Abstract
Background
Gastric adenocarcinoma accounts for 95% of all gastric malignant tumors. The purpose of this research was to identify differentially expressed genes (DEGs) of gastric adenocarcinoma by use of bioinformatics methods.
Material/Methods
The gene microarray datasets of GSE103236, GSE79973, and GSE29998 were imported from the GEO database, containing 70 gastric adenocarcinoma samples and 68 matched normal samples. Gene ontology (GO) and KEGG analysis were applied to screened DEGs; Cytoscape software was used for constructing protein-protein interaction (PPI) networks and to perform module analysis of the DEGs. UALCAN was used for prognostic analysis.
Results
We identified 2909 upregulated DEGs (uDEGs) and 7106 downregulated DEGs (dDEGs) of gastric adenocarcinoma. The GO analysis showed uDEGs were enriched in skeletal system development, cell adhesion, and biological adhesion. KEGG pathway analysis showed uDEGs were enriched in ECM-receptor interaction, focal adhesion, and Cytokine-cytokine receptor interaction. The top 10 hub genes – COL1A1, COL3A1, COL1A2, BGN, COL5A2, THBS2, TIMP1, SPP1, PDGFRB, and COL4A1 – were distinguished from the PPI network. These 10 hub genes were shown to be significantly upregulated in gastric adenocarcinoma tissues in GEPIA. Prognostic analysis of the 10 hub genes via UALCAN showed that the upregulated expression of COL3A1, COL1A2, BGN, and THBS2 significantly reduced the survival time of gastric adenocarcinoma patients. Module analysis revealed that gastric adenocarcinoma was related to 2 pathways: including focal adhesion signaling and ECM-receptor interaction.
Conclusions
This research distinguished hub genes and relevant signal pathways, which contributes to our understanding of the molecular mechanisms, and could be used as diagnostic indicators and therapeutic biomarkers for gastric adenocarcinoma.
MeSH Keywords: Prognosis; Stomach Neoplasms; Tumor Markers, Biological
Background
Gastric cancer (GC) is a common malignant disease with a mortality rate of about 10% [1], which does a great harm to global health. Gastric adenocarcinoma (GAC) is the most common pathological type of gastric cancer, accounting for 95% of gastric malignant tumors [2], and it is characterized by easy invasion and metastasis [3]. Most GC patients are diagnosed in advanced stages, which is the major reason for its poor prognosis [4]. Although multimodal therapy, including surgery, chemotherapy, radiotherapy, and targeted therapy, has recently improved, the 5-year overall survival rate of patients with terminal GC is still less than 20% [5], and it can be as high as 90% if GC is detected in the early stage [6]. Accordingly, the early diagnosis and treatment of GAC is crucial.
Studies have shown that many biochemical molecular markers are involved in the occurrence and development of tumors and can be used for early screening of tumors. However, many markers are highly expressed in various types of tumors and do not have good specificity [7]. Therefore, it is necessary to further explore new and specific diagnostic markers of gastric adenocarcinoma as an auxiliary detection project for early diagnosis. Recently, bioinformatics has become a promising and effective tool for screening significant genetic or epigenetic variations that occur in carcinogenesis and determine the diagnosis and prognosis of cancer [8]. Various bioinformatics databases, such as the GEO database, provide opportunities for data mining for gene expression profiles of cancer.
In this study, we imported 3 gastric adenocarcinoma datasets from the GEO database. We screened differentially expressed genes (DEGs) by comparing the gene expression between gastric adenocarcinoma samples and paired normal mucosa samples. Then, function annotations and signal pathway analysis of DEGs were performed using Gene ontology (GO) and KEGG signal pathway enrichment analysis in the DAVID database. Subsequently, to study the mechanism of occurrence and development of GAC at the molecular level, we used UALCAN for prognosis analysis and GEPIA for verification of the mRNA expression level, which may provide valuable insights for diagnosis, targeted drug research, and prognosis evaluation of GAC.
Material and Methods
Datasets
The Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo) is a communal functional genic database including array-based and sequence-based data, and is available to users free of charge. The gene expression datasets of GSE103236 [9], GSE79973 [10], and GSE29998 [11] were acquired from the GEO database. The 3 datasets selected in this experiment all met 3 criteria: (1) samples from human gastric tissue; (2) with case-control group; and (3) sample number ≥18, and only for the pathological type of GAC. GSE103236 was based on the GPL4133 platform (Agilent-014850 Whole Human Genome Microarray 4x44K G4112F). GSE79973 was based on the GPL570 platform ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array). GSE29998 was based on the GPL6947 platform (Illumina HumanHT-12 V3.0 expression BeadChip). GSE103236 contains 19 samples, including 10 gastric adenocarcinoma samples and 9 matched normal mucosa samples. GSE79973 contains 20 samples, including 10 gastric adenocarcinoma samples and 10 matched normal mucosa samples. GSE29998 contains 99 samples, including 50 gastric adenocarcinoma samples and 49 matched normal mucosa samples.
