Open Access

Identification of key genes and associated pathways in KIT/PDGFRA wild‑type gastrointestinal stromal tumors through bioinformatics analysis

  • Authors:
    • Wen‑Jie Wang
    • Hong‑Tao Li
    • Jian‑Ping Yu
    • Yu‑Min Li
    • Xiao‑Peng Han
    • Peng Chen
    • Wen‑Wen Yu
    • Wei‑Kai Chen
    • Zuo‑Yi Jiao
    • Hong‑Bin Liu
  • View Affiliations

  • Published online on: September 5, 2018     https://doi.org/10.3892/mmr.2018.9457
  • Pages: 4499-4515
  • Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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


Abstract

Gastrointestinal stromal tumors (GISTs) are the most common type of mesenchymal tumor in the gastrointestinal tract. The present study aimed to identify the potential candidate biomarkers that may be involved in the pathogenesis and progression of v‑kit Hardy‑Zuckerman 4 feline sarcoma viral oncogene homolog (KIT)/platelet‑derived growth factor receptor α (PDGFRA) wild‑type GISTs. A joint bioinformatics analysis was performed to identify the differentially expressed genes (DEGs) in wild‑type GIST samples compared with KIT/PDGFRA mutant GIST samples. Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs was conducted using Database for Annotation, Visualization and Integrated Discovery and KEGG Orthology‑Based Annotation System (KOBAS) online tools, respectively. Protein‑protein interaction (PPI) networks of the DEGs were constructed using Search Tool for the Retrieval of Interacting Genes online tool and Cytoscape, and divided into sub‑networks using the Molecular Complex Detection (MCODE) plug‑in. Furthermore, enrichment analysis of DEGs in the modules was analyzed with KOBAS. In total, 546 DEGs were identified, including 238 upregulated genes primarily enriched in ‘cell adhesion’, ‘biological adhesion’, ‘cell‑cell signaling’, ‘PI3K‑Akt signaling pathway’ and ‘ECM‑receptor interaction’, while the 308 downregulated genes were predominantly involved in ‘inflammatory response’, ‘sterol metabolic process’ and ‘fatty acid metabolic process’, ‘small GTPase mediated signal transduction’, ‘cAMP signaling pathway’ and ‘proteoglycans in cancer’. A total of 25 hub genes were obtained and four modules were mined from the PPI network, and sub‑networks also revealed these genes were primarily involved in significant pathways, including ‘PI3K‑Akt signaling pathway’, ‘proteoglycans in cancer’, ‘pathways in cancer’, ‘Rap1 signaling pathway’, ‘ECM‑receptor interaction’, ‘phospholipase D signaling pathway’, ‘ras signaling pathway’ and ‘cGMP‑PKG signaling pathway’. These results suggested that several key hub DEGs may serve as potential candidate biomarkers for wild‑type GISTs, including phosphatidylinositol‑4,5‑bisphosphate 3‑kinase, catalytic subunit γ, insulin like growth factor 1 receptor, hepatocyte growth factor, thrombospondin 1, Erb‑B2 receptor tyrosine kinase 2 and matrix metallopeptidase 2. However, further experiments are required to confirm these results.

Introduction

Gastrointestinal stromal tumors (GISTs) are the most common type of mesenchymal tumor in the gastrointestinal tract, which account for 20% of all soft-tissue sarcomas (1,2). GISTs originate from the interstitial cells of Cajal, and may occur in any part of the gastrointestinal tract; the most frequent sites of origin are the stomach (50–60%), followed by the small intestine (30–35%), the colon and rectum (5%), and finally the esophagus (<1%) (35). The number of newly diagnosed GISTs is increasing yearly, since the identification of v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog (KIT) and platelet-derived growth factor receptor α (PDGFRA) proteins as reliable biomarkers of these tumors (6,7).

Approximately 75–80% of GISTs have KIT gene mutations in exons 9, 11, 13, 14 and 17. Of the remaining GISTs with no detected KIT mutations, ~1/3 have mutations in the PDGFRA gene, in exons 12, 14 and 18 (8). While the majority of GISTs are characterized by KIT/PDGFRA gene mutations, 10–15% of GISTs lack such mutations and are defined as KIT/PDGFRA wild-type GISTs (9,10). KIT-also known as CD117 or C-kit receptor-encodes the c-KIT type III receptor tyrosine kinase, which is a cytokine receptor located on the surface of hematopoietic stem cells, as well as other cell types (11). PDGFRA-also known as PDGFRα - is also a type III receptor tyrosine kinase that is expressed on the surfaces of a wide range of cell types (12). Mutations in the PDGFRA gene may induce activation of constitutive ligand-independent kinases and are mutually exclusive with KIT gene mutations, i.e., KIT and PDGFRA mutations do not coexist in patients with GISTs (8). Imatinib, a small molecule selective tyrosine kinase inhibitor, has been used to treat KIT/PDGFRA mutated GISTs (1); however, the efficacy of imatinib depends on the mutated domains of KIT/PDGFRA. It has been reported that ~10% of patients with GISTs are resistant to imatinib, and 40–50% of imatinib-sensitive patients will develop secondary resistance in 2 years (13). In addition, wild-type GISTs are resistant to imatinib treatment and the genetic alterations in wild-type GISTs remain unclear (8). Therefore, it is necessary to identify new target molecules that may be involved in the development and progression of wild-type GISTs.

Currently, gene profiling is widely used in the field of cancer genetics research, which is particularly suitable for the differentially expressed gene (DEG) screening. A large amount of gene profile data has been generated, and most of the data has been shared in public databases. Reintegrating these public data may provide valuable clues for further research. Although many gene profile studies have been performed on GISTs in recent years, research regarding wild-type GISTs is limited and the results are not consistent. Therefore, a joint bioinformatics analysis will be innovative and may provide valuable clues for further research.

In the present study, a joint bioinformatics analysis of two gene expression profiles was performed, in order to identify potential genetic changes in wild-type GIST samples compared to KIT/PDGFRA-mutant GIST samples. Subsequently, functional and pathway enrichment analyses were performed on the DEGs to identify potential biological functions and signaling pathways. Furthermore, a protein-protein interaction (PPI) network was constructed to identify key hub genes. The aim of the current study was to investigate the underlying biological functions and pathways involved in the development and progression of GISTs, and to identify potential candidate biomarkers for these tumors.

Materials and methods

Microarray data

The raw gene expression profiles (GSE17743 and GSE20708) were downloaded from the public Gene Expression Omnibus database (www.ncbi.nlm.nih.gov/geo), which were based on the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array; Thermo Fisher Scientific, Inc., Waltham, MA, USA), and were submitted by Ostrowski et al (14) and Astolfi et al (15), respectively. The GSE17743 dataset contained 29 GIST samples, including 15 with KIT mutations detected, 11 with PDGFRA mutations detected, and three with no mutations detected. The GSE20708 dataset included 22 GIST tumor samples, including 13 with KIT mutations detected, five with PDGFRA mutations detected, and four with no mutations detected. Thus, a total of 51 GIST tumor samples were used for further analysis in the present study.

