT1, IGF1R, IL17A, HAND1, RUNX1, RUNX2, CEBPB, MAFB, NR4A2, CD276, SMAD3, SMAD2, HDAC4, NOTCH1, SP1, ETS1, NOTCH4, FOXE1, MTOR, BMP7, KLF4 8.77×10−27
GO:0031328 Positive regulation of cellular biosynthetic process 65 PPARA, MYOD1, HNF1A, PDGFB, PPARG, MITF, SPI1, FOXO1, TLR4, PDX1, ZEB1, FOXO3, GLI2, GLI3, IL10, CTNNB1, CCNE1, GATA6, FOXF1, HMOX1, IFNG, YAP1, MKL1, MYC, AKT2, IHH, IRS2, EGR2, RARG, RB1, PPARGC1A, HMGA1, GTF2H1, EP300, HNF4A, JUN, FOXC2, ZFPM2, CLOCK, SOX2, SOX9, SRF, WT1, IGF1R, IL17A, HAND1, AGT, RUNX1, RUNX2, CEBPB, MAFB, NR4A2, CD276, SMAD3, SMAD2, HDAC4, NOTCH1, SP1, ETS1, NOTCH4, FOXE1, JAK2, MTOR, BMP7, KLF4 1.79×10−26
GO:0010628 Positive regulation of gene expression 60 PPARA, MYOD1, HNF1A, PPARG, MITF, SPI1, FOXO1, PDX1, FOXO3, ZEB1, GLI2, GLI3, IL10, CTNNB1, CCNE1, GATA6, FOXF1, IFNG, YAP1, MKL1, MYC, IHH, RARG, EGR2, ESR1, RB1, HMGA1, PPARGC1A, GTF2H1, EP300, HNF4A, JUN, FOXC2, ZFPM2, CLOCK, CSF1, SOX2, SOX9, SRF, WT1, IL17A, HAND1, POU3F1, RUNX1, RUNX2, DNMT3B, CEBPB, MAFB, NR4A2, SMAD3, SMAD2, HDAC4, NOTCH1, SP1, ETS1, NOTCH4, FOXE1, DNMT1, BMP7, KLF4 3.06×10−26
CC GO:0031981 Nuclear lumen 69 BACH1, MYOD1, RNASEL, NBN, HNF1A, CDC14B, EZH2, FOXO1, ZEB1, GLI3, IQGAP1, CTNNB1, PNN, PRIM1, CCNE1, GATA6, FOXF1, HMOX1, YAP1, MYB, MYC, TWIST2, ZBTB20, RARG, UBE2I, RB1, PPARGC1A, HMGA1, GTF2H1, CCND1, EP300, HNF4A, CFL2, JUN, MATR3, CLOCK, ME1, MTDH, MCL1, HOXA11, SOX2, SOX9, SRF, WT1, HAND1, NPAT, POU3F1, WIPF1, DNMT3B, RUNX2, DNMT3A, CEBPB, TBX2, MAFB, PHB, NLK, SMAD3, SMAD2, ATM, POLD3, RPS6KA5, HDAC4, SNAI3, SP1, PLK1, TDG, JAK2, RERE, KLF4 1.25×10−14
GO:0005667 Transcription factor complex 26 MYOD1, HNF1A, HOXA11, SOX2, ZEB1, CTNNB1, GATA6, FOXF1, POU3F1, YAP1, RUNX2, TWIST2, RARG, TBX2, MAFB, SMAD3, SMAD2, RB1, HMGA1, GTF2H1, SNAI3, EP300, HNF4A, JUN, KLF4, CLOCK 3.85×10−14
GO:0005654 Nucleoplasm 49 MYOD1, NBN, HNF1A, EZH2, FOXO1, ZEB1, GLI3, CTNNB1, PNN, PRIM1, CCNE1, GATA6, FOXF1, YAP1, MYC, TWIST2, RARG, UBE2I, RB1, HMGA1, PPARGC1A, GTF2H1, CCND1, EP300, HNF4A, JUN, CLOCK, MCL1, HOXA11, SOX2, WT1, HAND1, NPAT, POU3F1, RUNX2, TBX2, MAFB, PHB, SMAD3, SMAD2, ATM, RPS6KA5, POLD3, HDAC4, SNAI3, PLK1, TDG, KLF4, RERE 1.44×10−12
GO:0031974 Membrane-enclosed lumen 75 BACH1, MYOD1, RNASEL, NBN, HNF1A, PDGFB, CDC14B, EZH2, FOXO1, ZEB1, GLI3, IQGAP1, CTNNB1, TGFB2, PNN, PRIM1, CCNE1, GATA6, FOXF1, HMOX1, YAP1, MYB, MYC, TWIST2, ZBTB20, RARG, UBE2I, RB1, PPARGC1A, HMGA1, GTF2H1, TIMM8A, CCND1, EP300, C1QBP, HNF4A, CFL2, JUN, MATR3, CLOCK, ME1, MTDH, MCL1, HOXA11, SOX2, SOX9, SRF, WT1, HAND1, NPAT, WIPF1, POU3F1, DNMT3B, RUNX2, DNMT3A, CEBPB, TBX2, MAFB, PHB, NLK, SMAD3, SMAD2, SPARC, ATM, POLD3, RPS6KA5, HDAC4, HSP90B1, SNAI3, SP1, PLK1, TDG, JAK2, RERE, KLF4 2.47×10−12