Data processing
GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/) is an online tool with which different groups of samples from the GEO database can be compared to identify DEGs [12]. The data were divided into a gastric adenocarcinoma group and a normal group for further analysis by GEO2R. The benchmark adj. p<0.05 and |log2FC|>1 were determined as the cutoff values for statistical analysis of each dataset, and the intersecting parts of the 3 datasets were determined by use of the online tool Draw Venn diagram (bioinformatics. psb. ugent. be/webtools/Venn/).
Gene ontology (GO) and KEGG signal pathway analysis of DEGs
The GO (http://www.geneontology.org) database [13] can provide functional classification for genomic data, including biological processes (BP), cellular component (CC), and molecular function (MF). GO analysis is a widely used annotating tool of genes and genic productions. The Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.ad.jp/kegg/) database [14] is a networked website designed for genic function analysis, exegesis, and visualizing. The Database for Annotation, Visualization and Integrated Discovery (DAVID, http://david.abcc.ncifcrf.gov/) [15] is an online tool for genic functional classification, which can be applied for gene analysis to assess the biological function of genes. In this research, GO enrichment analysis and KEGG pathway analysis were applied using the DAVID website to study the functions of DEGs. p<0.05 was set as the cutoff point for statistical significance.
Integration of protein-protein interaction (PPI) network and module analysis
The Search Tool for the Retrieval of Interacting Genes (STRING, http://string.embl.de/) [16] is a biological database designed for predicting PPI networks. The DEGs were imported to STRING to assess the interactive relationships, and a confidence score >0.9 was considered as significant. Then, we used Cytoscape [17], which biological graph visualization software that can construct comprehensive models of biologic molecular interaction. The Molecular Complex Detection (MCODE), a pluggable unit of Cytoscape, was applied for screening the modules of the PPI network. The benchmarks were determined as: degree cutoff=2, node score cutoff=0.2, k-core=4, and maximum depth=100. The KEGG signal pathway enrichment analysis was reapplied to DEGs located in the modules to study their major functions.
Expression levels and prognostic analysis of hub genes
GEPIA (Gene Expression Profiling Interactive Analysis) [18] is a well-known platform that can be used to analyze differences in the mRNA expression levels of a specific gene in specific cancers between cancerous tissues and paired normal tissues. We used GEPIA to study mRNA expression levels of hub genes in GAC and paired normal tissues. UALCAN (http://ualcan.path.uab.edu) [19] was used to assess the prognosis of hub genes. For each gene, cancer patients were automatically separated into high-expression and low-expression groups in accordance with the expression value of RNA, and the difference p<0.05 was regarded as significant.
Results
Screening of DEGs
In the aggregate, 70 gastric adenocarcinoma samples and 68 matched normal mucosa samples from 3 datasets were analyzed. In view of the GEO2R analysis, using the adj. p<0.05 and |log2FC|>1 criteria, 2909 upregulated DEGs (uDEGs) and 7106 downregulated DEGs (dDEGs) were screened in GAC tissues compared with normal tissues (Figure 1). A total of 250 genes were collected from all 3 datasets, including 92 uDEGs (Figure 2, Table 1) and 158 dDEGs (Figure 2, Table 1).
Figure 1.
Volcano plot of all significant DEGs. Volcano plot of all significant DEGs, including a total of 2909 uDEGs and 7106 dDEGs. Red color means uDEGs, green color represents dDEGs, and blue color represents genes that are not significantly different in expression. The criterion: |foldchange|>1, p<0.05 is determined as the cutoff value.
Figure 2.
(A, B) Venn diagram of all screened DEGs. Venn diagram shows: uDEGs shared by GSE103236, GSE79973, and GSE29998 microarrays. A total of 92 uDEGs and 158 dDEGs were identified in the intersections.
Table 1.
DEGs in gastric adenocarcinoma shared in 3 microarrays.