Data processing

Samples (n=51) were divided into two groups, including wild-type GIST groups (n=7) and KIT/PDGFRA mutant GIST groups (n=44). The CEL files were first converted into probe expression values and were preprocessed for background adjustment and quantile normalization by robust multiarray average algorithm using the ‘affy’ package in R (version 3.4.2) (16,17). The ‘sva’ package in R was used to remove batch effects between two gene expression profiles (18). The ‘Hclust’ method of R was used to perform cluster analysis for gene expression alterations at two batch levels (19). Following this, the probe-level data were transformed to the expression values of genes according to the latest version of annotation file (HG-U133_Plus_2; release 35) for Affymetrix Human Genome U133 Plus 2.0 Array, which was obtained from the official website (www.affymetrix.com/support/technical/byproduct.affx?product=hg-u133-plus). If one gene symbol was matched by multiple probes, then the average expression value was calculated for this gene.

Identification of DEGs

The ‘limma’ package (version 3.26.9) in R language was used to identify DEGs between two groups (20). Fold change (FC) of the gene expression was also observed and log2 FC was calculated. The threshold was defined as a |log2 FC| of >1 and an adjusted P-value of <0.05. Hierarchical clustering analysis was subsequently performed using the ‘pheatmap’ package in R (21).

Functional and pathway enrichment analysis

Gene Ontology (GO) enrichment analysis and functional annotation of DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) network software version 6.8 (https://david.ncifcrf.gov/) (22), and enriched GO terms were visualized using the BiNGO plug-in of Cytoscape software (version 3.5.1) (23). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and functional annotation were processed by KEGG Orthology-Based Annotation System (KOBAS) network software version 3.0 (kobas.cbi.pku.edu.cn/download.php) (24). An adjusted P-value <0.05 was set as the cut-off criterion.

PPI network construction and modules selection

The PPI networks of DEGs were identified using the Search Tool for the Retrieval of Interacting Genes (STRING) database (string-db.org; release 10) (25). Interactions with confidence scores of ≥0.4 were selected as significant and visualized using Cytoscape software (www.cytoscape.org) (26). The hub genes were selected by the cytoHubba plug-in, with ≥10 degrees for each gene (27), and also mapped into ClueGO to visualize functionally grouped GO terms and KEGG pathway annotation networks (28). The Molecular Complex Detection (MCODE) plug-in was applied to screen modules of the PPI network with degree cutoff=2, node score cutoff=0.2, k-core=2, and max. depth=100 (29). Subsequently, the functional and pathway enrichment analysis of genes in each module (MCODE score ≥6 and number of nodes ≥6) was performed by KOBAS.

Results

Identification of DEGs

As presented in Fig. 1, the batch effects between two gene expression profiles datasets were removed. The data was normalized prior to further analysis (Fig. 2A and B). In total, 546 DEGs (238 upregulated DEGs and 308 downregulated DEGs) were identified in wild-type GIST samples, compared with KIT/PDGFRA mutant GIST samples, based on the cut-off criteria. The volcano plot (Fig. 2C) and heatmap (Fig. 3) demonstrated the distribution and cluster of DEGs, respectively.

Functional and pathway enrichment analysis

The directed acyclic graph of GO enrichment analysis and functional annotation of DEGs is depicted in Fig. 4. For biological processes (BP), the upregulated DEGs were primarily enriched in ‘cell adhesion’, ‘biological adhesion’ and ‘synapse organization’ (Table I; Fig. 5A and B); the downregulated DEGs were primarily enriched in ‘sterol metabolic process’, ‘inflammatory response’, and ‘integrin-mediated signaling pathway’ (Table I; Fig. 5C and D). For cellular component (CC), the upregulated DEGs were primarily enriched in ‘plasma membrane part’, ‘synapse’ and ‘integral to plasma membrane’ (Table I; Fig. 5A and B); the downregulated DEGs were primarily enriched in ‘internal side of plasma membrane’, ‘lipid particle’, and ‘plasma membrane part’ (Table I; Fig. 5C and D). For molecular function (MF), the upregulated DEGs were primarily enriched in ‘transmembrane receptor protein tyrosine kinase activity’, ‘calcium ion binding’ and ‘transmembrane receptor protein tyrosine kinase signaling pathway’ (Table I; Fig. 5A and B); the downregulated DEGs were primarily enriched in ‘GTPase regulator activity’, ‘nucleoside-triphosphatase regulator activity’, and ‘calcium ion binding’ (Table I; Fig. 5C and D).

Table I.

GO enrichment analysis and functional annotation of DEGs.

Table I.

GO enrichment analysis and functional annotation of DEGs.

A, Upregulated

CategoryTermDescriptionCount%P-value
GOTERM_BP_FATGO:0007155Cell adhesion2511.1 7.08×10−6
GOTERM_BP_FATGO:0022610Biological adhesion2511.1 7.26×10−6
GOTERM_BP_FATGO:0050808Synapse organization73.1 1.13×10−4
GOTERM_BP_FATGO:0007169Transmembrane receptor protein tyrosine kinase signaling pathway125.3 1.24×10−4
GOTERM_BP_FATGO:0007267Cell-cell signaling198.4 5.50×10−4
GOTERM_CC_FATGO:0044459Plasma membrane part5524.4 7.52×10−6
GOTERM_CC_FATGO:0045202Synapse188.0 8.07×10−6
GOTERM_CC_FATGO:0005887Integral to plasma membrane3515.6 2.90×10−5
GOTERM_CC_FATGO:0030054Cell junction219.3 3.00×10−5
GOTERM_CC_FATGO:0031226Intrinsic to plasma membrane3515.6 4.59×10−5
GOTERM_MF_FATGO:0004714Transmembrane receptor protein tyrosine kinase activity62. 7 1.36×10−3
GOTERM_MF_FATGO:0005509Calcium ion binding229.8 4.10×10−3
GOTERM_MF_FATGO:0022843Voltage-gated cation channel activity73.1 9.44×10−3
GOTERM_MF_FATGO:0022836Gated channel activity104.4 1.41×10−2
GOTERM_MF_FATGO:0008083Growth factor activity73.1 1.43×10−2