GO:0043233 Organelle lumen 74 BACH1, MYOD1, RNASEL, NBN, HNF1A, PDGFB, CDC14B, EZH2, FOXO1, ZEB1, GLI3, IQGAP1, CTNNB1, TGFB2, PNN, PRIM1, CCNE1, GATA6, FOXF1, HMOX1, YAP1, MYB, MYC, TWIST2, ZBTB20, RARG, UBE2I, RB1, PPARGC1A, HMGA1, GTF2H1, CCND1, EP300, C1QBP, HNF4A, CFL2, JUN, MATR3, CLOCK, ME1, MTDH, MCL1, HOXA11, SOX2, SOX9, SRF, WT1, HAND1, NPAT, WIPF1, POU3F1, DNMT3B, RUNX2, DNMT3A, CEBPB, TBX2, MAFB, PHB, NLK, SMAD3, SMAD2, SPARC, ATM, POLD3, RPS6KA5, HDAC4, HSP90B1, SNAI3, SP1, PLK1, TDG, JAK2, RERE, KLF4 2.78×10−12
MF GO:0043565 Sequence-specific DNA binding 51 BACH1, PPARA, MYOD1, HNF1A, PPARG, MITF, SPI1, FOXO1, FOXO3, ZEB1, PDX1, GLI2, GLI3, CTNNB1, GATA6, FOXF1, MYC, RARG, SOX13, ESR1, HMGA1, HNF4A, JUN, FOXC2, HOXA11, SOX2, SOX9, SRF, WT1, HAND1, HOXA5, POU3F1, DNMT3B, CREBL2, FOXL1, CEBPB, TBX2, MAFB, MSH2, NR4A2, SMAD3, SMAD2, HDAC4, NOTCH1, NR1I2, SP1, MEOX2, ETS1, FOXE1, KLF4, RERE 2.73×10−18
GO:0016563 Transcription activator activity 41 MYOD1, PPARA, HNF1A, MITF, PPARG, FOXO1, PDX1, FOXO3, ZEB1, SOX9, GLI2, WT1, CTNNB1, CCNE1, HAND1, GATA6, FOXF1, BCL2, NPAT, YAP1, POU3F1, MKL1, MYB, RUNX1, RUNX2, CEBPB, PHB, NR4A2, SMAD3, SMAD2, RB1, HMGA1, PPARGC1A, HDAC4, NOTCH1, NR1I2, EP300, SP1, HNF4A, FOXC2, KLF4 3.06×10−17
GO:0030528 Transcription regulator activity 79 BACH1, PPARA, MYOD1, HNF1A, ZNFX1, EZH2, PPARG, MITF, SPI1, FOXO1, PDX1, ZEB1, FOXO3, GLI2, GLI3, CTNNB1, CCNE1, GATA6, FOXF1, YAP1, MYB, MKL1, MYC, TWIST2, TWIST1, EGR2, RARG, SOX13, MECP2, ESR1, UBE2I, RB1, PPARGC1A, HMGA1, GTF2H1, MYCN, EP300, HNF4A, JUN, MAP3K10, FOXC2, ZFPM2, CLOCK, HOXA11, SOX2, SOX9, SRF, WT1, HAND1, HOXA5, BCL2, NPAT, POU3F1, RUNX1, DNMT3B, RUNX2, CREBL2, PLAG1, CEBPB, FOXL1, TBX2, MAFB, JARID2, PHB, NR4A2, SMAD3, SMAD2, HDAC4, NOTCH1, SNAI3, NR1I2, SP1, MEOX2, DMTF1, ETS1, FOXE1, FABP4, RERE, KLF4 3.63×10−16
GO:0003700 Transcription factor activity 61 BACH1, PPARA, MYOD1, HNF1A, ZNFX1, PPARG, MITF, SPI1, FOXO1, PDX1, FOXO3, ZEB1, GLI2, GLI3, CTNNB1, GATA6, FOXF1, MYC, TWIST2, RARG, EGR2, SOX13, ESR1, RB1, HMGA1, MYCN, EP300, HNF4A, JUN, FOXC2, CLOCK, HOXA11, SOX2, SOX9, SRF, WT1, HAND1, HOXA5, NPAT, POU3F1, RUNX1, RUNX2, CREBL2, PLAG1, CEBPB, FOXL1, TBX2, MAFB, NR4A2, SMAD3, SMAD2, SNAI3, NOTCH1, NR1I2, SP1, MEOX2, ETS1, DMTF1, FOXE1, RERE, KLF4 9.84×10−16
GO:0008134 Transcription factor binding 35 MYOD1, NBN, YWHAZ, MTDH, PPARG, ZEB1, SRF, CTNNB1, CCNE1, HAND1, GATA6, BCL2, NPAT, YAP1, MKL1, RUNX1, DNMT3B, RARG, MAFB, NLK, MECP2, SMAD3, SMAD2, RB1, HMGA1, PPARGC1A, HDAC4, EP300, NR1I2, HNF4A, ETS1, JUN, MAP3K10, DNMT1, ZFPM2 5.43×10−10
Open in a new tab