| DEGS | Total | Elements |
|---|---|---|
| uDEGs | 92 | IGF2BP3, CDC25B, C5AR1, ZMYND15, COL1A1, GDPD5, ANLN, CHRNA5, FNDC1, COL18A1, PRRX1, PDGFRB, COL5A2, KIF4A, THY1, ASCL2, ANTXR1, SPP1, WNT5A, OLR1, MSR1, MELK, CDH11, TIMP1, BGN, COL8A1, TEAD4, ECT2, MMP11, KRT80, DDX31, FSCN1, SRPX2, WNT2, LRP8, CEMIP, BMP1, DIO2, ARPC1B, MFAP2, WISP1, VMO1, COL4A1, SLC1A3, SULF1, CLDN1, COL11A1, TREM1, COL1A2, APOC1, COL12A1, ESM1, ARHGAP11A, PLAU, RFC3, TGM2, OSMR, FOXC1, CHEK1, TNFRSF11B, VSNL1, IGFBP7, CST1, RCC2, LEF1, IL13RA2, LZTS1, SPHK1, KIF2C, AGPAT4, BUB1, TNFRSF12A, TROAP, ANGPT2, COL3A1, TMEM158, SERPINH1, FAP, INHBA, CDCA3, SLC5A6, CKAP2, THBS2, OLFML2B, S100A10, COL6A3, CSF2RA, HSD11B1, PMEPA1, CTHRC1, GAD1, NCAPG |
| dDEGs | 158 | EPB41L4B, COL4A5, ZNF385B, NRG4, IRX3, KLF4, NDRG2, IGFBP2, SLC9A2, SCNN1B, ESRRB, HTR4, F13A1, CNTD1, ADHFE1, CELA3B, OSBPL7, ADGRG2, MYOC, GIF, GPER1, HBB, GNG7, COL4A6, ERO1B, SOSTDC1, SBSPON, TMED6, ARHGAP24, CKMT2, KCNJ16, SLC26A7, ADAMTSL1, SYT4, PTGER3, ATP4A, DNASE1L3, CKB, MAL, AQP4, ESRRG, STOX2, CPA2, OXCT1, ABCA8, TTLL7, AXDND1, FXYD4, PER3, SHMT1, ETNPPL, DPT, PACRG, CAPN13, SLC5A5, FGA, NTN1, LGI1, ATP4B, EPM2A, SLC2A4, ADH1A, KLF15, SCUBE2, GPX3, KCNMB2, MAMDC2, SELENBP1, GPAT3, CLIC6, HAPLN1, TRIM50, B3GAT1, MS4A2, BHMT, GHRL, PNOC, GPRC5C, KCNJ13, DGKD, BMP6, GC, ALDH6A1, PDGFD, HTR1E, KIT, ADH1C, TOX, GCNT2, SST, PLCXD3, XYLT2, SLC6A16, CWH43, PDK4, GFRA2, GPR155, GREM2, NR3C2, PLA2G1B, MUC6, LINGO2, RNASE1, MYZAP, GUCA1C, AKR1C1, ACACB, PNPLA7, FXYD1, BAALC, PPP2R3A, BMP5, CKM, FBXL13, COBLL1, RGMB, FBP2, FAM150B, HIF3A, GSTA4, FGG, CHGA, RAB26, CAPN9, MT1M, SGSM1, ASPA, SCGN, SULT2A1, GAMT, CDHR3, CCKBR, ADRB2, GRIA3, SCGB2A1, SLC7A8, DUOX1, SORCS1, ARHGEF37, SCARA5, PACSIN1, LIFR, FNDC5, GLUL, CHIA, METTL7A, FAM189A2, SSTR1, PAIP2B, ACER2, ADH1B, MYRIP, KCNE2, PPP1R3C, TCEA3, PDE8B, SIGLEC11, KBTBD12 |
GO term enrichment analysis
GO analysis outcomes showed that for biological process (BP), uDEGs were markedly enriched in skeletal system development, cell adhesion, and biological adhesion (Figure 3, Table 2); the dDEGs are mainly in ion transport, homeostatic process, and chemical homeostatic (Figure 3, Table 2). For molecular function (MF), the uDEGs are enriched in structural molecule activity, extracellular matrix (ECM) structural constituent, and growth factor binding (Figure 3, Table 2); and the dDEGs are enriched in channel activity, passive transmembrane transporter activity, and substrate specific channel activity (Figure 3, Table 2). Cellular component (CC) analysis revealed that uDEGs are concentrated in extracellular region, extracellular region part, and proteinaceous extract (Figure 3, Table 2); and dDEGs are concentrated in extracellular region, plasma membrane part, and extracellular region part (Figure 3, Table 2).
Figure 3.
Gene ontology analysis of DEGs related to gastric adenocarcinoma. The x-axis stands for the number of DEGs, and the vertical axis stands for GO terms, (A) The top 10 enriched biological processes (BP), cellular component (CC), and molecular function (MF) of 92 uDEGs. (B) The top 10 enriched BP, CC, and MF for 158 dDEGs.
Table 2.
Gene ontology analysis of DEGs related to gastric adenocarcinoma.
