Open Access

Identification of key pathways and candidate genes in pancreatic ductal adenocarcinoma using bioinformatics analysis

  • Authors:
    • Yiping He
    • Yan Liu
    • Jianping Gong
    • Changan Liu
    • Hua Zhang
    • Hao Wu
  • View Affiliations

  • Published online on: February 14, 2019     https://doi.org/10.3892/ol.2019.10041
  • Pages: 3751-3764
  • Copyright : © He et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY 4.0].

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a high degree of malignancy that is difficult to diagnose and treat. The present study integrated PDAC cohort profile datasets to identify key candidate genes and pathways involved in the pathogenesis of the disease. The expression profiles of GSE28735 included 45 PDCA and matching pairs of adjacent non‑tumor tissue. Differentially expressed genes (DEGs) were sorted and candidate genes and pathway enrichment were analyzed. A DEG‑associated protein‑protein interaction (PPI) network was constructed. A total of 424 DEGs were identified in PDAC, including 159 upregulated genes and 265 downregulated genes. Gene Ontology analysis results indicated that upregulated DEGs were significantly enriched in biological process, molecular function and cellular component categories. Kyoto Encyclopedia of Genes and Genomes pathway analysis demonstrated that the upregulated DEGs were enriched in ‘pancreatic secretion’, ‘protein digestion’ and ‘absorption’. Downregulated DEGs were enriched in ‘ECM‑receptor interaction’, ‘focal adhesion’ and ‘PI3K/AKT’ signaling pathways. The PPI network revealed that these genes were involved in significant pathways, including ‘ECM organization’ signaling pathways (Hippo signaling pathway, TGF‑β signaling pathway, Hedgehog signaling pathway and Wnt signaling pathway), ‘serine‑type peptidase activity’ signaling pathway (PI3K‑Akt signaling pathway, TNF‑α signaling pathway and Wnt signaling pathway) and ‘extracellular region’ signaling pathways (RTP signaling pathway, G protein‑coupled receptor signaling pathway and RAS‑RAF‑MAPK signaling pathway). The identification of these candidate genes and pathways sheds light on the etiology and molecular mechanisms of PDAC and may guide the development of novel therapies for pancreatic cancer.

Introduction

Pancreatic cancer has a poor prognosis, with a median survival time of 3–6 month and a 5-year survival rate of less than 5% (13). The most common type of pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC), accounting for ~90% of pancreatic cancer cases (4). Although numerous studies have focused on the pathogenesis and progression of pancreatic cancer, the etiology and molecular mechanisms of pancreatic cancer remain unclear (5,6). Previous scientific studies have demonstrated that the occurrence and progression of pancreatic cancer involve the interaction of several factors, including gene mutations and environmental conditions (7,8). Thus far, there remains a lack of information regarding the molecular mechanisms that cause the development and progression of pancreatic cancer that would allow for improved precision therapies. Therefore, understanding the molecular mechanisms of pancreatic cancer can provide an effective basis for early prevention, diagnosis and treatment.

The advent of the gene chip and high-throughput gene analysis platforms allows for the rapid detection of gene expression in a microarray, which is particularly suitable for screening differentially expressed genes (DEGs) (9). With the widespread application of gene chip technology in cancer research, a large amount of genetic data has been produced and stored in public gene databases. Classification, integration and analysis of these data can provide valuable insights and evidence for cancer research. In the past few years, numerous gene chip expression profiles have been used to study the pathogenesis and development of PDAC and hundreds of DEGs have been identified (10). However, due to differences in sample size and limitations of the studies, no reliable biomarkers were identified. The combination of gene chip and biological information analysis technology can be used to monitor the expression of DEGs in the development and progression of PDAC and to elucidate the signaling pathways involved, potentially revealing targets which can be modulated to treat PDAC (11).

In the present study, the original GSE28735 data set (12) was downloaded from the Gene Expression Omnibus (GEO) database (13). The dataset contained the gene expression profiles of 45 matching pairs of pancreatic tumor and adjacent non-tumor tissues from 45 patients with PDAC. DEGs were detected by comparing the gene expression profiles between tumor tissues and paracancerous tissues in patients with PDAC. Subsequently, the DEGs were filtered using the Morpheus website (https://software.broadinstitute.org/morpheus/) with data processing standard. Then, the DEGs were screened using the Gene-Spring software (version 11.5; Agilent Technologies, Inc., Santa Clara, CA, USA), followed by Gene Ontology (GO); (www.geneontology.org) and pathway enrichment analysis. In addition, a protein-protein interaction (PPI) network was established and three significant modules were analyzed. The analysis of the biological pathways underlying the development of PDAC may provide information for its diagnosis, prognosis and treatment.

Materials and methods

Microarray data

The gene expression profiles of the GSE28735 dataset were downloaded from the GEO database. The GPL6244 [HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array platform (Affymetrix; Thermo Fisher Scientific, Inc., Waltham, MA, USA) was used. The GSE28735 dataset contained 90 samples, including 45 PDAC tumor samples and 45 matching pairs of adjacent non-tumor tissue samples.

Identification of DEGs in GSE28735

The raw expression data files include TXT files (Affymetrix platform) used for analysis by processing using the Morpheus website. Data were categorized into two groups with similar expression patterns in PDAC tumor samples and matching pairs of adjacent non-tumor tissue samples. A t-test was used to identify the DEGs and |log2 fold change|≥1 and P<0.05 were considered statistically significant.

Gene ontology and pathway enrichment analysis of DEGs

GO analysis was used to annotate genes and classify up and downregulated DEGs. GO terms are divided into three main categories: Biological process (BP), cellular component (CC) and molecular function (MF). The Kyoto Encyclopedia of Genes and Genomes (KEGG; www.kegg.jp) website is an online database which contains defined and associated gene sets and their pathways. The Database for Annotation, Visualization and Integrated Discovery (DAVID; david.ncifcrf.gov) allows analysis of gene lists and provides biological information regarding genes. To analyze the upregulated and downregulated genes in DEGs, GO and KEGG pathway analysis were used in the DAVID database. P<0.05 was considered to indicate a statistically significant difference.

Integration of PPI network

The Search Tool for the Retrieval of Interacting Genes (STRING; www.string-db.org) was used to evaluate the PPI information. The PPI network served to identify the key genes and Cytoscape software (version 3.51; www.cytoscape.org) was used to draw the network diagram. The topology of the PPI network was analyzed and the extent of the expression of each gene was calculated. P<0.05 was considered to indicate a statistically significant difference.