BP, biological process; CC, cellular component; MF, molecular function.

Table II.

The enriched pathways for target genes of differentially expressed microRNAs.

Term Description Gene number Gene symbol P-value
hsa05200 Pathways in cancer 41 FGFR3, PDGFB, MITF, PPARG, SPI1, FOXO1, GLI2, GLI3, TGFB2, CTNNB1, WNT2, CCNE1, CDC42, IGF1R, KRAS, BCL2, RHOA, RUNX1, MYC, AKT2, COL4A2, COL4A1, MSH2, MET, TGFBR2, SMAD3, RAF1, CDK6, FADD, SMAD2, RB1, SMO, NRAS, CCND1, HSP90B1, CDKN1B, EP300, ETS1, JUN, MTOR, LAMC1 5.32×10−16
hsa04510 Focal adhesion 22 COL4A2, COL4A1, PDGFB, COL3A1, MET, RAF1, COL2A1, CTNNB1, KDR, CDC42, IGF1R, CCND1, PDPK1, CCND3, CCND2, JUN, BCL2, COL1A2, RHOA, COL1A1, LAMC1, AKT2 1.70×10−7
hsa04060 Cytokine-cytokine receptor interaction 18 CCL1, PDGFB, CSF1, MET, TGFBR2, IL13, CCL27, IL10, TGFB2, KDR, AMH, ACVR2A, IL17A, IL23A, INHBE, INHBC, IFNG, BMP7 0.001139
hsa05215 Prostate cancer 17 PDGFB, FOXO1, RAF1, RB1, CTNNB1, CCNE1, NRAS, IGF1R, HSP90B1, CCND1, PDPK1, EP300, CDKN1B, KRAS, BCL2, MTOR, AKT2 2.48×10−9
hsa04010 MAPK signaling pathway 17 FGFR3, PDGFB, NLK, TGFBR2, MAP2K4, RAF1, HSPA1A, SRF, TAB2, TGFB2, RPS6KA5, CDC42, NRAS, KRAS, JUN, MYC, AKT2 0.003593
hsa04110 Cell cycle 16 YWHAZ, CDC14B, SMAD3, CDK6, SMAD2, RB1, ATM, TGFB2, CCNE1, CCND1, EP300, CDKN1B, CCND3, CCND2, PLK1, MYC 1.99×10−6
hsa04520 Adherens junction 15 CDC42, IGF1R, EP300, WASF3, NLK, TGFBR2, MET, RHOA, SMAD3, SMAD2, SNAI2,… YES1, SNAI1, IQGAP1, CTNNB1 2.25×10−8
hsa05210 Colorectal cancer 15 MSH2, MET, TGFBR2, SMAD3, RAF1, SMAD2, TGFB2, CTNNB1, IGF1R, CCND1, KRAS, BCL2, JUN, MYC, AKT2 7.17×10−8
hsa04310 Wnt signaling pathway 15 WNT2, CSNK1A1, CCND1, EP300, SFRP1, CCND3, CCND2, JUN, NLK, BTRC, RHOA, SMAD3, SMAD2, MYC, CTNNB1 8.61×10−5
hsa05212 Pancreatic cancer 11 CDC42, CCND1, KRAS, TGFBR2, SMAD3, RAF1, CDK6, SMAD2, RB1, TGFB2, AKT2 3.08×10−5
Open in a new tab