B, Downregulated

GOTERM_BP_FAT GO:0016125Sterol metabolic process62.2 1.22×10−2

GOTERM_BP_FATGO:0006954Inflammatory response114.1 1.34×10−2
GOTERM_BP_FATGO:0007229Integrin-mediated signaling pathway51.9 1.51×10−2
GOTERM_BP_FATGO:0006631Fatty acid metabolic process83.0 1.87×10−2
GOTERM_BP_FATGO:0007264Small GTPase mediated signal transduction103.7 2.38×10−2
GOTERM_CC_FATGO:0009898Internal side of plasma membrane124.5 4.55×10−2
GOTERM_CC_FATGO:0005811Lipid particle31.1 1.78×10−2
GOTERM_CC_FATGO:0044459Plasma membrane part4215.7 2.57×10−2
GOTERM_CC_FATGO:0005739Mitochondrion249.0 2.68×10−2
GOTERM_CC_FATGO:0031090Organelle membrane249.0 2.91×10−2
GOTERM_MF_FATGO:0030695GTPase regulator activity145.2 3.60×10−3
GOTERM_MF_FATGO:0060589 Nucleoside-triphosphatase regulator activity145.2 4.34×10−3
GOTERM_MF_FATGO:0005509Calcium ion binding238.6 6.95×10−3
GOTERM_MF_FATGO:0005178Integrin binding51.9 8.49×10−3
GOTERM_MF_FATGO:0005096GTPase activator activity83.0 3.12×10−2

[i] Count indicates the enriched gene number in the category. GO, gene ontology; DEGs, differentially expressed genes BP, biological process; CC, cellular component; MF, molecular function.

According to the KEGG pathway enrichment analysis, upregulated genes were primarily enriched in ‘PI3K-Akt signaling pathway’, ‘ras signaling pathway’, ‘Rap1 signaling pathway’, ‘calcium signaling pathway’ and ‘ErbB signaling pathway’ (Table II; Fig. 5E). Downregulated genes were primarily enriched in ‘insulin signaling pathway’, ‘cAMP signaling pathway’, ‘PPAR signaling pathway’, and ‘NF-kappa B signaling pathway’ (Table II; Fig. 5F).

Table II.

KEGG pathways analysis results of DEGs.

Table II.

KEGG pathways analysis results of DEGs.

A, Upregulated

Pathway IDDescriptionCountP-valueGenes
hsa05218Melanoma7 4.22×10−7CDK6, HGF, FGF3, FGF10, IGF1R, FGF4, PDGFC
hsa04151PI3K-Akt signaling pathway12 1.53×10−6CDK6, HGF, FGF3, MYB, EFNA2, FGF10, IGF1R, FGF4, ITGA8, PPP2R2B, PDGFC, THBS4
hsa04514Cell adhesion molecules (CAMs)8 4.06×10−6PVRL3, NRXN1, CDH2, NRCAM, CNTNAP2, ITGA8, NLGN4X, CADM1
hsa04014Ras signaling pathway9 1.30×10−5HGF, SHC3, FGF3, EFNA2, FGF10, IGF1R, HTR7, FGF4, PDGFC
hsa01521EGFR tyrosine kinase inhibitor resistance6 1.34×10−5HGF, SHC3, ERBB3, ERBB2, IGF1R, PDGFC
hsa04512ECM-receptor interaction6 1.43×10−5CD44, SV2B, CD36, ITGA8, THBS4, SV2C
hsa04510Focal adhesion8 3.98×10−5ARHGAP5, HGF, SHC3, ERBB2, IGF1R, ITGA8, PDGFC, THBS4
hsa04015Rap1 signaling pathway8 5.17×10−5HGF, FGF3, EFNA2, FGF10, IGF1R, FGF4, ADCY2, PDGFC
hsa04020Calcium signaling pathway7 1.31×10−4ATP2B1, ERBB3, ERBB2, HTR7, TACR1, CAMK4, ADCY2
hsa05200Pathways in cancer10 1.70×10−4CDK6, HGF, DAPK1, ERBB2, FGF3, FGF10, IGF1R, FZD2, FGF4, ADCY2
hsa05014Amyotrophic lateral sclerosis (ALS)4 3.18×10−4GRIA2, MAP3K5, GRIA1, NEFL
hsa04080Neuroactive ligand-receptor interaction8 3.22×10−4GRIA2, PRLHR, PTH1R, HTR7, TACR1, GRIA1, OPRK1, GLRB
hsa04730Long-term depression4 5.69×10−4GRIA2, IGF1R, GRIA1, PPP1R17
hsa05205Proteoglycans in cancer6 1.66×10−3HGF, CD44, ERBB3, ERBB2, IGF1R, FZD2
hsa04012ErbB signaling pathway4 2.20×10−3SHC3, ERBB3, ERBB2, NRG3

B, Downregulated

Pathway ID DescriptionCountP-valueGenes

hsa05145Toxoplasmosis7 5.50×10−5PIK3CG, SOCS1, HLA-DPA1, MYD88, CD40, LDLR, MAPK10
hsa04925Aldosterone synthesis and secretion6 5.68×10−5KCNK3, PDE2A, CREB3L1, LDLR, ORAI1, CACNA1H
hsa05144Malaria5 5.85×10−5VCAM1, THBS1, GYPC, MYD88, CD40
hsa04810Regulation of actin cytoskeleton9 6.24×10−5ITGA4, PIK3CG, CD14, F2R, FGFR4, MRAS, SSH3, VAV2, ITGAE
hsa04910Insulin signaling pathway7 1.39×10−4PIK3CG, SOCS2, SOCS1, SREBF1, PYGM, PDE3B, MAPK10
hsa04024cAMP signaling pathway8 2.08×10−4HHIP, PIK3CG, F2R, CREB3L1, PDE3B, ORAI1, MAPK10, VAV2
hsa05205Proteoglycans in cancer8 2.52×10−4MMP2, CTSL, PIK3CG, THBS1, WNT5B, WNT9A, MRAS, VAV2
hsa04931Insulin resistance6 2.65×10−4PIK3CG, CREB3L1, SREBF1, PYGM, CPT1A, MAPK10
hsa03320PPAR signaling pathway5 3.17×10−4ME1, PLIN2, CPT1A, ACSL5, CYP27A1
hsa00071Fatty acid degradation4 4.98×10−4ACAA2, CPT1A, ACSL5, ALDH7A1
hsa01212Fatty acid metabolism4 6.77×10−4PTPLAD2, ACAA2, CPT1A, ACSL5
hsa04930Type II diabetes mellitus4 6.77×10−4PIK3CG, SOCS2, SOCS1, MAPK10
hsa04064NF-kappa B signaling pathway5 9.57×10−4VCAM1, CD14, SYK, MYD88, CD40
hsa05166HTLV-I infection8 1.11×10−3JAK3, PIK3CG, VCAM1, WNT5B, HLA-DPA1, WNT9A, MRAS, CD40
hsa04510Focal adhesion7 1.22×10−3ITGA4, PIK3CG, THBS1, PARVA, MAPK10, VAV2, PARVB

[i] Count indicates the enriched gene number in the pathway. KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.