Results

Identification of DEGs in pancreatic cancer

A total of 45 PDAC tumor samples and 45 matching pairs of adjacent non-tumor tissue samples were analyzed. A total of 424 DEGs were identified from GSE28735, including 159 upregulated and 265 downregulated genes (Table I). The heat map of DEG expression, presenting the top 50 upregulated and 50 downregulated genes was constructed using the web-based tool Morpheus (Fig. 1).

Table I.

A total of 424 DEGs were identified from the GSE28735 dataset, including 159 upregulated genes and 265 downregulated genes in PDAC tissues, compared to adjacent non-tumor tissue samples.

Table I.

A total of 424 DEGs were identified from the GSE28735 dataset, including 159 upregulated genes and 265 downregulated genes in PDAC tissues, compared to adjacent non-tumor tissue samples.

Differential expressionGene symbol
UpregulatedEPB41L4B, FAM129A, SLC1A2, KLKB1, ALDH1A1, PAH, CHGA, CHST9, SEMA6A, SERPINA5, KIF1A, CHRDL1, SLC16A10, CLU, MIR27B, PRKAR2B, FAM3B, ADHFE1, LONRF2, DPT, CHRM3, SLC3A1, ABAT, PPY, BNIP3, NUCB2, GPHA2, ATRNL1, ESRRG, ABCA8, FAM150B, ONECUT1, PRSS3P2, OR4D5, CXCL12, IL22RA1, TSPAN7, F8, GCG, ADGRV1, SV2B, UGT2B11, SPINK1, PROX1, ANGPTL1, UNC79, AMY2B, MCOLN3, AQP12B, FAM159B, FOSB, BTG2, SLC43A1, FLRT2, GSTA1, AQP12B, C5, SCG3, CCDC141, DPP10, PKHD1, PRSS3, C2CD4B, MT1G, HOMER2, GRB14, LYVE1, BACE1, SLC39A5, CD36, RGN, SYCN, GC, EPHX2, REG3G, DCDC2, GUCA1C, SST, PCSK1, PDZK1P1, BEX1, PRSS2, LIFR, GRPR, SLC30A8, MIR217, LMO3, ANKRD62, CTNND2, PM20D1, CFTR, GNMT, TFPI2, SLC17A4, PAK3, GSTA2, AMY2B, G6PC2, TTN, CELP, SLC4A4, PRSS3P2, C6, TTR, QP8, SLC7A2, KCNJ16, PDK4, OR8D4, REG3A, FABP4, NRCAM, NRG4, PAIP2B, GATM, FGL1, ACADL, ADH1B, TRHDE, RBPJL, SCGN, REG1A, PRSS1, CPB1, SLC16A12, ANPEP, TMED6, KLK1, RO1B, F11, CTRB2, AOX1, NR5A2, KIAA1324, CELA3B, EGF, CPA1, PDIA2, REG1CP, EG1B, PNLIP, CTRB1, CTRL, CELA3A, CELA2B, CELA2A, PLA2G1B, SERPINI2, CLPS, ERP27, FAM24B, ALB, CPA2, CEL, GP2, CTRC, IAPP, PNLIPRP2, PNLIPRP1
DownregulatedCEACAM5, SLC6A14, LAMC2, GALNT5, TSPAN1, CTSE, POSTN, CEACAM6, ANXA10, LAMB3, ITGA2, TMPRSS4, FN1, COL11A1, SERPINB5, DPCR1, AGR2, CLDN18, ITGB6, KRT19, GABRP, CST1, VSIG1, SULF1, TFF1, COL17A1, SLC2A1, PLAC8, CEMIP, SLPI, CP, AHNAK2, MMP12, COL12A1, TMC5, VCAN, MUC17, KRT7, ANLN, INHBA, TRIM31, LIPH, CDH3, TRIM31, SCEL, NOX4, THBS2, EGLN3, C13, ADGRF1, MBOAT2, ANTXR1, TCN1, ANKRD22, COL10A1, CXCL5, XYD3, KRT17, BCAS1, ITGA3, SDR16C5, EDIL3, APOL1, UGT1A3, COL1A1, MMP11, FERMT1, FAP, ANXA8L1, CDH11, COL1A2, MET, FNDC1, FBXO32, COMP, NQO1, ACSL5, MLPH, NPR3, ANXA8L1, MIA-RAB4B, COL8A1, GCNT3, IGFL2, ADAMTS12, TNS4, CAPG, TRIM29, TSPAN8, CYP2C18, TRIM31, TMEM45B, MATN3, COL5A2, PLAU, PADI1, ITGA11, COL3A1, CCL20, IGFP5, LAMA3, HK2, IFI27, MYOF, PLAT, FER1L6, KRT6C, ECT2, LY75, MMP14, TOP2A, DNRA, LEF1, CENPF, TNFAIP6, ITGB4, PLEK2, CEACAM1, LAMP5, TMC7, NPR3, OLR1, SERPINB3, ANO1, DHRS9, SLC6A6, MICAL2, MUC16, ARNTL2, PTPRR, KYNU, NRP2, S100A14, CD109, BAIAP2L1, AFAP1-AS1, LOXL2, FGD6, CST2, IFI44L, S100P, MMP1, COL6A3, SL44A4, ERO1A, ASPM, BGN, DKK1, STYK1, MMP7, RUNX2, NT5E, TGM2, HEPH, KRT17, GPX2, OSBPL3, LMO7, GPRC5A, EPHA4, DCP1, GF2BP3, S100A16, PXDN, MKI67, EFNA5, KRT17, MELK, ADAM9, SLC22A3, MST1R, ACTA2, FF2, LCN2, PLPP4, ADAM28, MXRA5, DPYSL3, TGFBI, XDH, CCL18, OAS1, ABHD17C, RHBDL2, HIST1H3H, MUC1, INPP4B, AEBP1, MMP9, MTMR11, FOXQ1, ENO2, OCIAD2, DLGAP5, HPGD, TPX2, PLA2R1, SRPX2, LRRN1, SLCO1B3, SEMA3C, IL1RAP, SYTL2, FER1L4, DSG2, SULF2, HOXB5, MFP5, IL2RG, SULT1B1, CORIN, SLC9A2, GJB2, ADAM12, PLS1, AK4, ATP2C2, GREM1, ETV1, LTBP1, OAS2, ASAP2, SGIP1, PGM2L1, DDX60, DGKH, KCNN4, MALL, P4HA1, ANXA3, TSK, EPYC, NRP2, FUT3, ADAMTS6, KRT6A, IL1R2, DCBLD2, NMU, EFNB2, ST6GALNAC1, ANGPT2, FCGR3B, KIF23, FBN1, PKM, SEMA7A, TRIM16, RTKN2, SLC26A9, NTM, PCDH7, RAI14, SULT1C2, ESM1, AREG, DSG3, GPX8, MACC1, CTHRC1, HIST1H3I, SCNN1A, SLC16A3

[i] DEGs, differentially expressed genes; PDAC, pancreatic ductal adenocarcinoma.