MAPK, mitogen-activated protein kinase.

Regulatory network analysis

The CTD database revealed that a total of 279 genes out of the 290 target genes were associated with pancreatitis and were targeted by 37 miRNAs. The regulatory network of pancreatitis-associated genes and the miRNAs that targeted them is presented in Fig. 2. It is of note that hsa-miR-15a targeted 56 genes, including CCND1, hsa-miR-16 targeted 63 genes including CCND1, hsa-miR-155 targeted 20 genes, including CCND1 and SMAD2, hsa-miR-375 targeted 44 genes, including AKT2 and CDK6 and hsa-miR-429 targeted 59 genes, including CCND1 Additionally, hsa-miR-16 targeted the highest number of genes in the regulatory network (Fig. 3).

Figure 2.

Figure 2.

Open in a new tab

Regulatory network of pancreatitis-associated genes and their target miRNAs. Circle nodes indicate pancreatitis-associated genes. Square nodes represent differentially expressed miRNAs. Red nodes stand for upregulated miRNAs and green nodes for down-regulated miRNAs. miRNA, microRNA.

Figure 3.

Figure 3.

Open in a new tab

The number of pancreatitis-associated genes targeted by each differentially expressed miRNA. miRNA, microRNA.

Discussion

In the present study, 14 upregulated and 39 downregulated miRNAs were identified in the screened pancreatitis samples compared with the control samples. A total 424 DE-miRNA-gene pairs were identified in the miRecords and miRWalk databases and 290 target genes of DE-miRNAs were obtained. It is of note that CCND1, AKT2, CDK6 and SMAD2 were involved in the pancreatic cancer pathway. Among the target genes, 279 genes were pancreatitis-associated genes, which were targeted by 37 miRNAs in the regulatory network.

miR-155, which has abnormal expression in the early stage of pancreatic cancer, may be used as a biomarker for intraductal papillary mucinous neoplasms (28). miR-155, miR-203, miR-210 and miR-222 have an oncogenic function in the development and progression of pancreatic cancer by targeting several genes; therefore, it is possible that the tumorigenesis and tumor progression of PDAC was influenced by a complex signaling network (29). miR-15a directly targeted multiple pancreatic cancer-associated genes and its downregulation may aid the proliferation of pancreatic cancer cells, indicating that it may be used as a novel therapeutic target for the disease (30). Pathway enrichment revealed that CCND1 was also involved in pancreatic cancer progression. Previous studies determined that CCND1, which is frequently co-overexpressed with CCND3 (31), integrated extracellular mitogenic signaling and impacted tumor cell migration in PDAC (32). Upregulated CCND1 promoted chemoresistance of pancreatic cancer cells by facilitating cell proliferation and suppression of drug-induced apoptosis (33). As a component of the E-cadherin-catenin cell adhesion complex, β-catenin may also contribute to the tumorigenesis of pancreatic cancer primarily by transactivation of CCND1 (34). In the regulatory network, hsa-miR-15a, hsa-miR-16, hsa-miR-155 and hsa-miR-429 targeted more genes than other miRNAs, and CCND1 was a common target gene. This indicated that hsa-miR-15a, hsa-miR-16, hsa-miR-155 and hsa-miR-429 may be important for the regulation of pancreatitis via CCND1.