PPI network construction and modules selection

To investigate the interactions between DEGs, a PPI network for the DEGs was constructed. The PPI network consisted of 338 nodes and 628 edges with a confidence score of ≥0.4 (Fig. 6). A total of 25 hub genes were selected from the PPI network with a degree of ≥10 (Table III), including leucine-rich repeat kinase 2 (LRRK2), phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit γ (PIK3CG), CD44, vascular cell adhesion molecule 1 (VCAM1) and hepatocyte growth factor (HGF). GO enrichment analysis revealed that hub genes were primarily enriched in ‘plasma membrane region’, ‘extracellular exosome’, ‘angiogenesis’, ‘regulation of transmembrane transport’ and ‘protein binding’ (Fig. 7A; Table IV). The KEGG analysis indicated that hub genes were predominantly enriched in ‘PI3K-Akt signaling pathway’, ‘proteoglycans in cancer’, ‘focal adhesion’, ‘Rap1 signaling pathway’, ‘pathways in cancer’ and ‘ECM-receptor interaction’ (Fig. 7B; Table IV).

Table III.

Top 25 hub genes identified in PPI network of DEGs.

Table III.

Top 25 hub genes identified in PPI network of DEGs.

Affy IDGene symbolGene nameDegreelog FCP-valueRegulation
229584_atLRRK2Leucine rich repeat kinase 236−1.78 1.04×10−4Down
206369_s_atPIK3CG Phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit gamma29−1.89 5.37×10−6Down
204489_s_atCD44CD44 molecule (Indian blood group)281.11 1.14×10−3Up
203868_s_atVCAM1Vascular cell adhesion molecule 120−1.76 8.36×10−4Down
209960_atHGFHepatocyte growth factor191.05 2.25×10−3Up
216836_s_atERBB2Erb-b2 receptor tyrosine kinase 2181.21 6.69×10−5Up
243358_atIGF1RInsulin-like growth factor 1 receptor181.39 1.72×10−11Up
203441_s_atCDH2Cadherin 2151.41 2.63×10−8Up
206488_s_atCD36CD36 molecule151.49 2.97×10−3Up
205153_s_atCD40CD40 molecule15−1.12 5.87×10−4Down
207540_s_atSYKSpleen associated tyrosine kinase14−1.48 2.52×10−3Down
205428_s_atCALB2Calbindin 2131.25 3.93×10−6Up
210001_s_atSOCS1Suppressor of cytokine signaling 113−1.46 4.87×10−5Down
202291_s_atMGPMatrix Gla protein121.52 5.01×10−6Up
213217_atADCY2Adenylate cyclase 2121.96 7.75×10−4Up
201069_atMMP2Matrix metallopeptidase 212−2.29 2.25×10−4Down
205884_atITGA4Integrin subunit alpha 412−1.94 6.88×10−4Down
201108_s_atTHBS1Thrombospondin 112−1.65 2.30×10−3Down
204579_atFGFR4Fibroblast growth factor receptor 412−1.49 2.41×10−3Down
219321_atMPP5Membrane protein, palmitoylated 5111.07 6.57×10−5Up
209124_atMYD88Myeloid differentiation primary response 8811−1.15 9.07×10−6Down
217173_s_atLDLRLow density lipoprotein receptor11−1.06 8.12×10−5Down
227486_atNT5E5′-nucleotidase ecto10−1.64 1.19×10−4Down
205751_atSH3GL2SH3-domain GRB2-like 2101.48 1.32×10−3Up
208951_atALDH7A1Aldehyde dehydrogenase 7 family, member A110−1.21 5.77×10−5Down

[i] PPI, protein-protein interaction; DEGs, differentially expressed genes; FC, fold change.

Table IV.

Enriched function and pathways of hub genes and selected modules of PPI network.

Table IV.

Enriched function and pathways of hub genes and selected modules of PPI network.

A, Hub genes

TermDescriptionCountP-valueHub genes
GO:0005886Plasma membrane19 7.42×10−8PIK3CG, FGFR4, ADCY2, LDLR, ERBB2, MPP5, CD40, ITGA4, CDH2, MMP2, VCAM1, IGF1R, MYD88, CD36, CD44, LRRK2, NT5E, SH3GL2, SYK
GO:0016020Membrane11 2.53×10−4PIK3CG, VCAM1, IGF1R, ADCY2, CD36, LDLR, ERBB2, ITGA4, HGF, CDH2, NT5E
GO:0070062Extracellular exosome12 4.07×10−4VCAM1, ALDH7A1, CD44, MPP5, MGP, ITGA4, CD40, CDH2, THBS1, LRRK2, NT5E, SH3GL2
GO:0005737Cytoplasm16 5.71×10−4PIK3CG, FGFR4, ADCY2, ERBB2, SOCS1, MPP5, CD40, CDH2, CALB2, ALDH7A1, MYD88, CD44, LRRK2, NT5E, SH3GL2, SYK
GO:0005515Protein binding21 1.47×10−3PIK3CG, FGFR4, LDLR, ERBB2, SOCS1, MPP5, MGP, HGF, CD40, ITGA4, CDH2, MMP2, IGF1R, ALDH7A1, MYD88, CD36, CD44, THBS1, LRRK2, SH3GL2, SYK
hsa05205Proteoglycans in cancer7 4.89×10−11PIK3CG, IGF1R, CD44, ERBB2, HGF, THBS1, MMP2
hsa04151PI3K-Akt signaling pathway7 1.58×10−9PIK3CG, IGF1R, FGFR4, ITGA4, HGF, THBS1, SYK
hsa04510Focal adhesion6 3.19×10−9PIK3CG, IGF1R, ERBB2, ITGA4, HGF, THBS1
hsa04015Rap1 signaling pathway6 3.99×10−9PIK3CG, IGF1R, FGFR4, ADCY2, HGF, THBS1
hsa05200Pathways in cancer6 1.57×10−7PIK3CG, IGF1R, ADCY2, ERBB2, HGF, MMP2

B, Module 1

Term DescriptionCountP-valueGenes

hsa04151PI3K-Akt signaling pathway4 6.16×10−5IGF1R, FGFR4, JAK3, SYK
hsa04550Signaling pathways regulating pluripotency of stem cells3 1.03×10−4IGF1R, FGFR4, JAK3
hsa05203Viral carcinogenesis2 7.47×10−3FGFR4, SYK
hsa04015Rap1 signaling pathway2 7.89×10−3IGF1R, JAK3
hsa04014Ras signaling pathway2 9.15×10−3IGF1R, JAK3

C, Module 2

Term DescriptionCountP-valueGenes

hsa04668TNF signaling pathway2 5.09×10−4PIK3CG, MAP3K5
hsa04071Sphingolipid signaling pathway2 6.14×10−4PIK3CG, MAP3K5
hsa04210Apoptosis2 8.16×10−4PIK3CG, MAP3K5
hsa04072Phospholipase D signaling pathway2 8.62×10−4MRAS, PIK3CG
hsa05205Proteoglycans in cancer2 1.72×10−3MRAS, PIK3CG