GO term and pathway enrichment analyses

To further elucidate the function of the selected genes, the online software DAVID was used to perform DEG GO analysis. As aforementioned, GO analysis results classify DEG functions and pathways into three functional groups: BP, CC and MF. For BP, the upregulated DEGs were enriched in ‘digestion’, ‘lipid digestion’ and ‘proteolysis’, while the downregulated DEGs were enriched in ‘ECM organization’, ‘extracellular structure organization’ and ‘cell adhesion’ (Tables II and III). For CC, the upregulated DEGs were enriched in the ‘extracellular region’, and the downregulated DEGs were enriched in ‘extracellular region’ and ‘ECM’ (Tables II and III). For MF, the upregulated DEGs were enriched in ‘serine-type peptidase activity’, ‘serine hydrolase activity’ and ‘peptidase activity’, and the downregulated DEGs were enriched in ‘ECM structural constituent’, ‘integrin binding’ and ‘cell adhesion molecule binding’ (Tables II and III).

Table II.

GO analysis of upregulated DEGs associated with PDAC.

Table II.

GO analysis of upregulated DEGs associated with PDAC.

CategoryTermGene functionCountP-value
BPGO:0007586Digestion18 3.31×10−14
BPGO:0044241Lipid digestion6 9.18×10−7
BPGO:0006508Proteolysis35 1.08×10−6
BPGO:0006766Vitamin metabolic process9 1.57×10−5
BPGO:0009235Cobalamin metabolic process5 2.56×10−5
BPGO:0015850Organic hydroxy compound transport10 4.03×10−5
BPGO:0006767Water-soluble vitamin metabolic process7 9.77×10−5
BPGO:0006629Lipid metabolic process26 1.11×10−4
BPGO:0046903Secretion23 1.23×10−4
BPGO:0032940Secretion by cell21 1.67×10−4
CCGO:0005576Extracellular region90 1.47×10−17
CCGO:0005615Extracellular space47 4.64×10−15
CCGO:0044421Extracellular region part78 8.75×10−15
CCGO:0031988Membrane-bounded vesicle64 6.61×10−9
CCGO:0070062Extracellular exosome52 1.46×10−7
CCGO:1903561Extracellular vesicle52 1.72×10−7
CCGO:0043230Extracellular organelle52 1.74×10−7
CCGO:0030141Secretory granule15 4.27×10−6
CCGO:0060205Cytoplasmic membrane-bounded vesicle lumen8 4.51×10−5
CCGO:0031983Vesicle lumen8 4.80×10−5
MFGO:0008236Serine-type peptidase activity18 2.13×10−10
MFGO:0017171Serine hydrolase activity18 2.51×10−10
MFGO:0008233Peptidase activity27 3.41×10−10
MFGO:0004252Serine-type endopeptidase activity17 4.20×10−10
MFGO:0070011Peptidase activity, acting on L-amino acid peptides26 8.65×10−10
MFGO:0004175Endopeptidase activity18 8.45×10−7
MFGO:0008238Exopeptidase activity9 4.27×10−6
MFGO:0008235Metalloexopeptidase activity6 1.98×10−4
MFGO:0004806Triglyceride lipase activity4 7.93×10−4
MFGO:0005179Hormone activity6 3.54×10−3

[i] GO, gene ontology; DEGs, differentially expressed genes; PDAC, pancreatic ductal adenocarcinoma; BP, biological process; MF, molecular function; CC, cellular component.

Table III.

GO analysis of downregulated DEGs associated with PDAC.

Table III.

GO analysis of downregulated DEGs associated with PDAC.

CategoryTermGene functionCountP-value
BPGO:0030198Extracellular matrix organization42 2.61×10−27
BPGO:0043062Extracellular structure organization42 2.94×10−27
BPGO:0007155Cell adhesion67 2.12×10−15
BPGO:0022610Biological adhesion67 2.52×10−15
BPGO:0016477Cell migration53 3.81×10−14
BPGO:0030574Collagen catabolic process15 1.91×10−13
BPGO:0051674Localization of cell55 2.69×10−13
BPGO:0048870Cell motility55 2.69×10−13
BPGO:0044243Multicellular organism catabolic process15 8.59×10−13
BPGO:0006928Movement of cell or subcellular component64 1.18×10−12
CCGO:0005576Extracellular region148 4.33×10−25
CCGO:0044421Extracellular region part133 3.86×10−24
CCGO:0031012Extracellular matrix42 8.29×10−18
CCGO:0005578Proteinaceous extracellular matrix35 1.90×10−17
CCGO:0044420Extracellular matrix component19 1.21×10−12
CCGO:0005615Extracellular space59 6.91×10−12
CCGO:0070062Extracellular exosome84 6.11×10−10
CCGO:1903561Extracellular vesicle84 7.84×10−10
CCGO:0043230Extracellular organelle84 7.99×10−10
CCGO:0031988Membrane-bounded vesicle96 9.00×10−9
MFGO:0005201Extracellular matrix structural constituent13 1.27×10−9
MFGO:0005178Integrin binding14 2.41×10−9
MFGO:0050839Cell adhesion molecule binding25 2.52×10−8
MFGO:0019838Growth factor binding13 2.08×10−7
MFGO:0004222 Metalloendopeptidase activity12 4.62×10−7
MFGO:0005539Glycosaminoglycan binding15 1.20×10−6
MFGO:0005509Calcium ion binding28 2.19×10−6
MFGO:0005518Collagen binding9 2.67×10−6
MFGO:0005102Receptor binding43 3.59×10−6
MFGO:0008237Metallopeptidase activity13 1.34×10−5

[i] GO, gene ontology; DEGs, differentially expressed genes; PDAC, pancreatic ductal adenocarcinoma; BP, biological process; MF, molecular function; CC, cellular component.