In PDAC, SMAD protein expression levels in stromal fibroblasts and in epithelial tumor cells was correlated with both stromal features and a shorter postsurgical overall survival (35). Overexpression of the embryonic protein Nodal may lead to a metastatic phenotype in pancreatic carcinoma cells by the SMAD2/3 pathway. Therefore, Nodal may be a potential target for treatment of pancreatic cancer (36). A previous study determined that the expression levels of transforming growth factor β1 and SMAD2 in pancreatic adenocarcinoma were higher when compared with the normal control pancreatic parenchyma and their expression correlated with the tumor stage (37). In the present study, SMAD2 was enriched in the pathway of pancreatic cancer. In addition, SMAD2 was targeted by hsa-miR-155 in the regulatory network. Therefore, hsa-miR-155 may also function in pancreatitis by mediating SMAD2.

A previous study indicated that the anticancer agent benzyl isothiocyanate may inhibit the proliferation of pancreatic cancer cells via regulation of the expression levels of miR-375 and miR-221 (38). Elevated miR-375 affected proliferation, apoptosis and cell cycle distribution of pancreatic cancer cells; therefore, miR-375 may contribute to novel treatments for pancreatic cancer (39). Pathway enrichment also indicated that AKT2 and CDK6 were involved in pancreatic cancer progression. AKT2 is frequently activated by upstream perturbations of the phosphatidylinositol 3-kinase (PI3K)/AKT pathway, which may contribute to the pathogenesis of highly aggressive pancreatic cancer (40). It has been determined that suppression of extracellular signal-regulated kinase and PI3K/AKT pathways activate forkhead members of the class O (FOXO) transcription factor and strengthen transcriptional activity of sulforaphane-induced FOXO, resulting in cell cycle arrest and cell apoptosis in pancreatic cancer (41). Downregulation of CDK6 and p16 may be involved in cell cycle progression and transformation of endocrine tumors, including pancreatic endocrine neoplasms (42). Overexpression of CDK4 and CDK6 may result in a stronger response in patients with pancreatic neuroendocrine tumors treated with CDK4/6 inhibitors (43). In the regulatory network constructed in the present study, hsa-miR-375 targeted 44 genes, including AKT2 and CDK6, suggesting that hsa-miR-155 targeting AKT2 and CDK6 may also contribute to pancreatitis.

In conclusion, the present study performed a comprehensive bioinformatics analysis, and identified 14 upregulated and 39 downregulated miRNAs in pancreatitis samples compared with control samples. Additionally, hsa-miR-15a, hsa-miR-16 and hsa-miR-429 were determined to target CCND1, hsa-miR-155 targeted CCND1 and SMAD2 and hsa-miR-375 targeted AKT2 and CDK6; therefore, these miRNAs may contribute to the pathogenesis of pancreatitis and be potential therapeutic targets. However, these predictions require further experimental validation in future studies.