D, Module 3

Term DescriptionCountP-valueGenes

hsa05200Pathways in cancer7 5.55×10−8ADCY2, ERBB2, FGF10, FZD2, MMP2, FGF3, FGF4
hsa05205Proteoglycans in cancer5 1.11×10−6CD44, ERBB2, FZD2, THBS1, MMP2
hsa04015Rap1 signaling pathway5 1.28×10−6ADCY2, FGF10, THBS1, FGF3, FGF4
hsa04512ECM-receptor interaction3 5.86×10−5CD36, CD44, THBS1
hsa05166HTLV-I infection4 8.31×10−5VCAM1, ADCY2, FZD2, CD40

E, Module 4

Term DescriptionCountP-valueGenes

hsa05205Proteoglycans in cancer4 3.03×10−5WNT5B, ERBB3, WNT9A, HGF
hsa04810Regulation of actin cytoskeleton4 3.64×10−5ITGAE, ITGA4, CD14, F2R
hsa04151PI3K-Akt signaling pathway4 2.12×10−4MYB, ITGA4, CD14, WNT5B
hsa05200Pathways in cancer4 3.71×10−4F2R, ERBB3, WNT9A, WNT5B
hsa01100Metabolic pathways6 5.98×10−4CYP3A7, ACSL5, NT5E, PTGIS, ACSS3, ACAA2

[i] Count indicates the enriched gene number in the category. PPI, protein-protein interaction; GO, gene ontology.

Four significant modules were screened from the PPI network of DEGs using MCODE plug-in, and enrichment analysis revealed that the module genes in the sub-networks were mainly associated with ‘PI3K-Akt signaling pathway’, ‘Rap1 signaling pathway’, ‘proteoglycans in cancer’ and ‘pathways in cancer’ (Table IV).

Discussion

KIT/PDGFRA mutations are the major genetic alterations that occur in the development and progression of GISTs, and have been the only targets of molecular-based therapies in the last decade (4,30). However, other genetic alterations may also be associated with the development and progression of GISTs (15). Currently, little is known about differences in gene expression levels between wild-type GISTs and KIT/PDGFRA mutant GISTs. Therefore, joint bioinformatics analysis was performed in the present study, to obtain other potential candidate biomarkers that may be involved in the development and progression of wild-type GISTs. Ultimately, a total of 546 DEGs were identified, including 238 upregulated DEGs and 308 downregulated DEGs. GO functional and KEGG pathway enrichment analysis of DEGs was subsequently performed. Traditionally, distant metastasis is determined to be the leading cause of morbidity and mortality in patients with cancer, and genes encoding adhesion proteins, inflammatory factors, cytokines, growth factors and transduction molecules are considered major mediators of metastasis (31,32). Enrichment analysis in the present study revealed that the identified DEGs may be involved in the aforementioned process and signaling pathway, and were associated with proliferation, differentiation, apoptosis and distant metastasis in GISTs.

Furthermore, PPI networks of DEGs were constructed and 25 hub genes with degrees of ≥10 were identified. Functional enrichment analysis of hub genes determined that these genes were significantly associated with heterophilic cell-cell adhesion via plasma membrane cell adhesion molecules, regulation of transmembrane transport, cardiovascular system development and angiogenesis. Additionally, pathway analysis indicated that these genes were primarily associated with the following pathways: ‘PI3K-Akt signaling pathway’, ‘proteoglycans in cancer’, ‘focal adhesion’, ‘pathways in cancer’, ‘Rap1 signaling pathway’, ‘ECM-receptor interaction’, ‘cell adhesion molecules’, ‘phospholipase D signaling pathway’, ‘cAMP signaling pathway’, ‘Ras signaling pathway’ and ‘cGMP-PKG signaling pathway’. Furthermore, pathway analyses of four significant modules filtered from the PPI network was performed, and the results revealed that the genes in these modules were also primarily involved in the aforementioned pathways. Specifically, several high-frequency hub genes were identified that may be involved in the progression of GISTs by combining these results, including PIK3CG, insulin like growth factor 1 receptor (IGF1R), HGF, thrombospondin 1 (THBS1), Erb-B2 receptor tyrosine kinase (ERBB) 2 and matrix metallopeptidase 2 (MMP2).

The PIK3CG gene is located on chromosome 7 long arm q22.3, contains 12 exons, and encodes phosphoinositide 3-kinase (PI3K)γ, which phosphorylates inositol lipids and is involved in the immune response (33). Semba et al (34) reported that the PIK3CG gene is downregulated in colorectal cancer and is involved in tumorigenesis and progression, mainly through the PI3K-protein kinase B (Akt) signaling pathway. Li et al (35) determined that the PI3K-Akt signaling pathway is partially activated following imatinib secondary resistance, and PI3K activation may occur at an early stage of secondary resistance. In the present study, PIK3CG was downregulated in wild-type GISTs. PIK3CG was identified as a hub gene with 29 degrees in the PPI network, and as the core gene of module 2. The enrichment analyses demonstrated that PIK3CG was associated with cardiovascular and vasculature development, regulation of transmembrane transport, angiogenesis and anatomical structure morphogenesis, as well as the PI3K-Akt signaling pathway, phospholipase D signaling pathway and cAMP signaling pathway. This suggested that PIK3CG may be a key molecule associated with wild-type GISTs.

IGF1R, a transmembrane receptor tyrosine kinase, serves a critical role in tumor transformation and malignant cell survival. IGF1R is predominantly involved in two signaling pathways: The PI3K-Akt pathway and the Ras-mitogen-activated protein kinase pathway (36). Ludovini et al (37) identified that activation of IGF1R is a necessary condition for mediating tumor cell proliferation and invasion, and is an independent poor prognostic factor in early stage non-small cell lung cancer. In addition, previous studies have demonstrated that IGF1R is overexpressed in wild-type GISTs, and inhibition of IGF1R signaling may be an effective therapeutic strategy (3841). In the present study, IGF1R was upregulated in wild-type GIST samples and was associated with fibroblast growth factor receptor (FGFR) 4, Janus kinase 3 and tyrosine-protein kinase SYK in module 1. Pathway analysis revealed that IGF1R was associated with the PI3K-Akt signaling pathway, proteoglycans in cancer, and ras signaling pathway. These findings were consistent with the results of the aforementioned studies and indicated that IGF1R overexpression may act as an alternative genetic alteration event to the KIT/PDGFRA mutations in GISTs. Therefore, further research is necessary to clarify underlying mechanisms of IGF1R in wild-type GISTs.