| Expression | Category | Term | Count | % | P value | FDR |
|---|---|---|---|---|---|---|
| Upregulated | GOTERM_BP_FAT | GO: 0001501~skeletal system development | 19 | 3.140495868 | 6.25E-17 | 1.67E-13 |
| GOTERM_BP_FAT | GO: 0007155~cell adhesion | 20 | 3.305785124 | 4.39E-12 | 6.55E-09 | |
| GOTERM_BP_FAT | GO: 0022610~biological adhesion | 20 | 3.305785124 | 4.50E-12 | 6.72E-09 | |
| GOTERM_BP_FAT | GO: 0030199~collagen fibril organization | 8 | 1.32231405 | 1.19E-11 | 1.78E-08 | |
| GOTERM_BP_FAT | GO: 0030198~extracellular matrix organization | 8 | 1.32231405 | 1.30E-07 | 1.95E-04 | |
| GOTERM_BP_FAT | GO: 0001503~ossification | 8 | 1.32231405 | 2.60E-07 | 3.89E-04 | |
| GOTERM_BP_FAT | GO: 0060348~bone development | 8 | 1.32231405 | 4.12E-07 | 6.16E-04 | |
| GOTERM_BP_FAT | GO: 0043062~extracellular structure organization | 8 | 1.32231405 | 2.75E-06 | 0.0041046 | |
| GOTERM_BP_FAT | GO: 0032963~collagen metabolic process | 5 | 0.826446281 | 3.71E-06 | 0.005542186 | |
| GOTERM_BP_FAT | GO: 0043588~skin development | 5 | 0.826446281 | 4.29E-06 | 0.006410681 | |
| GOTERM_CC_FAT | GO: 0005578~proteinaceous extracellular matrix | 25 | 4.132231405 | 1.86E-24 | 2.10E-21 | |
| GOTERM_CC_FAT | GO: 0031012~extracellular matrix | 25 | 4.132231405 | 1.14E-23 | 1.28E-20 | |
| GOTERM_CC_FAT | GO: 0005576~extracellular region | 40 | 6.611570248 | 6.86E-20 | 7.75E-17 | |
| GOTERM_CC_FAT | GO: 0044421~extracellular region part | 30 | 4.958677686 | 8.27E-19 | 9.34E-16 | |
| GOTERM_CC_FAT | GO: 0044420~extracellular matrix part | 14 | 2.314049587 | 1.64E-15 | 1.88E-12 | |
| GOTERM_CC_FAT | GO: 0005581~collagen | 9 | 1.487603306 | 1.49E-12 | 1.68E-09 | |
| GOTERM_CC_FAT | GO: 0005583~fibrillar collagen | 5 | 0.826446281 | 1.48E-07 | 1.67E-04 | |
| GOTERM_CC_FAT | GO: 0005604~basement membrane | 6 | 0.991735537 | 2.04E-05 | 0.023013251 | |
| GOTERM_CC_FAT | GO: 0031093~platelet alpha granule lumen | 5 | 0.826446281 | 2.76E-05 | 0.031185256 | |
| GOTERM_CC_FAT | GO: 0060205~cytoplasmic membrane-bounded vesicle lumen | 5 | 0.826446281 | 3.67E-05 | 0.041403378 | |
| GOTERM_MF_FAT | GO: 0005201~extracellular matrix structural constituent | 11 | 1.818181818 | 5.06E-13 | 5.90E-10 | |
| GOTERM_MF_FAT | GO: 0005198~structural molecule activity | 14 | 2.314049587 | 3.15E-07 | 3.67E-04 | |
| GOTERM_MF_FAT | GO: 0005518~collagen binding | 5 | 0.826446281 | 8.88E-06 | 0.010355681 | |
| GOTERM_MF_FAT | GO: 0005539~glycosaminoglycan binding | 7 | 1.157024793 | 1.19E-05 | 0.013923194 | |
| GOTERM_MF_FAT | GO: 0001871~pattern binding | 7 | 1.157024793 | 2.05E-05 | 0.023961179 | |
| GOTERM_MF_FAT | GO: 0030247~polysaccharide binding | 7 | 1.157024793 | 2.05E-05 | 0.023961179 | |
| GOTERM_MF_FAT | GO: 0008201~heparin binding | 6 | 0.991735537 | 3.72E-05 | 0.043401787 | |
| GOTERM_MF_FAT | GO: 0050840~extracellular matrix binding | 4 | 0.661157025 | 1.30E-04 | 0.151895387 | |
| GOTERM_MF_FAT | GO: 0005509~calcium ion binding | 12 | 1.983471074 | 4.20E-04 | 0.488103889 | |
| GOTERM_MF_FAT | GO: 0008237~metallopeptidase activity | 6 | 0.991735537 | 5.52E-04 | 0.641829759 | |
| Downregulated | GOTERM_BP_FAT | GO: 0007586~digestion | 12 | 1.038062284 | 7.45E-12 | 1.09E-08 |
| GOTERM_BP_FAT | GO: 0055114~oxidation reduction | 15 | 1.297577855 | 3.07E-05 | 0.045029161 | |
| GOTERM_BP_FAT | GO: 0006081~cellular aldehyde metabolic process | 4 | 0.346020761 | 6.53E-04 | 0.952399247 | |
| GOTERM_BP_FAT | GO: 0030001~metal ion transport | 10 | 0.865051903 | 0.00213818 | 3.08666015 | |
| GOTERM_BP_FAT | GO: 0006812~cation transport | 10 | 0.865051903 | 0.006666616 | 9.333089916 | |
| GOTERM_BP_FAT | GO: 0006811~ion transport | 12 | 1.038062284 | 0.007065784 | 9.865306976 | |
| GOTERM_BP_FAT | GO: 0046903~secretion | 7 | 0.605536332 | 0.010249221 | 14.00683938 | |