References

  • 1.Apte MV, Pirola RC, Wilson JS. Pancreas: Alcoholic pancreatitis-it's the alcohol, stupid. Nat Rev Gastroenterol Hepatol. 2009;6:321–322. doi: 10.1038/nrgastro.2009.84. [DOI] [PubMed] [Google Scholar]
  • 2.Yadav D, Hawes RH, Brand RE, Anderson MA, Money ME, Banks PA, Bishop MD, Baillie J, Sherman S, DiSario J, et al. Alcohol consumption, cigarette smoking, and the risk of recurrent acute and chronic pancreatitis. Arch Intern Med. 2009;169:1035–1045. doi: 10.1001/archinternmed.2009.125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Hofmeyr S, Meyer C, Warren BL. Serum lipase should be the laboratory test of choice for suspected acute pancreatitis. S Afr J Surg. 2014;52:72–75. doi: 10.7196/sajs.2003. [DOI] [PubMed] [Google Scholar]
  • 4.Turner B. Acute pancreatitis: Symptoms, diagnosis and management. Nurs Times. 2003;99:38–40. [PubMed] [Google Scholar]
  • 5.Banks PA, Freeman ML. Practice Parameters Committee of the American College of Gastroenterology: Practice guidelines in acute pancreatitis. Am J Gastroenterol. 2006;101:2379–2400. doi: 10.1111/j.1572-0241.2006.00856.x. [DOI] [PubMed] [Google Scholar]
  • 6.Lipshutz GS, Wilkinson AH. Pancreas-kidney and pancreas transplantation for the treatment of diabetes mellitus. Endocrinol Metab Clin North Am. 2007;36:1015–1038. doi: 10.1016/j.ecl.2007.07.010. [DOI] [PubMed] [Google Scholar]
  • 7.Tien YW, Lee PH, Yang CY, Ho MC, Chiu YF. Risk factors of massive bleeding related to pancreatic leak after pancreaticoduodenectomy. J Am Coll Surg. 2005;201:554–559. doi: 10.1016/j.jamcollsurg.2005.05.007. [DOI] [PubMed] [Google Scholar]
  • 8.Marik PE, Zaloga GP. Meta-analysis of parenteral nutrition versus enteral nutrition in patients with acute pancreatitis. BMJ. 2004;328:1407. doi: 10.1136/bmj.38118.593900.55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.GBD 2013 Mortality and Causes of Death Collaborators, corp-author. Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;385:117–171. doi: 10.1016/S0140-6736(14)61682-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Bartel DP. MicroRNAs: Target recognition and regulatory functions. Cell. 2009;136:215–233. doi: 10.1016/j.cell.2009.01.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Lund E, Güttinger S, Calado A, Dahlberg JE, Kutay U. Nuclear export of microRNA precursors. Science. 2004;303:95–98. doi: 10.1126/science.1090599. [DOI] [PubMed] [Google Scholar]
  • 12.Bauer AS, Keller A, Costello E, Greenhalf W, Bier M, Borries A, Beier M, Neoptolemos J, Büchler M, Werner J, et al. Diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis by measurement of microRNA abundance in blood and tissue. PLoS One. 2012;7:e34151. doi: 10.1371/journal.pone.0034151. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Bloomston M, Frankel WL, Petrocca F, Volinia S, Alder H, Hagan JP, Liu CG, Bhatt D, Taccioli C, Croce CM. MicroRNA expression patterns to differentiate pancreatic adenocarcinoma from normal pancreas and chronic pancreatitis. JAMA. 2007;297:1901–1908. doi: 10.1001/jama.297.17.1901. [DOI] [PubMed] [Google Scholar]
  • 14.Yu S, Lu Z, Liu C, Meng Y, Ma Y, Zhao W, Liu J, Yu J, Chen J. miRNA-96 suppresses KRAS and functions as a tumor suppressor gene in pancreatic cancer. Cancer Res. 2010;70:6015–6025. doi: 10.1158/0008-5472.CAN-09-4531. [DOI] [PubMed] [Google Scholar]
  • 15.Mees ST, Mardin WA, Sielker S, Willscher E, Senninger N, Schleicher C, Colombo-Benkmann M, Haier J. Involvement of CD40 targeting miR-224 and miR-486 on the progression of pancreatic ductal adenocarcinomas. Ann Surg Oncol. 2009;16:2339–2350. doi: 10.1245/s10434-009-0531-4. [DOI] [PubMed] [Google Scholar]