HGF, also known as scatter factor, is a member of the endothelium-specific growth factor family, and regulates cell growth and motility, migration, and angiogenesis through binding to its receptor c-Met (42). Hack et al (43) revealed that aberrant activation of the HGF/MET signaling pathway occurs in the malignant transformation and progression of gastroesophageal cancer, and consistently correlates with an aggressive metastatic phenotype and poor prognosis. However, there is a lack of research on the role of HGF in GISTs. The results of enrichment analysis in the present study demonstrated that HGF was associated with angiogenesis, cardiovascular and vasculature development, proteoglycans in cancer, focal adhesion, pathways in cancer, and PI3K-Akt signaling pathway. Furthermore, it was associated with Wnt family member (WNT) 5B, ERBB3, and WNT9A in module 4, revealing that HGF may be involved in progression of wild-type GISTs.

THBS1, also known as TSP-1, encodes an adhesive glycoprotein that is involved in platelet aggregation, angiogenesis and tumorigenesis (44). In the present study, THBS1 was associated with proteoglycans in cancer, ECM-receptor interaction, PI3K-Akt signaling pathway, and rap1 signaling pathway. In addition, functional enrichment analysis identified that THBS1 was associated with angiogenesis, protein binding, cell adhesion, regulation of transmembrane transport and inflammatory response. Kashihara et al (45) revealed that THBS1 is associated with carcinogenesis occurring in patients with intestinal inflammation, and may serve an important role in gastric carcinogenesis. Huang et al (46) reported that upregulation of THBS1 is induced by FGF7/FGFR2 via the PI3K/Akt/mechanistic target of rapamycin signaling pathway, and is associated with the regulation of invasion and migration in gastric cancer. However, no studies have elucidated the mechanism of THBS1 in GISTs. In module 3, THBS1 was associated with adenyl cyclase type 2 (ADCY2), FGF10, FGF3, FGF4, CD36 and CD44, indicating that THBS1 may also be involved in GISTs by mediating these genes. Therefore, further research is necessary to clarify the underlying mechanism of THBS1 in wild-type GISTs.

ERBB2, also known as Her2 or Neu, is a member of the ERBB family of receptor tyrosine kinases. It encodes a transmembrane tyrosine kinase receptor of the ERBB family that has important roles in many aspects of various human cancers (47,48). However, the oncogenic role and clinical significance of ERBB2 in GISTs has not been investigated in detail. In the present study, ERBB2 was upregulated and identified as the core gene of module 3. Enrichment analyses revealed that ERBB2 was associated with plasma membrane region, cardiovascular system development, angiogenesis, focal adhesion and proteoglycans in cancer. Furthermore, it was associated with ADCY2, FGF10, frizzled-2 (FZD2), FGF3, FGF4, CD44, THBS1, and MMP2, revealing these genes may have joint function in GISTs. Hence, further studies are also required to determine the role of ERBB2 in wild-type GISTs.

MMP2, a member of the matrix metalloproteinase (MMP) gene family, is involved in many cancer pathways and exists in several proteoglycans in cancer. MMP family proteins are zinc-dependent enzymes capable of cleaving components of the extracellular matrix and molecules involved in signal transduction. MMP activity has been implicated in a number of key pathological processes, including tumor growth, progression, metastasis and dysregulated angiogenesis (49). Sebastiano et al (50) reported that MMP2 increases platelet activation by cleaving PAR1 at a noncanonical extracellular site, which induces biased receptor signaling through certain signaling pathways, usually only activated by full PAR1 agonism. Furthermore, it has been reported that the gene interaction between MMP2 and PARP1 may increase the incidence of gastric cancer development and lymph node metastasis (51). In the current study, MMP2 was identified as a downregulated hub gene, enriched in pathways and proteoglycans in cancer of module 3 and associated with ADCY2, ERBB2, FGF10, FZD2, FGF3, FGF4, CD44 and THBS1.

There are several limitations of the present study that require acknowledgement. First, due to the limitations of the gene chip itself, the differentially expressed genes between GIST tissue and normal gastrointestinal tract tissue could not be identified. Second, all predicted results still require confirmation by laboratory data. Finally, a limited number of samples were used in the present study, which should be increased in future studies to improve the reliability of the conclusions drawn.

In conclusion, 546 DEGs were identified in wild-type GISTs, compared with the mutant GIST samples, which may be closely associated with GIST progression. In addition, several key hub DEGs were selected as potential candidate biomarkers for wild-type GISTs, including PIK3CG, IGF1R, HGF, THBS1, ERBB2 and MMP2. However, further verification experiments are required to confirm these results.

Acknowledgements

The authors are grateful to medical staff of Department of General Surgery, Lanzhou General Hospital of Chinese People's Liberation Army for their support.

Funding

This study was supported by the Huimin Plan of Ministry of Science and Technology & Ministry of Finance, P.R. China (grant no. 2012GS620101). the Major Projects of Science and Technology in Gansu Province of P.R. China (grant no. 2011GS04390), the Natural Science Foundation of in Gansu Province of P.R. China (grant no. 1506RJZA309), and the Postdoctoral Research Foundation of P.R. China (grant no. 2015M572710).

Availability of data and materials

The datasets analyzed in the present study are available from the GEO repository, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17743 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20708.

Authors' contributions

The original study design was undertaken by HBL, WJW and ZYJ. Data collection was undertaken JPY and YML. Microarray data were analyzed by WJW and HTL, and appraised by XPH, PC and WWY. Functional and pathway enrichment analysis was undertaken by XPH, PC, WWY and WKC. The draft manuscript was written by WJW, and was reviewed and edited by HTL, JPY, WKC, ZYJ and HBL. All authors have read and approved the final version of the manuscript.

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 

Valsangkar N, Sehdev A, Misra S, Zimmers TA, O'Neil BH and Koniaris LG: Current management of gastrointestinal stromal tumors: Surgery, current biomarkers, mutations, and therapy. Surgery. 158:1149–1164. 2015. View Article : Google Scholar : PubMed/NCBI

2 

Ducimetière F, Lurkin A, Ranchère-Vince D, Decouvelaere AV, Péoc'h M, Istier L, Chalabreysse P, Muller C, Alberti L, Bringuier PP, et al: Incidence of sarcoma histotypes and molecular subtypes in a prospective epidemiological study with central pathology review and molecular testing. PLoS One. 6:e202942011. View Article : Google Scholar : PubMed/NCBI

3 

Patil DT and Rubin BP: Gastrointestinal stromal tumor: Advances in diagnosis and management. Arch Pathol Lab Med. 135:1298–1310. 2011. View Article : Google Scholar : PubMed/NCBI

4 

Joensuu H, Hohenberger P and Corless CL: Gastrointestinal stromal tumour. Lancet. 382:973–983. 2013. View Article : Google Scholar : PubMed/NCBI

5 

Joensuu H, Vehtari A, Riihimaki J, Nishida T, Steigen SE, Brabec P, Plank L, Nilsson B, Cirilli C, Braconi C, et al: Risk of recurrence of gastrointestinal stromal tumour after surgery: An analysis of pooled population-based cohorts. Lancet Oncol. 13:265–274. 2012. View Article : Google Scholar : PubMed/NCBI