KEGG pathway analysis in pancreatic cancer

KEGG pathway analysis was used to analyze the most significantly enriched pathways of the upregulated DEGs and downregulated DEGs. The upregulated DEGs were enriched in ‘pancreatic secretion’, ‘protein digestion and absorption’ and ‘fat digestion and absorption’ (Table IV). The downregulated DEGs were enriched in ‘ECM-receptor interaction’, ‘focal adhesion’ and ‘PI3K/Akt signaling’ pathways (Table IV).

Table IV.

KEGG pathway analysis of DEGs associated with PDAC.

Table IV.

KEGG pathway analysis of DEGs associated with PDAC.

A, Upregulated

PathwayNameCountP-valueGenes
hsa04972Pancreatic secretion19 4.1×10−18PNLIP, CELA3A, PNLIPRP1, CELA3B, PNLIPRP2, PRSS1, CFTR, CEL, CHRM3, PRSS2, PRSS3, CPA2, PLA2G1B, CELA2B, CELA2A, CPA1, CPB1, SLC4A4, CTRL
hsa04974Protein digestion and absorption13 1.9×10−10CELA3A, CELA3B, SLC16A10, PRSS2, PRSS3, PRSS1, CPA2, CELA2B, CELA2A, CPA1, SLC3A1, CPB1, CTRL
hsa04975Fat digestion and absorption7 4.1×10−6PNLIP, CEL, CLPS, PNLIPRP1, PNLIPRP2, CD36, PLA2G1B
hsa04610Complement and coagulation cascades6 1.0×10−3F11, KLKB1, SERPINA5, C6, C5, F8
hsa00982Drug metabolism- cytochrome P4505 7.1×10−3GSTA1, GSTA2, AOX1, UGT2B11, ADH1B
hsa00561Glycerolipid metabolism4 2.7×10−2PNLIP, CEL, PNLIPRP1, PNLIPRP2
hsa04950Maturity onset diabetes of the young3 3.4×10−2ONECUT1, IAPP, NR5A2
hsa00830Retinol metabolism4 3.7×10−2ALDH1A1, AOX1, UGT2B11, ADH1B
hsa04971Gastric acid secretion4 4.9×10−2KCNJ16, CHRM3, CFTR, SST
hsa00980Metabolism of xenobiotics by cytochrome P4504 5.1×10−2GSTA1, GSTA2, UGT2B11, ADH1B

B, Downregulated

PathwayNameCountP-valueGenes

hsa04512ECM-receptor interaction17 1.4×10−13COL3A1, ITGB4, ITGA11, ITGA2, ITGA3, COL5A2, LAMB3, LAMA3, COMP, ITGB6, COL6A3, COL1A2, LAMC2, COL1A1, THBS2, COL11A1, FN1
hsa04510Focal adhesion18 1.1×10−8COL3A1, MET, ITGB4, ITGA11, ITGA2, ITGA3, COL5A2, LAMB3, LAMA3, COMP, COL6A3, ITGB6, COL1A2, LAMC2, COL1A1, THBS2, COL11A1, FN1
hsa04151PI3K-Akt signaling pathway21 20×10−7COL3A1, MET, ITGA11, ITGB4, ITGA2, ITGA3, COL5A2, LAMB3, LAMA3, COMP, COL6A3, ITGB6, COL1A2, LAMC2, EFNA5, IL2RG, COL1A1, THBS2, ANGPT2, COL11A1, FN1
hsa05146Amoebiasis11 4.4×10−6IL1R2, LAMB3, LAMA3, COL3A1, COL1A2, LAMC2, COL1A1, SERPINB3, COL11A1, COL5A2, FN1
hsa04974Protein digestion and absorption10 7.0×10−6KCNN4, COL17A1, COL3A1, COL6A3, COL1A2, COL12A1, COL1A1, COL11A1, COL5A2, COL10A1
hsa05412Arrhythmogenic right ventricular cardiomyopathy7 7.2×10−4DSG2, ITGB6, ITGA11, ITGB4, LEF1, ITGA2, ITGA3
hsa05202Transcriptional misregulation in cancer10 1.0×10−3PLAT, IL1R2, MMP9, MET, ETV1, RUNX2, HPGD, HIST1H3H, PLAU, HIST1H3I
hsa05222Small cell lung cancer6 9.6×10−3LAMB3, LAMA3, ITGA2, LAMC2, ITGA3, FN1
hsa05230Central carbon metabolism in cancer5 1.6×10−2SLC16A3, PKM, SLC2A1, MET, HK2
hsa05410Hypertrophic cardiomyopathy5 3.1×10−2ITGB6, ITGA11, ITGB4, ITGA2, ITGA3

[i] KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes; PDAC, pancreatic ductal adenocarcinoma.

PPI and modular analysis in pancreatic cancer

Using the STRING online database and Cytoscape software analysis, a total of 386 DEGs (143 upregulated and 243 downregulated genes) of the 424 commonly altered DEGs were filtered into the DEGs PPI network complex, including 424 nodes and 1090 edges (Fig. 2A). The 10 nodes with the highest degree were cystic fibrosis transmembrane conductance regulator (CFTR), SLC7A2 (solute carrier family 7 member 2), C-C motif chemokine ligand 18 (CCL18), pyruvate dehydrogenase kinase 4 (PDK4), BAI1 associated protein 2 like 1 (BAIAP2L1), integrin subunit α3 (ITGA3), carboxypeptidase A1 (CPA1), G protein-coupled receptor class C group 5 member A (GPRC5A), serine/threonine/tyrosine kinase 1 (STYK1), and ST6 N-acetylgalactosaminide α-2, 6-sialyltransferase 1 (ST6GALNAC1). Among the upregulated DEGs, a total of 143 DEGs were filtered into the DEG PPI network complex including 143 nodes and 263 edges (Fig. 2B), which were mainly associated with ‘digestion’, ‘serine-type peptidase activity’ and the ‘extracellular region’ (Table V). Among the downregulated DEGs, a total of 143 DEGs were filtered into the DEGs PPI network complex including 243 nodes and 497 edges (Fig. 2C), which were mainly associated with ‘ECM organization’, ‘ECM structural constituents’ and the ‘extracellular region’ (Table VI).

Table V.

The enriched pathways of upregulated DEGs PPI network in PDAC.

Table V.

The enriched pathways of upregulated DEGs PPI network in PDAC.