| GOTERM_BP_FAT | GO: 0015672~monovalent inorganic cation transport | 7 | 0.605536332 | 0.01337189 | 17.89646714 | |
| GOTERM_BP_FAT | GO: 0006813~potassium ion transport | 5 | 0.432525952 | 0.016821807 | 22.00279704 | |
| GOTERM_BP_FAT | GO: 0022600~digestive system process | 3 | 0.259515571 | 0.018374485 | 23.78773935 | |
| GOTERM_CC_FAT | GO: 0005576~extracellular region | 31 | 2.6816609 | 1.26E-05 | 0.014253173 | |
| GOTERM_CC_FAT | GO: 0045177~apical part of cell | 7 | 0.605536332 | 0.001371031 | 1.545706714 | |
| GOTERM_CC_FAT | GO: 0016324~apical plasma membrane | 6 | 0.519031142 | 0.002113034 | 2.373127171 | |
| GOTERM_CC_FAT | GO: 0005624~membrane fraction | 11 | 0.951557093 | 0.047644803 | 42.55171216 | |
| GOTERM_MF_FAT | GO: 0031420~alkali metal ion binding | 7 | 0.605536332 | 0.004013875 | 5.198242704 | |
| GOTERM_MF_FAT | GO: 0004033~aldo-keto reductase activity | 3 | 0.259515571 | 0.004305787 | 5.566366984 | |
| GOTERM_MF_FAT | GO: 0004198~calcium-dependent cysteine-type endopeptidase activity | 3 | 0.259515571 | 0.004899806 | 6.31139254 | |
| GOTERM_MF_FAT | GO: 0008289~lipid binding | 9 | 0.778546713 | 0.009796998 | 12.2496158 | |
| GOTERM_MF_FAT | GO: 0016620~oxidoreductase activity, acting on the aldehyde or oxo group of donors, NAD or NADP as acceptor | 3 | 0.259515571 | 0.010025001 | 12.51741656 | |
| GOTERM_MF_FAT | GO: 0030955~potassium ion binding | 5 | 0.432525952 | 0.010277041 | 12.81257072 | |
| GOTERM_MF_FAT | GO: 0008233~peptidase activity | 10 | 0.865051903 | 0.013517076 | 16.52574449 | |
| GOTERM_MF_FAT | GO: 0015267~channel activity | 8 | 0.692041522 | 0.019316845 | 22.80964632 | |
| GOTERM_MF_FAT | GO: 0022803~passive transmembrane transporter activity | 8 | 0.692041522 | 0.01954695 | 23.04969236 | |
| GOTERM_MF_FAT | GO: 0008900~hydrogen: potassium-exchanging ATPase activity | 2 | 0.173010381 | 0.019742308 | 23.25294859 |
KEGG signal pathway analysis
The most remarkably enriched pathways of uDEGs and dDEGs identified by KEGG analysis are shown in Table 3. The uDEGs are enriched in focal adhesion, ECM-receptor interaction, and cytokine-cytokine receptor interaction, while the dDEGs are enriched in pathways in arginine and proline metabolism, as well as glycine, serine, and threonine metabolism.
Table 3.
KEGG pathway analysis of DEGs associated with gastric adenocarcinoma.
| Expression | Term | Count | % | P value | FDR |
|---|---|---|---|---|---|
| Upregulated | hsa04512: ECM-receptor interaction | 11 | 1.818181818 | 2.66E-13 | 2.14E-10 |
| hsa04510: Focal adhesion | 11 | 1.818181818 | 1.80E-09 | 1.44E-06 | |
| hsa04350: TGF-beta signaling pathway | 4 | 0.661157025 | 0.005149174 | 4.056689024 | |
| Downregulated | hsa00982: Drug metabolism | 8 | 0.692041522 | 1.46E-07 | 1.41E-04 |
| hsa00830: Retinol metabolism | 7 | 0.605536332 | 1.38E-06 | 0.001338048 | |
| hsa00980: Metabolism of xenobiotics by cytochrome P450 | 7 | 0.605536332 | 2.60E-06 | 0.002518197 | |
| hsa00010: Glycolysis/Gluconeogenesis | 4 | 0.346020761 | 0.007818033 | 7.311759835 | |
| hsa00591: Linoleic acid metabolism | 3 | 0.259515571 | 0.015552893 | 14.070282 | |
| hsa00350: Tyrosine metabolism | 3 | 0.259515571 | 0.036348334 | 30.10544025 |
PPI network construction, module analysis and hub genes determination
The interaction between DEGs was calculated using the STRING database, and 250 DEGs differently expressed in all 3 data sets were imported into Cytoscape software for visualization. PPI network involves 143 nodes and 578 edges (Figure 4). The top 10 genes in connectivity ranking in the PPI network were selected as hub genes. The results showed that COL1A1 ranked highest among all DEGs, with 34 degree, followed by COL3A1, COL1A2, BGN, COL5A2, THBS2, TIMP1, SPP1, PDGFRB, and COL4A1 (Table 4).