6 

Pedroso FE, Raut CP, Xiao H, Yeo CJ and Koniaris LG: Has the survival rate for surgically resected gastric gastrointestinal stromal tumors improved in the tyrosine kinase inhibitor era? Ann Surg Oncol. 19:1748–1758. 2012. View Article : Google Scholar : PubMed/NCBI

7 

Artinyan A, Kim J, Soriano P, Chow W, Bhatia S and Ellenhorn JD: Metastatic gastrointestinal stromal tumors in the era of imatinib: Improved survival and elimination of socioeconomic survival disparities. Cancer Epidemiol Biomarkers Prev. 17:2194–2201. 2008. View Article : Google Scholar : PubMed/NCBI

8 

Corless CL, Barnett CM and Heinrich MC: Gastrointestinal stromal tumours: Origin and molecular oncology. Nat Rev Cancer. 11:865–878. 2011. View Article : Google Scholar : PubMed/NCBI

9 

Vernuccio F, Taibbi A, Picone DLA, Grutta L, Midiri M, Lagalla R, Lo Re G and Bartolotta TV: Imaging of gastrointestinal stromal tumors: From diagnosis to evaluation of therapeutic response. Anticancer Res. 36:2639–2648. 2016.PubMed/NCBI

10 

Heinrich MC, Corless CL, Duensing A, McGreevey L, Chen CJ, Joseph N, Singer S, Griffith DJ, Haley A, Town A, et al: PDGFRA activating mutations in gastrointestinal stromal tumors. Science. 299:708–710. 2003. View Article : Google Scholar : PubMed/NCBI

11 

Ricci R: Syndromic gastrointestinal stromal tumors. Hered Cancer Clin Pract. 14:152016. View Article : Google Scholar : PubMed/NCBI

12 

Joensuu H and DeMatteo RP: The management of gastrointestinal stromal tumors: A model for targeted and multidisciplinary therapy of malignancy. Annu Rev Med. 63:247–258. 2012. View Article : Google Scholar : PubMed/NCBI

13 

Gramza AW, Corless CL and Heinrich MC: Resistance to tyrosine kinase inhibitors in gastrointestinal stromal tumors. Clin Cancer Res. 15:7510–7518. 2009. View Article : Google Scholar : PubMed/NCBI

14 

Ostrowski J, Polkowski M, Paziewska A, Skrzypczak M, Goryca K, Rubel T, Kokoszyñska K, Rutkowski P, Nowecki ZI, Vel Dobosz AJ, et al: Functional features of gene expression profiles differentiating gastrointestinal stromal tumours according to KIT mutations and expression. BMC Cancer. 9:4132009. View Article : Google Scholar : PubMed/NCBI

15 

Astolfi A, Nannini M, Pantaleo MA, Di Battista M, Heinrich MC, Santini D, Catena F, Corless CL, Maleddu A, Saponara M, et al: A molecular portrait of gastrointestinal stromal tumors: An integrative analysis of gene expression profiling and high-resolution genomic copy number. Lab Invest. 90:1285–1294. 2010. View Article : Google Scholar : PubMed/NCBI

16 

Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U and Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 4:249–264. 2003. View Article : Google Scholar : PubMed/NCBI

17 

R Core Team, . R: A language and environment for statistical computing. R Foundation for Statistical Computing; Vienna: http://www.R-project.org/2017

18 

Leek JT, Johnson WE, Parker HS, Jaffe AE and Storey JD: The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 28:882–883. 2012. View Article : Google Scholar : PubMed/NCBI

19 

Paradis E, Claude J and Strimmer K: APE: Analyses of phylogenetics and evolution in R language. Bioinformatics. 20:289–290. 2004. View Article : Google Scholar : PubMed/NCBI

20 

Smyth GK, Michaud J and Scott HS: Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics. 21:2067–2075. 2005. View Article : Google Scholar : PubMed/NCBI

21 

Metsalu T and Vilo J: ClustVis: A web tool for visualizing clustering of multivariate data using principal component analysis and heatmap. Nucleic Acids Res. 43:W566–W570. 2015. View Article : Google Scholar : PubMed/NCBI

22 

Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI

23 

Maere S, Heymans K and Kuiper M: BiNGO: A Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 21:3448–3449. 2005. View Article : Google Scholar : PubMed/NCBI

24 

Xie C, Mao X, Huang J, Ding Y, Wu J, Dong S, Kong L, Gao G, Li CY and Wei L: KOBAS 2.0: A web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 39(Web Server Issue): W316–W322. 2011. View Article : Google Scholar : PubMed/NCBI

25 

Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, et al: The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 45:D362–D368. 2017. View Article : Google Scholar : PubMed/NCBI

26 

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI

27 

Chin CH, Chen SH, Wu HH, Ho CW, Ko MT and Lin CY: CytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 8 Suppl 4:S112014. View Article : Google Scholar : PubMed/NCBI

28 

Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman WH, Pagès F, Trajanoski Z and Galon J: ClueGO: A Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 25:1091–1093. 2009. View Article : Google Scholar : PubMed/NCBI

29 

Bader GD and Hogue CW: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 4:22003. View Article : Google Scholar : PubMed/NCBI

30 

Ahmad F, Lad P, Bhatia S and Das BR: Molecular spectrum of c-KIT and PDGFRA gene mutations in gastro intestinal stromal tumor: Determination of frequency, distribution pattern and identification of novel mutations in Indian patients. Med Oncol. 32:4242015. View Article : Google Scholar : PubMed/NCBI

31 

Eccles SA and Welch DR: Metastasis: Recent discoveries and novel treatment strategies. Lancet. 369:1742–1757. 2007. View Article : Google Scholar : PubMed/NCBI

32 

Malanchi I, Santamaria-Martínez A, Susanto E, Peng H, Lehr HA, Delaloye JF and Huelsken J: Interactions between cancer stem cells and their niche govern metastatic colonization. Nature. 481:85–89. 2011. View Article : Google Scholar : PubMed/NCBI

33 

Wain LV, Verwoert GC, O'Reilly PF, Shi G, Johnson T, Johnson AD, Bochud M, Rice KM, Henneman P, Smith AV, et al: Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure. Nat Genet. 43:1005–1011. 2011. View Article : Google Scholar : PubMed/NCBI

34 

Semba S, Itoh N, Ito M, Youssef EM, Harada M, Moriya T, Kimura W and Yamakawa M: Down-regulation of PIK3CG, a catalytic subunit of phosphatidylinositol 3-OH kinase, by CpG hypermethylation in human colorectal carcinoma. Clin Cancer Res. 8:3824–3831. 2002.PubMed/NCBI

35 

Li J, Dang Y, Gao J, Li Y, Zou J and Shen L: PI3K/AKT/mTOR pathway is activated after imatinib secondary resistance in gastrointestinal stromal tumors (GISTs). Med Oncol. 32:1112015. View Article : Google Scholar : PubMed/NCBI