A, Biological process

TermDescriptionCountP-value
GO.0007586Digestion13 1.12×10−8
GO.0065008Regulation of biological quality45 7.78×10−5
GO.0044281Small molecule metabolic process36 1.85×10−4
GO.0046903Secretion18 1.85×10−4
GO.0002576Platelet degranulation  7 6.08×10−3

B, Molecular function

Term DescriptionCountP-value

GO.0008236Serine-type peptidase activity13 2.61×10−8
GO.0004252Serine-type endopeptidase activity12 2.83×10−7
GO.0070011Peptidase activity, acting on L-amino acid peptides18 1.40×10−5
GO.0008233Peptidase activity18 1.77×10−5
GO.0004175Endopeptidase activity13 3.51×10−3

C, Cellular component

Term DescriptionCountP-value

GO.0005576Extracellular region76 2.57×10−15
GO.0031988Membrane-bounded vesicle62 3.76×10−12
GO.0044421Extracellular region part62 4.12×10−11
GO.0005615Extracellular space35 8.18×10−11
GO.0070062Extracellular exosome50 2.10×10−9

[i] DEGs, differentially expressed genes; PPI, protein-protein interactions; PDAC, pancreatic ductal adenocarcinoma.

Table VI.

The enriched pathways of downregulated DEGs PPI network in PDAC.

Table VI.

The enriched pathways of downregulated DEGs PPI network in PDAC.

A, Biological process

TermDescriptionCountP-value
GO.0030198Extracellular matrix organization35 4.14×10−24
GO.0022617Extracellular matrix disassembly20 1.55×10−17
GO.0030574Collagen catabolic process15 6.65×10−14
GO.0007155Cell adhesion32 4.45×10−9
GO.0001704Formation of primary germ layer13 5.25×10−9

B, Molecular function

Term DescriptionCountP-value

GO.0005201Extracellular matrix structural constituent10 5.72×10−7
GO.0005518Collagen binding  8 4.92×10−6
GO.0005539Glycosaminoglycan binding13 1.12×10−5
GO.0005515Protein binding66 1.16×10−4
GO.0004222 Metalloendopeptidase activity  9 1.94×10−4

C, Cellular component

Term DescriptionCountP-value

GO.0005576Extracellular region100 7.19×10−25
GO.0044421Extracellular region part  91 2.70×10−24
GO.0005615Extracellular space  44 1.93×10−14
GO.0070062Extracellular exosome  63 4.76×10−13
GO.0031012Extracellular matrix  24 6.44×10−13

[i] DEGs, differentially expressed genes; PPI, protein-protein interactions; PDAC, pancreatic ductal adenocarcinoma.

Discussion

The incidence of PDAC is increasing worldwide (14). The clinical signs and symptoms may be difficult to diagnose in the initial stages of the disease (7). Patients are often diagnosed at a late stage, when regional invasion or distant metastasis have occurred, resulting in a 5-year survival rate of ~5% (15,16). An insight into the molecular mechanisms of PDAC would allow for earlier diagnosis and more effective treatment. The rapid development of gene chips and high-throughput sequencing can rapidly and accurately provide gene expression data for thousands of genes in the human genome. Previous studies have identified some of the genes and signaling pathways that serve a role in the development of pancreatic cancer from chip analysis (17,18). In the present study, the chip data in the GSE28735 dataset was comprehensively analyzed and 424 common DEGs (159 upregulated and 265 downregulated) between PDAC and matching pairs of adjacent non-tumor tissue were identified using bioinformatics analysis.

GO analysis is an international standardized gene function classification system that provides the molecular function of genes involved in a variety of biological processes (19). In the current study, GO term analysis revealed that the upregulated genes were mainly involved in ‘digestion’, ‘lipid digestion’ and ‘proteolysis’, and downregulated DEGs were involved in ‘extracellular matrix organization’, ‘extracellular structure organization’ and ‘cell adhesion’. The pancreas mainly secretes trypsin and pancreatic lipase and abnormalities in secretions can interfere with protein and lipid metabolism, leading to chronic pancreatitis which is one of the important contributing factors for pancreatic cancer (20). The stability of cell structure and cell adhesion is also a major factor in the formation of pancreatic cancer (21).

Furthermore, KEGG pathway analysis indicated that the upregulated DEGs were involved in pancreatic secretion pathways and protein and lipid digestion and absorption pathways. Existing studies revealed that metabolic change is considered one of the characteristics of cancer, especially the dysfunction of pancreatic secretion (11,22). In pancreatic cancer, metabolic changes are prominent in protein and lipid digestion and absorption pathways (23). The downregulated DEGs were associated with ‘ECM-receptor interaction’, ‘focal adhesion’ and the ‘PI3K-Akt signaling’ pathways.

Previous studies indicated that pancreatic stellate cells, which can cause pancreatic fibrosis leading to pancreatic cancer, can produce and secrete ECM (24,25). One of the components of ECM, hyaluronic acid, can combine with CD44 antigen and influence vascular epithelial-mesenchymal transition (EMT) as well as cancer cell resistance to chemotherapy (4). Furthermore, the main constitutive protein of ECM, collagen I, can promote the adhesion of pancreatic cancer cells through the proliferation and migration of integrin α2β1 (24). Collagen, fibronectin and laminin are also associated with chemoresistance in pancreatic cancer cells in vitro (26). Previous studies revealed that focal adhesions interact with the ECM and can promote EMT, thereby promoting cell carcinogenesis (27). Furthermore, the PI3K-Akt signaling pathway is important in the etiology of pancreatic cancer (28). Therefore, these signaling pathways can promote the development of pancreatic cancer in a variety of ways, and may provide a new direction for the systematic treatment of pancreatic cancer.

In the current study, the top 10 degree hub genes identified in the PPI network were: CFTR, SLC7A2, CCL18, PDK4, BAIAP2L1, ITGA3, CPA1, GPRC5A, STYK1 and ST6GALNAC1. CFTR was the highest scoring gene. The CFTR gene codes for the cystic fibrosis transmembrane conductance regulator protein, an important member of the ATP binding cassette transporter family (29). It serves an important role in anion regulation and tissue homeostasis of various epithelial cells, activates the cAMP channel and promotes chloride and bicarbonate secretion in the digestive system (30,31). A previous study revealed that increased expression of CFTR in drug-resistant prostate cancer tissues or cells that block CFTR can inhibit tumor cell viability and autophagy via the PI3K/Akt signaling pathway (32). In CFTR knockout mice, mucosal barrier function was impaired, including tight junction disruption, which resulted in impaired tolerance to bacterial colonization and infection, abnormal innate and adaptive immune responses, and inflammation (33,34). It has been reported that CFTR is a negative regulator of the pro-inflammatory nuclear factor k-light-chain-enhancer of activated B cells-mediated innate immune response, including interleukin-8, and evokes a positive feedback loop of cyclooxygenase 2-prostaglandin E2 in inflammation, and therefore, these factors may work together to promote tumorigenesis (35,36). The pancreas is a digestive organ that secretes a variety of substances to regulate the digestive fluids through exocrine and endocrine methods (37). At the same time, the abovementioned 10 hub genes can also regulate the development and progression of pancreatic cancer by regulating immune and inflammatory processes, protein glycosylation and energy metabolism which affect multiple signaling pathways (3843). Therefore, these genes can be an important target for the precise treatment of pancreatic cancer.