Figure 4.
PPI network construction, module analysis, and pathway enrichment analysis. Protein-protein interaction network for products of DEGs. A total of 143 nodes and 419 interaction associations were identified. (A) The nodes mean proteins; the edges mean the interactions of proteins; green circles meant dDEGs and orange circles meant uDEGs. (B) Module analysis based on Cytoscape software. (C) KEGG pathway enrichment analysis of DEGs in the module.
Table 4.
Connectivity and regulation of the top 10 hub genes.
| Gene symbol | Gene title | Connectivity | Regulation |
|---|---|---|---|
| COL1A1 | Collagen type I alpha 1 chain | 34 | Up |
| COL3A1 | Collagen type III alpha 1 chain | 30 | Up |
| COL1A2 | Collagen type I alpha 2 chain | 29 | Up |
| BGN | Biglycan | 29 | Up |
| COL5A2 | Collagen type V alpha 2 chain | 23 | Up |
| THBS2 | Thrombospondin 2 | 23 | Up |
| TIMP1 | TIMP metallopeptidase inhibitor 1 | 21 | Up |
| SPP1 | Secreted phosphoprotein 1 | 20 | Up |
| PDGFRB | Platelet-derived growth factor receptor beta | 20 | Up |
| COL4A1 | Collagen type IV alpha 1 chain | 19 | Up |
The module analysis of 143 nodes showed that the most important module with higher score involves 15 nodes and 143 edges (Figure 4). All 15 nodes are all upregulated genes, which suggests the vital role of uDEGs in GAC. KEGG signal pathway analysis of the 15 genes showed that they mainly participated in 2 pathways: ECM-receptor interaction and focal adhesion. It is noteworthy that 8 of the 15 genes in the module (COL4A1, COL6A3, COL3A1, COL1A2, COL1A1, COL11A1, COL4A6, and THBS2) are involved in both pathways.
Expression levels and prognostic analysis of hub genes
GEPIA database showed that all 10 hub genes are upregulated in GAC (Figure 5). To assess the prognostic value of 10 hub genes, we used UALCAN for prognostic analysis. The results of the prognostic analysis showed the upregulated expression of COL3A1, COL1A2, BGN, and THBS2 significantly reduce the survival time of GAC patients (Figure 6).
Figure 5.
Validation of the gene expression levels of COL1A1, COL3A1, COL1A2, BGN, COL5A2, THBS2, TIMP1, SPP1, PDGFRB, and COL4A1 between GAC and normal gastric tissues in the GEPIA database. They are significantly upregulated in GAC compared with normal tissues (P<0.01). The red * represents P<0.01.
Figure 6.
UALCAN overall survival analysis plot of the top 10 hub genes expressed in gastric adenocarcinoma patient samples and 4 DEGs among the top 10 hub genes that are significantly related to the survival of gastric adenocarcinoma patients (P<0.05). (A) COL3A1; (B) COL1A2; (C) BGN; (D) THBS2.
Discussion
Gastric cancer is a leading cause of death. Early diagnosis and treatment are essential to prolong the survival time of GC patients. GAC is the most common type of gastric cancer. Therefore, it is crucial to further explore the predictive indicators and therapeutic targets of GAC. Recently, with the rapid development of bioinformatics, DNA microarray is increasingly applied to explore the early diagnosis, treatment, and prognosis of cancer [20]. Therefore, the present study explored the potential target genes and pathways of GAC by use of bioinformatics methods.
In this study, 2909 uDEGs and 7106 dDEGs were identified from the GSE103236, GSE79973, and GSE29998 datasets downloaded from the GEO database, among which, 92 uDEGs and 158 dDEGs were significantly expressed in all 3 datasets. To further define the role of these DEGs in gastric adenocarcinoma, we performed a series of bioinformatics and prognostic analysis of these DEGs.
GO analysis revealed uDEGs are highly involved in cell adhesion, biological adhesion, and skeletal system development, while the dDEGs are mainly in ion transport, homeostatic process, and chemical homeostasis. Studies [21] have shown that the decrease of cell adhesion is a key step in the metastasis of cancer, which agrees with our GO analysis results. For MF, the uDEGs are markedly enriched in ECM structural constituent, structural molecule activity, and growth factor binding, while the dDEGs were enriched in channel activity, passive transmembrane transporter activity, and substrate specific channel activity. GO CC analysis revealed that uDEGs were concentrated in extracellular region part, proteinaceous extract, and extracellular region, while dDEGs were concentrated in extracellular region, plasma membrane part, and extracellular region part. The role of ECM and collagen binding in development and progression of tumors has been confirmed in some previous studies [22,23], which agrees with results of the present study.