  • 16.Liffers ST, Munding JB, Vogt M, Kuhlmann JD, Verdoodt B, Nambiar S, Maghnouj A, Mirmohammadsadegh A, Hahn SA, Tannapfel A. MicroRNA-148a is down-regulated in human pancreatic ductal adenocarcinomas and regulates cell survival by targeting CDC25B. Lab Invest. 2011;91:1472–1479. doi: 10.1038/labinvest.2011.99. [DOI] [PubMed] [Google Scholar]
  • 17.Ohuchida K, Mizumoto K, Lin C, Yamaguchi H, Ohtsuka T, Sato N, Toma H, Nakamura M, Nagai E, Hashizume M, Tanaka M. MicroRNA-10a is overexpressed in human pancreatic cancer and involved in its invasiveness partially via suppression of the HOXA1 gene. Ann Surg Oncol. 2012;19:2394–2402. doi: 10.1245/s10434-012-2252-3. [DOI] [PubMed] [Google Scholar]
  • 18.Huber W, Von Heydebreck A, Sültmann H, Poustka A, Vingron M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics. 2002;18:S96–S104. doi: 10.1093/bioinformatics/18.suppl_1.S96. (Suppl 1) [DOI] [PubMed] [Google Scholar]
  • 19.Smyth G. Limma: Linear models for microarray data. In: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W, editors. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer Science & Business Media; New York, NY: 2005. pp. 397–420. [DOI] [Google Scholar]
  • 20.Benjamini Y. Discovering the false discovery rate. J Royal Stat Soc: Series B (Statistical Methodology) 2010;72:405–416. doi: 10.1111/j.1467-9868.2010.00746.x. [DOI] [Google Scholar]
  • 21.Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T. miRecords: An integrated resource for microRNA-target interactions. Nucleic Acids Res. 2009;37:D105–D110. doi: 10.1093/nar/gkn851. (Database issue) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Dweep H, Sticht C, Pandey P, Gretz N. miRWalk-database: Prediction of possible miRNA binding sites by ‘walking’ the genes of three genomes. J Biomed Inform. 2011;44:839–847. doi: 10.1016/j.jbi.2011.05.002. [DOI] [PubMed] [Google Scholar]
  • 23.Tweedie S, Ashburner M, Falls K, Leyland P, McQuilton P, Marygold S, Millburn G, Osumi-Sutherland D, Schroeder A, Seal R, et al. FlyBase: Enhancing Drosophila gene ontology annotations. Nucleic Acids Res. 2009;37:D555–D559. doi: 10.1093/nar/gkn788. (Database issue) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30. doi: 10.1093/nar/28.1.27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44–57. doi: 10.1038/nprot.2008.211. [DOI] [PubMed] [Google Scholar]
  • 26.Davis AP, Grondin CJ, Lennon-Hopkins K, Saraceni-Richards C, Sciaky D, King BL, Wiegers TC, Mattingly CJ. The Comparative Toxicogenomics Database's 10th year anniversary: Update 2015. Nucleic Acids Res. 2015;43:D914–D920. doi: 10.1093/nar/gku935. (Database issue) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Saito R, Smoot ME, Ono K, Ruscheinski J, Wang PL, Lotia S, Pico AR, Bader GD, Ideker T. A travel guide to Cytoscape plugins. Nat Methods. 2012;9:1069–1076. doi: 10.1038/nmeth.2212. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Habbe N, Koorstra JB, Mendell JT, Offerhaus GJ, Ryu JK, Feldmann G, Mullendore ME, Goggins MG, Hong SM, Maitra A. MicroRNA miR-155 is a biomarker of early pancreatic neoplasia. Cancer Biol Ther. 2009;8:340–346. doi: 10.4161/cbt.8.4.7338. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Greither T, Grochola LF, Udelnow A, Lautenschläger C, Würl P, Taubert H. Elevated expression of microRNAs 155, 203, 210 and 222 in pancreatic tumors is associated with poorer survival. Int J Cancer. 2010;126:73–80. doi: 10.1002/ijc.24687. [DOI] [PubMed] [Google Scholar]