For the upregulated DEGs, module analysis of the PPI network revealed that they were associated with pancreatic secretion signaling pathways and ‘protein digestion and absorption’ and ‘lipid digestion and absorption’ signaling pathways. Stimulation of the pancreas by secretagogues, including acetylcholine and cholecystokinin, results in intracellular Ca2+ signals, leading to the polarized secretion of enzymes (44). However, activation of the CFTR Cl- channel and the CFTR-dependent Cl-/HCO3- exchange is responsible for cAMP-induced HCO3- secretion (44). The secretory function of the pancreas is directly associated with both protein and lipid metabolism in the body, the disruption of which may lead to chronic inflammation of the pancreas, developing into pancreatic cancer (45).

The downregulated DEGs were associated with ECM-receptor interactions, focal adhesion and the PI3K-Akt signaling pathway (46). The ECM serves an important role in the morphogenesis of tissues and organs, and in the maintenance of cell and tissue structures and functions (47). These interactions lead to direct or indirect control of cell activity, including adhesion, migration, differentiation, proliferation, and apoptosis (48). Furthermore, the focal adhesion signaling pathway is the key signaling pathway of cell matrix adhesion, which serves an important role in cell movement, cell proliferation, cell differentiation, gene expression regulation and cell survival (49). The proliferation and metastasis of cancer cells depend on the regulation of this pathway (50,51). The PI3K-Akt signaling pathway serves as a bridge between extracellular signals and intracellular responses (52,53). Once activated, Akt phosphorylation can be involved in apoptosis, matrix control, important cellular processes, protein synthesis, metabolism and the cell cycle (54). The results obtained in the current study suggest that pancreatic secretory dysfunction, the imbalance of ECM-associated signaling pathways and the PI3K-Akt signaling pathway may result in cell cycle disruption and metabolism-associated microenvironmental changes, which can trigger the development of pancreatic cancer.

In conclusion, the current study investigated the biological pathways involved in PDAC by providing a comprehensive bioinformatics map of DEGs. These DEGs are involved in the development and progression of PDAC and provide a basis for the effective study of the molecular mechanisms of pancreatic cancer. Further molecular biological experiments and animal studies are required to confirm the functions and roles of these DEGs in PDAC.

Acknowledgements

Not applicable.

Funding

This study was supported by the Key Foundation of Sichuan Municipal Commission of Health and Family Planning Foundation of China (grant no. 17ZD008) and the Sichuan Medical Research Project Foundation of China (grant no. S16007).

Availability of data and materials

The datasets analyzed during the current study are available in the GSE28735 repository (www.ncbi.nlm.nih.gov/geo).

Authors' contributions

YH, YL and HW conceived of and designed the experiments. YH, YL, JG, CL and HZ performed the experiments. YH, YL and HW acquired, analyzed and interpreted the data and wrote the paper.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1 

Ryan DP, Hong TS and Bardeesy N: Pancreatic adenocarcinoma. N Engl J Med. 371:2140–2141. 2014. View Article : Google Scholar : PubMed/NCBI

2 

Quaresma M, Coleman MP and Rachet B: 40-year trends in an index of survival for all cancers combined and survival adjusted for age and sex for each cancer in England and Wales, 1971–2011: A population-based study. Lancet. 385:1206–1218. 2015. View Article : Google Scholar : PubMed/NCBI

3 

Siegel R, Naishadham D and Jemal A: Cancer statistics, 2013. CA Cancer J Clin. 63:11–30. 2013. View Article : Google Scholar : PubMed/NCBI

4 

Maron R, Schechter B, Mancini M, Mahlknecht G, Yarden Y and Sela M: Inhibition of pancreatic carcinoma by homo- and heterocombinations of antibodies against EGF-receptor and its kin HER2/ErbB-2. Proc Natl Acad Sci USA. 110:15389–15394. 2013. View Article : Google Scholar : PubMed/NCBI

5 

Chen YL, Hu CM, Hsu JT, Chang CC, Huang TY, Chiang PH, Chen WY, Chang YT, Chang MC, Tien YW, et al: Cellular 5-hydroxylmethylcytosine content determines tumorigenic potential and prognosis of pancreatic ductal adenocarcinoma. Am J Cancer Res. 8:2548–2563. 2018.PubMed/NCBI

6 

Capello M, Vykoukal JV, Katayama H, Bantis LE, Wang H, Kundnani DL, Aguilar-Bonavides C, Aguilar M, Tripathi SC, Dhillon DS, et al: Exosomes harbor B cell targets in pancreatic adenocarcinoma and exert decoy function against complement-mediated cytotoxicity. Nat Commun. 10:2542019. View Article : Google Scholar : PubMed/NCBI

7 

Chu LC, Goggins MG and Fishman EK: Diagnosis and detection of pancreatic cancer. Cancer J. 23:333–342. 2017. View Article : Google Scholar : PubMed/NCBI

8 

Ilic M and Ilic I: Epidemiology of pancreatic cancer. World J Gastroenterol. 22:9694–9705. 2016. View Article : Google Scholar : PubMed/NCBI

9 

Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA Jr and Kinzler KW: Cancer genome landscapes. Science. 339:1546–1558. 2013. View Article : Google Scholar : PubMed/NCBI

10 

Lin QJ, Yang F, Jin C and Fu DL: Current status and progress of pancreatic cancer in China. World J Gastroenterol. 21:7988–8003. 2015. View Article : Google Scholar : PubMed/NCBI

11 

Yan X, Wan H, Hao X, Lan T, Li W, Xu L, Yuan K and Wu H: Importance of gene expression signatures in pancreatic cancer prognosis and the establishment of a prediction model. Cancer Manag Res. 11:273–283. 2018. View Article : Google Scholar : PubMed/NCBI