36 

Yamaguchi R, Harada H and Hirota K: VHL-deficient renal cancer cells gain resistance to mitochondria-activating apoptosis inducers by activating AKT through the IGF1R-PI3K pathway. Tumour Biol. 37:13295–13306. 2016. View Article : Google Scholar : PubMed/NCBI

37 

Ludovini V, Flacco A, Bianconi F, Ragusa M, Vannucci J, Bellezza G, Chiari R, Minotti V, Pistola L, Tofanetti FR, et al: Concomitant high gene copy number and protein overexpression of IGF1R and EGFR negatively affect disease-free survival of surgically resected non-small-cell-lung cancer patients. Cancer Chemother Pharmacol. 71:671–680. 2013. View Article : Google Scholar : PubMed/NCBI

38 

Lee EJ, Kang G, Kang SW, Jang KT, Lee J, Park JO, Park CK, Sohn TS, Kim S and Kim KM: GSTT1 copy number gain and ZNF overexpression are predictors of poor response to imatinib in gastrointestinal stromal tumors. PLoS One. 8:e772192013. View Article : Google Scholar : PubMed/NCBI

39 

Tarn C, Rink L, Merkel E, Flieder D, Pathak H, Koumbi D, Testa JR, Eisenberg B, von Mehren M and Godwin AK: Insulin-like growth factor 1 receptor is a potential therapeutic target for gastrointestinal stromal tumors. Proc Natl Acad Sci USA. 105:8387–8392. 2008. View Article : Google Scholar : PubMed/NCBI

40 

Belinsky MG, Rink L, Flieder DB, Jahromi MS, Schiffman JD, Godwin AK and Mehren Mv: Overexpression of insulin-like growth factor 1 receptor and frequent mutational inactivation of SDHA in wild-type SDHB-negative gastrointestinal stromal tumors. Genes Chromosomes Cancer. 52:214–224. 2013. View Article : Google Scholar : PubMed/NCBI

41 

Pantaleo MA, Ravegnini G, Astolfi A, Simeon V, Nannini M, Saponara M, Urbini M, Gatto L, Indio V, Sammarini G, et al: Integrating miRNA and gene expression profiling analysis revealed regulatory networks in gastrointestinal stromal tumors. Epigenomics. 8:1347–1366. 2016. View Article : Google Scholar : PubMed/NCBI

42 

Patel MB, Pothula SP, Xu Z, Lee AK, Goldstein D, Pirola RC, Apte MV and Wilson JS: The role of the hepatocyte growth factor/c-MET pathway in pancreatic stellate cell-endothelial cell interactions: Antiangiogenic implications in pancreatic cancer. Carcinogenesis. 35:1891–1900. 2014. View Article : Google Scholar : PubMed/NCBI

43 

Hack SP, Bruey JM and Koeppen H: HGF/MET-directed therapeutics in gastroesophageal cancer: A review of clinical and biomarker development. Oncotarget. 5:2866–2880. 2014. View Article : Google Scholar : PubMed/NCBI

44 

Soto-Pantoja DR, Sipes JM, Martin-Manso G, Westwood B, Morris NL, Ghosh A, Emenaker NJ and Roberts DD: Dietary fat overcomes the protective activity of thrombospondin-1 signaling in the Apc(Min/+) model of colon cancer. Oncogenesis. 5:e2302016. View Article : Google Scholar : PubMed/NCBI

45 

Kashihara H, Shimada M, Yoshikawa K, Higashijima J, Tokunaga T, Nishi M, Takasu C and Ishikawa D: Correlation between thrombospondin-1 expression in non-cancer tissue and gastric carcinogenesis. Anticancer Res. 37:3547–3552. 2017.PubMed/NCBI

46 

Huang T, Wang L, Liu D, Li P, Xiong H, Zhuang L, Sun L, Yuan X and Qiu H: FGF7/FGFR2 signal promotes invasion and migration in human gastric cancer through upregulation of thrombospondin-1. Int J Oncol. 50:1501–1512. 2017. View Article : Google Scholar : PubMed/NCBI

47 

Moasser MM: Targeting the function of the HER2 oncogene in human cancer therapeutics. Oncogene. 26:6577–6592. 2007. View Article : Google Scholar : PubMed/NCBI

48 

Van Cutsem E, Sagaert X, Topal B, Haustermans K and Prenen H: Gastric cancer. Lancet. 388:2654–2664. 2016. View Article : Google Scholar : PubMed/NCBI

49 

Roy R, Yang J and Moses MA: Matrix metalloproteinases as novel biomarkers and potential therapeutic targets in human cancer. J Clin Oncol. 27:5287–5297. 2009. View Article : Google Scholar : PubMed/NCBI

50 

Sebastiano M, Momi S, Falcinelli E, Bury L, Hoylaerts MF and Gresele P: A novel mechanism regulating human platelet activation by MMP-2-mediated PAR1 biased signaling. Blood. 129:883–895. 2017. View Article : Google Scholar : PubMed/NCBI

51 

Kim J, Pyun JA, Cho SW, Lee K and Kwack K: Lymph node metastasis of gastric cancer is associated with the interaction between poly(ADP-ribose) polymerase 1 and matrix metallopeptidase 2. DNA Cell Biol. 30:1011–1017. 2011. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

November-2018
Volume 18 Issue 5

Print ISSN: 1791-2997
Online ISSN:1791-3004

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Wang WJ, Li HT, Yu JP, Li YM, Han XP, Chen P, Yu WW, Chen WK, Jiao ZY, Liu HB, Liu HB, et al: Identification of key genes and associated pathways in KIT/PDGFRA wild‑type gastrointestinal stromal tumors through bioinformatics analysis. Mol Med Rep 18: 4499-4515, 2018
APA
Wang, W., Li, H., Yu, J., Li, Y., Han, X., Chen, P. ... Liu, H. (2018). Identification of key genes and associated pathways in KIT/PDGFRA wild‑type gastrointestinal stromal tumors through bioinformatics analysis. Molecular Medicine Reports, 18, 4499-4515. https://doi.org/10.3892/mmr.2018.9457
MLA
Wang, W., Li, H., Yu, J., Li, Y., Han, X., Chen, P., Yu, W., Chen, W., Jiao, Z., Liu, H."Identification of key genes and associated pathways in KIT/PDGFRA wild‑type gastrointestinal stromal tumors through bioinformatics analysis". Molecular Medicine Reports 18.5 (2018): 4499-4515.
Chicago
Wang, W., Li, H., Yu, J., Li, Y., Han, X., Chen, P., Yu, W., Chen, W., Jiao, Z., Liu, H."Identification of key genes and associated pathways in KIT/PDGFRA wild‑type gastrointestinal stromal tumors through bioinformatics analysis". Molecular Medicine Reports 18, no. 5 (2018): 4499-4515. https://doi.org/10.3892/mmr.2018.9457