To better understanding the relationships and interactions between these DEGs, we used Cytoscape software to construct a PPI network of DEGs-encoded proteins, and screened out 10 hub genes with high degrees. The order of degree from high to low was COL1A1, COL3A1, COL1A2, BGN, COL5A2, THBS2, TIMP1, SPP1, PDGFRB, and COL4A1. COL1A1, COL1A2, COL3A1, COL5A2, and COL4A1, which belong to the collagen (COL) family, are the top 10 hub genes, which suggests that the collagen gene is likely to be a potential target for gastric adenocarcinoma. Collagen is the main protein in bone and teeth, and is involved in the adhesion of tumor cells, gap junction, and formation of extracellular matrix (ECM) [24]. COL1A1 is the major component of type I collagen. Some studies have shown that mir-129-5p stops the invasion and proliferation of gastric cancer cells by inhibiting COL1A1[25]. Ma et al. [26] found silencing the collagen gene inhibits tumor proliferation and metastasis. Our study also found the uDEGs are enriched in cell adhesion and biological adhesion at the BP level, which suggests that DEGs belonging to the COL family play a vital role in invasion and metastasis of tumor cells. Studies [27,28] showed that COL1A2 is highly expressed in colorectal cancer and medulloblastoma. Research has revealed the high expression of COL3A1 is independently associated with the low survival rate of colorectal carcinoma [29]. However, the relationship between COL3A1 and gastric adenocarcinoma has not been studied. Zhang et al. [30] found that the high expression of COL4A1 is closely related to the depth of invasion, TNM stage, and lymph node metastasis. Makito et al. [32] demonstrated that COL4A1 can promote invasive ability and invasive growth pattern by activating the AKT pathway and upregulating epithelial-mesenchymal transition. Zhao et al. [32] also used bioinformatics methods show that COL5A2 is a key factor in gastric cancer, but there is no laboratory evidence to prove that COL5A2 is involved in gastric adenocarcinoma.
ECM is a protein compound that plays an indispensable role in cell migration and cancer development [33]. BGN, as an integral part of ECM, is considered to be a pathway for malignant tumor cells to acquire migration and invasiveness [34]. Studies have shown that the expression of BGN in GC is notably upregulated, and correlated with depth of tumor invasion and TNF staging [35]. Thromboreactive protein (THBS) is an extracellular glycoprotein that plays roles in cell matrix and intercellular interactions [36]. Studies have shown that high THBS2 expression is correlated with low proliferation rate of gastric cancer cells [37]. Tissue inhibitor matrix metalloproteinase-1 (TIMP-1) is classified into the family of tissue inhibitors of metalloproteinases, and the proteins encoded by TIMP-1 are considered to be the key biofactors in the invasion and metastasis of tumors [38]. Wang et al. [39] showed that the expression level of TIMP1 in peripheral blood was associated with the stage of cancer, and the upregulation of TIMP1 may be an adverse prognostic factor for recurrence of gastric cancer. SPP1 is an ECM-related protein that has carcinogenic and anti-tumor effects [40]. Li et al. [41] also identified SPP1 as a prognostic pivotal gene in gastric cancer by bioinformatics. Sharvesh et al. [42] found that SPP1 is highly expressed in gastric cancer tissues compared with normal adjacent tissues, and its expression increased with the depth of tumor invasion. Platelet-derived growth factor receptors (PDGFRs) can induce activation of intracellular signal transduction pathways, which can promote cell proliferation, metastasis, and invasion [43]. Chen et al. [44] identified PDGFRB as a candidate gene for gastric cancer by constructing a gene co-expression network, which is consistent with the results of our study. It has also been affirmed that PDGFRB is upregulated in gastric cancer tissues, and its high expression is positively correlated with poor prognosis of gastric cancer patients [45]. These results suggest that BGN, THBS2, TIMP1, SPP1, and PDGFRB are key factors in GAC.
Module analysis from the PPI network showed that gastric adenocarcinoma is closely related to focal adhesion and ECM-receptor interaction. Focal adhesion is a complex, dynamic process involving the driving activity of actin cytoskeleton and the participation of specific receptors and signal transduction [46]. Studies have found that focal adhesions are intensely involved in multiple key pathways of tumor migration and metastasis [47]. Research by Lu et al. showed that abnormal ECM can promote the growth and metastasis of tumors by directly promoting cell metastasis on the one hand, and indirectly by promoting the formation of tumor microvessels on the other hand [48]. It is noteworthy that 8 of the 15 genes in the module (COL4A1, COL6A3, COL3A1, COL1A2, COL1A1, COL11A1, COL4A6, and THBS2) are involved in both pathways, and most of them belong to the COL family, which strengthens the findings of the role of the COL family in gastric adenocarcinoma.
To study the expression levels and prognostic value of 10 hub genes, we used GEPIA database and UALCAN for expression validation and prognostic analysis. The GEPIA database showed all the 10 hub genes are upregulated in GAC compared to normal gastric tissue. The results of the prognostic analysis showed that the upregulated expression of COL3A1, COL1A2, BGN, and THBS2 significantly reduced the survival time of GAC patients. Therefore, COL3A1, COL1A2, BGN, and THBS2 appear to be ideal prognostic indicators for gastric adenocarcinoma.