12 

Zhang G, Schetter A, He P, Funamizu N, Gaedcke J, Ghadimi BM, Ried T, Hassan R, Yfantis HG, Lee DH, et al: DPEP1 inhibits tumor cell invasiveness, enhances chemosensitivity and predicts clinical outcome in pancreatic ductal adenocarcinoma. PLoS One. 7:e315072012. View Article : Google Scholar : PubMed/NCBI

13 

Zhou S, Liu P, Jiang W and Zhang H: Identification of potential target genes associated with the effect of propranolol on angiosarcoma via microarray analysis. Oncol Lett. 13:4267–4275. 2017. View Article : Google Scholar : PubMed/NCBI

14 

Huang H and Brekken RA: The next wave of stroma-targeting therapy in pancreatic cancer. Cancer Res. 79:328–330. 2019. View Article : Google Scholar : PubMed/NCBI

15 

Siegel RL, Miller KD and Jemal A: Cancer statistics, 2016. CA Cancer J Clin. 66:7–30. 2016. View Article : Google Scholar : PubMed/NCBI

16 

Heinemann V, Reni M, Ychou M, Richel DJ, Macarulla T and Ducreux M: Tumour-stroma interactions in pancreatic ductal adenocarcinoma: rationale and current evidence for new therapeutic strategies. Cancer Treat Rev. 40:118–128. 2014. View Article : Google Scholar : PubMed/NCBI

17 

Zhang G, He P, Tan H, Budhu A, Gaedcke J, Ghadimi BM, Ried T, Yfantis HG, Lee DH, Maitra A, et al: Integration of metabolomics and transcriptomics revealed a fatty acid network exerting growth inhibitory effects in human pancreatic cancer. Clin Cancer Res. 19:4983–4993. 2013. View Article : Google Scholar : PubMed/NCBI

18 

Zhang G, Schetter A, He P, Funamizu N, Gaedcke J, Ghadimi BM, Ried T, Hassan R, Yfantis HG, Lee DH, et al: DPEP1 inhibits tumor cell invasiveness, enhances chemosensitivity and predicts clinical outcome in pancreatic ductal adenocarcinoma. PLoS One. 7:e315072012. View Article : Google Scholar : PubMed/NCBI

19 

Ling YH, Ren CH, Guo XF, Xu LN, Huang YF, Luo JC, Zhang YH, Zhang XR and Zhang ZJ: Identification and characterization of microRNAs in the ovaries of multiple and uniparous goats (Capra hircus) during follicular phase. BMC Genomics. 15:3392014. View Article : Google Scholar : PubMed/NCBI

20 

Raimondi S, Lowenfels AB, Morselli-Labate AM, Maisonneuve P and Pezzilli R: Pancreatic cancer in chronic pancreatitis; aetiology, incidence, and early detection. Best Pract Res Clin Gastroenterol. 24:349–358. 2010. View Article : Google Scholar : PubMed/NCBI

21 

Grippo PJ and Munshi HG: Imaging the Pancreatic ECM. Pancreatic Cancer and Tumor Microenvironment. Trivandrum (India): Transworld Research Network. Chapter 2. 2012

22 

Hanahan D and Weinberg RA: Hallmarks of cancer: The next generation. Cell. 144:646–674. 2011. View Article : Google Scholar : PubMed/NCBI

23 

Le A, Rajeshkumar NV, Maitra A and Dang CV: Conceptual framework for cutting the pancreatic cancer fuel supply. Clin Cancer Res. 18:4285–4290. 2012. View Article : Google Scholar : PubMed/NCBI

24 

Apte MV, Pirola RC and Wilson JS: Pancreatic stellate cells: A starring role in normal and diseased pancreas. Front Physiol. 3:3442012. View Article : Google Scholar : PubMed/NCBI

25 

Koikawa K, Ohuchida K, Takesue S, Ando Y, Kibe S, Nakayama H, Endo S, Abe T, Okumura T, Horioka K, et al: Pancreatic stellate cells reorganize matrix components and lead pancreatic cancer invasion via the function of Endo180. Cancer Lett. 412:143–154. 2018. View Article : Google Scholar : PubMed/NCBI

26 

Grzesiak JJ, Ho JC, Moossa AR and Bouvet M: The integrin-extracellular matrix axis in pancreatic cancer. Pancreas. 35:293–301. 2007. View Article : Google Scholar : PubMed/NCBI

27 

Burridge K: Focal Adhesions: A personal perspective on a half century of progress. FEBS J. 284:3355–3361. 2017. View Article : Google Scholar : PubMed/NCBI

28 

Ebrahimi S, Hosseini M, Shahidsales S, Maftouh M, Ferns GA, Ghayour-Mobarhan M, Hassanian SM and Avan A: Targeting the Akt/PI3K signaling pathway as a potential therapeutic strategy for the treatment of pancreatic cancer. Curr Med Chem. 24:1321–1331. 2017. View Article : Google Scholar : PubMed/NCBI

29 

Zou WB, Tang XY, Zhou DZ, Qian YY, Hu LH, Yu FF, Yu D, Wu H, Deng SJ, Lin JH, et al: SPINK1, PRSS1, CTRC, and CFTR genotypes influence disease onset and clinical outcomes in chronic pancreatitis. Clin Transl Gastroenterol. 9:2042018. View Article : Google Scholar : PubMed/NCBI

30 

Collins FS: Cystic fibrosis: Molecular biology and therapeutic implications. Science. 256:774–779. 1992. View Article : Google Scholar : PubMed/NCBI

31 

Anderson MP, Gregory RJ, Thompson S, Souza DW, Paul S, Mulligan RC, Smith AE and Welsh MJ: Demonstration that CFTR is a chloride channel by alteration of its anion selectivity. Science. 253:202–205. 1991. View Article : Google Scholar : PubMed/NCBI

32 

Zhu Q, Li H, Liu Y and Jiang L: Knockdown of CFTR enhances sensitivity of prostate cancer cells to cisplatin via inhibition of autophagy. Neoplasma. 64:709–717. 2017. View Article : Google Scholar : PubMed/NCBI

33 

De Lisle RC: Disrupted tight junctions in the small intestine of cystic fibrosis mice. Cell Tissue Res. 355:131–142. 2014. View Article : Google Scholar : PubMed/NCBI

34 

Munck A: Cystic fibrosis: Evidence for gut inflammation. Int J Biochem Cell Biol. 52:180–183. 2014. View Article : Google Scholar : PubMed/NCBI

35 

Vij N, Mazur S and Zeitlin PL: CFTR is a negative regulator of NFkappaB mediated innate immune response. PLoS One. 4:e46642009. View Article : Google Scholar : PubMed/NCBI