  • 30.Zhang XJ, Ye H, Zeng CW, He B, Zhang H, Chen YQ. Dysregulation of miR-15a and miR-214 in human pancreatic cancer. J Hematol Oncol. 2010;3:46. doi: 10.1186/1756-8722-3-46. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Ebert MP, Hernberg S, Fei G, Sokolowski A, Schulz HU, Lippert H, Malfertheiner P. Induction and expression of cyclin D3 in human pancreatic cancer. J Cancer Res Clin Oncol. 2001;127:449–454. doi: 10.1007/s004320100235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Radulovich N, Pham NA, Strumpf D, Leung L, Xie W, Jurisica I, Tsao MS. Differential roles of cyclin D1 and D3 in pancreatic ductal adenocarcinoma. Mol Cancer. 2010;9:24. doi: 10.1186/1476-4598-9-24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Biliran H, Jr, Wang Y, Banerjee S, Xu H, Heng H, Thakur A, Bollig A, Sarkar FH, Liao JD. Overexpression of cyclin D1 promotes tumor cell growth and confers resistance to cisplatin-mediated apoptosis in an elastase-myc transgene-expressing pancreatic tumor cell line. Clin Cancer Res. 2005;11:6075–6086. doi: 10.1158/1078-0432.CCR-04-2419. [DOI] [PubMed] [Google Scholar]
  • 34.Qiao Q, Ramadani M, Gansauge S, Gansauge F, Leder G, Beger HG. Reduced membranous and ectopic cytoplasmic expression of beta-catenin correlate with cyclin D1 overexpression and poor prognosis in pancreatic cancer. Int J Cancer. 2001;95:194–197. doi: 10.1002/1097-0215(20010520)95:3&#x0003c;194::AID-IJC1033&#x0003e;3.3.CO;2-D. [DOI] [PubMed] [Google Scholar]
  • 35.Handra-Luca A, Hammel P, Sauvanet A, Ruszniewski P, Couvelard A. Tumoral epithelial and stromal expression of SMAD proteins in pancreatic ductal adenocarcinomas. J Hepatobiliary Pancreat Sci. 2013;20:294–302. doi: 10.1007/s00534-012-0518-6. [DOI] [PubMed] [Google Scholar]
  • 36.Duan W, Li R, Ma J, Lei J, Xu Q, Jiang Z, Nan L, Li X, Wang Z, Huo X, et al. Overexpression of Nodal induces a metastatic phenotype in pancreatic cancer cells via the Smad2/3 pathway. Oncotarget. 2015;6:1490–1506. doi: 10.18632/oncotarget.2686. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Pan X, Wang X, Wang Z, Zhu Q, Han Y. Expression of Smad2 and TGFβ 1 in human pancreatic carcinoma tissues and its significance. Prog Mod Biomed. 2012;6:032. [Google Scholar]
  • 38.Basu A, Alder H, Khiyami A, Leahy P, Croce CM, Haldar S. MicroRNA-375 and MicroRNA-221 Potential noncoding RNAs associated with antiproliferative activity of Benzyl isothiocyanate in pancreatic cancer. Genes Cancer. 2011;2:108–119. doi: 10.1177/1947601911409212. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Zhou J, Song S, Cen J, Zhu D, Li D, Zhang Z. MicroRNA-375 is downregulated in pancreatic cancer and inhibits cell proliferation in vitro. Oncol Res. 2012;20:197–203. doi: 10.3727/096504013X13589503482734. [DOI] [PubMed] [Google Scholar]
  • 40.Altomare DA, Tanno S, De Rienzo A, Klein-Szanto AJ, Tanno S, Skele KL, Hoffman JP, Testa JR. Frequent activation of AKT2 kinase in human pancreatic carcinomas. J Cell Biochem. 2002;87:470–476. doi: 10.1002/jcb.10287. [DOI] [PubMed] [Google Scholar]
  • 41.Roy SK, Srivastava RK, Shankar S. Inhibition of PI3K/AKT and MAPK/ERK pathways causes activation of FOXO transcription factor, leading to cell cycle arrest and apoptosis in pancreatic cancer. J Mol Signal. 2010;5:10. doi: 10.1186/1750-2187-5-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Tomita T. Cyclin-dependent kinase (cdk6) and p16 in pancreatic endocrine neoplasms. Pathology. 2004;36:566–570. doi: 10.1080/00313020400011342. [DOI] [PubMed] [Google Scholar]
  • 43.Tang LH, Contractor T, Clausen R, Klimstra DS, Du YC, Allen PJ, Brennan MF, Levine AJ, Harris CR. Attenuation of the retinoblastoma pathway in pancreatic neuroendocrine tumors due to increased cdk4/cdk6. Clin Cancer Res. 2012;18:4612–4620. doi: 10.1158/1078-0432.CCR-11-3264. [DOI] [PubMed] [Google Scholar]

Articles from Molecular Medicine Reports are provided here courtesy of Spandidos Publications

RESOURCES