36 

Chen J, Jiang XH, Chen H, Guo JH, Tsang LL, Yu MK, Xu WM and Chan HC: CFTR negatively regulates cyclooxygenase-2-PGE(2) positive feedback loop in inflammation. J Cell Physiol. 227:2759–2766. 2012. View Article : Google Scholar : PubMed/NCBI

37 

Brereton MF, Iberl M, Shimomura K, Zhang Q, Adriaenssens AE, Proks P, Spiliotis II, Dace W, Mattis KK, Ramracheya R, et al: Reversible changes in pancreatic islet structure and function produced by elevated blood glucose. Nat Commun. 5:46392014. View Article : Google Scholar : PubMed/NCBI

38 

Sun P, Zhu X, Shrubsole MJ, Ness RM, Hibler EA, Cai Q, Long J, Chen Z, Li G, Hou L, et al: Genetic variation in SLC7A2 interacts with calcium and magnesium intakes in modulating the risk of colorectal polyps. J Nutr Biochem. 47:35–40. 2017. View Article : Google Scholar : PubMed/NCBI

39 

Chen J, Yao Y, Gong C, Yu F, Su S, Chen J, Liu B, Deng H, Wang F, Lin L, et al: CCL18 from tumor-associated macrophages promotes breast cancer metastasis via PITPNM3. Cancer Cell. 19:541–555. 2011. View Article : Google Scholar : PubMed/NCBI

40 

Bonnet S, Archer SL, Allalunis-Turner J, Haromy A, Beaulieu C, Thompson R, Lee CT, Lopaschuk GD, Puttagunta L, Bonnet S, et al: A mitochondria-K+ channel axis is suppressed in cancer and its normalization promotes apoptosis and inhibits cancer growth. Cancer Cell. 11:37–51. 2007. View Article : Google Scholar : PubMed/NCBI

41 

Wang YP, Huang LY, Sun WM, Zhang ZZ, Fang JZ, Wei BF, Wu BH and Han ZG: Insulin receptor tyrosine kinase substrate activates EGFR/ERK signalling pathway and promotes cell proliferation of hepatocellular carcinoma. Cancer Lett. 337:96–106. 2013. View Article : Google Scholar : PubMed/NCBI

42 

Yang L, Ma T and Zhang J: GPRC5A exerts its tumor-suppressive effects in breast cancer cells by inhibiting EGFR and its downstream pathway. Oncol Rep. 36:2983–2990. 2016. View Article : Google Scholar : PubMed/NCBI

43 

Wang J, Farris AB, Xu K, Wang P, Zhang X, Duong DM, Yi H, Shu HK, Sun SY and Wang Y: GPRC5A suppresses protein synthesis at the endoplasmic reticulum to prevent radiation-induced lung tumorigenesis. Nat Commun. 7:117952016. View Article : Google Scholar : PubMed/NCBI

44 

Chey WY and Chang T: Neural hormonal regulation of exocrine pancreatic secretion. Pancreatology. 1:320–335. 2001. View Article : Google Scholar : PubMed/NCBI

45 

Vaziri-Gohar A, Zarei M, Brody JR and Winter JM: Metabolic dependencies in pancreatic cancer. Front Oncol. 8:6172018. View Article : Google Scholar : PubMed/NCBI

46 

Rijkers AP, Bakker OJ, Ahmed Ali U, Hagenaars JCJP, van Santvoort HC, Besselink MG, Bollen TL and van Eijck CH;: Dutch Pancreatitis Study Group: Risk of pancreatic cancer after a primary episode of acute pancreatitis. Pancreas. 46:1018–1022. 2017. View Article : Google Scholar : PubMed/NCBI

47 

Theocharis AD, Skandalis SS, Gialeli C and Karamanos NK: Extracellular matrix structure. Adv Drug Deliv Rev. 97:4–27. 2016. View Article : Google Scholar : PubMed/NCBI

48 

Canobbio I, Balduini C and Torti M: Signalling through the platelet glycoprotein Ib-V-IX complex. Cell Signal. 16:1329–1344. 2004. View Article : Google Scholar : PubMed/NCBI

49 

Eke I and Cordes N: Focal adhesion signaling and therapy resistance in cancer. Semin Cancer Biol. 31:65–75. 2015. View Article : Google Scholar : PubMed/NCBI

50 

Guo W and Giancotti FG: Integrin signalling during tumour progression. Nat Rev Mol Cell Biol. 5:816–826. 2004. View Article : Google Scholar : PubMed/NCBI

51 

Lee JW and Juliano R: Mitogenic signal transduction by integrin- and growth factor receptor-mediated pathways. Mol Cells. 17:188–202. 2004.PubMed/NCBI

52 

Engelman JA, Luo J and Cantley LC: The evolution of phosphatidylinositol 3-kinases as regulators of growth and metabolism. Nat Rev Genet. 7:606–619. 2006. View Article : Google Scholar : PubMed/NCBI

53 

Song G, Ouyang G and Bao S: The activation of Akt/PKB signaling pathway and cell survival. J Cell Mol Med. 9:59–71. 2005. View Article : Google Scholar : PubMed/NCBI

54 

Hers I, Vincent EE and Tavaré JM: Akt signalling in health and disease. Cell Signal. 23:1515–1527. 2011. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

April-2019
Volume 17 Issue 4

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
He Y, Liu Y, Gong J, Liu C, Zhang H and Wu H: Identification of key pathways and candidate genes in pancreatic ductal adenocarcinoma using bioinformatics analysis. Oncol Lett 17: 3751-3764, 2019.
APA
He, Y., Liu, Y., Gong, J., Liu, C., Zhang, H., & Wu, H. (2019). Identification of key pathways and candidate genes in pancreatic ductal adenocarcinoma using bioinformatics analysis. Oncology Letters, 17, 3751-3764. https://doi.org/10.3892/ol.2019.10041
MLA
He, Y., Liu, Y., Gong, J., Liu, C., Zhang, H., Wu, H."Identification of key pathways and candidate genes in pancreatic ductal adenocarcinoma using bioinformatics analysis". Oncology Letters 17.4 (2019): 3751-3764.
Chicago
He, Y., Liu, Y., Gong, J., Liu, C., Zhang, H., Wu, H."Identification of key pathways and candidate genes in pancreatic ductal adenocarcinoma using bioinformatics analysis". Oncology Letters 17, no. 4 (2019): 3751-3764. https://doi.org/10.3892/ol.2019.10041