In sum, we identified DEGs and performed GO analysis, pathway enrichment analysis, and PPI network construction to understand their roles in gastric adenocarcinoma. In addition, we identified COL3A1, COL1A2, BGN, and THBS2 as hub genes and evaluated their prognostic value. This study provided evidence for early diagnosis and prognostic evaluation of gastric adenocarcinoma at the molecular level, but these findings need to be confirmed by subsequent laboratory studies.
Conclusions
In this study, we used bioinformatics to predict the DEGs of gastric adenocarcinoma and its enriched pathways and screened and evaluated some hub genes to provide some ideas and references for the early diagnosis and treatment of gastric adenocarcinoma at the molecular level. However, the limitation of our research lies in the lack of laboratory evidence. Therefore, further laboratory studies are needed to validate these findings.
Acknowledgements
We express our cordial thanks to all those who participated in this study.
Footnotes
Conflict of interest
None.
Source of support: National Natural Science Foundation (NO. 81602425); Anhui Provincial Teaching Research Project (NO. 2016jyxm0529); National Innovation and Entrepreneurship Project for College Students (NO. 201710366009)
References
- 1.Yao L, Shi W, Gu J. Micro-RNA 205-5p is involved in the progression of gastric cancer and targets phosphatase and tensin homolog (PTEN) in SGC-7901 human gastric cancer cells. Med Sci Monit. 2019;25:6367–77. doi: 10.12659/MSM.915970. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Ferlay J, Shin HR, Bray F, et al. Estimates of worldwide burden of cancer in 2008: globocan 2008. Int J Cancer. 2010;127(12):2893–917. doi: 10.1002/ijc.25516. [DOI] [PubMed] [Google Scholar]
- 3.Wu P-L, He Y-F, Yao H-H, et al. Martrilin-3 (MATN3) overexpression in gastric adenocarcinoma and its prognostic significance. Med Sci Monit. 2018;24:348–55. doi: 10.12659/MSM.908447. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Zhang Z, Zhu X. Clinical significance of lysophosphatidic acid receptor-2 (LPA2) and Krüppel-like factor 5 (KLF5) protein expression detected by tissue microarray in gastric adenocarcinoma. Med Sci Monit. 2019;25:4705–15. doi: 10.12659/MSM.916336. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Ahn S, Park DY. Practical points in gastric pathology. Arch Pathol Lab Med. 2016;140(5):397–405. doi: 10.5858/arpa.2015-0300-RA. [DOI] [PubMed] [Google Scholar]
- 6.Beeharry MK. New blood markers detection technology: A leap in the diagnosis of gastric cancer. World J Gastroenterol. 2016;22(3):1202–12. doi: 10.3748/wjg.v22.i3.1202. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Blee TK, Gray NK, Brook M. Modulation of the cytoplasmic functions of mammalian post-transcriptional regulatory proteins by methylation and acetylation: A key layer of regulation waiting to be uncovered. Biochem Soc Trans. 2015;43(6):1285–95. doi: 10.1042/BST20150172. [DOI] [PubMed] [Google Scholar]
- 8.Kulasingam V, Diamandis EP. Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nat Clin Pract Oncol. 2008;5(10):588–99. doi: 10.1038/ncponc1187. [DOI] [PubMed] [Google Scholar]
- 9.Chivu Economescu M, Necula LG, Dragu D, et al. Identification of potential biomarkers for early and advanced gastric adenocarcinoma detection. Hepatogastroenterol. 2010;57(104):1453–64. [PubMed] [Google Scholar]
- 10.He J, Jin Y, Chen Y, et al. Downregulation of ALDOB is associated with poor prognosis of patients with gastric cancer. Oncotargets Ther. 2016;9:6099–109. doi: 10.2147/OTT.S110203. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Holbrook JD, Parker JS, Gallagher KT, et al. Deep sequencing of gastric carcinoma reveals somatic mutations relevant to personalized medicine. J Transl Med. 2011;9:119. doi: 10.1186/1479-5876-9-119. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets – update. Nucleic Acids Res. 2013;41:991–95. doi: 10.1093/nar/gks1193. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Ashburner M, Ball CA, Blake JA, et al. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–29. doi: 10.1038/75556. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30. doi: 10.1093/nar/28.1.27. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Dennis G, Jr, Sherman BT, Hosack DA, et al. DAVID: Database for annotation, visualization and integrated discovery. Genome Biol. 2003;4:P3. [PubMed] [Google Scholar]
- 16.Szklarczyk D, Franceschini A, Wyder S, et al. STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;3:D447–52. doi: 10.1093/nar/gku1003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Shannon P, Markiel A, Ozier O, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504. doi: 10.1101/gr.1239303. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Tang Z, Li C, Kang B, et al. GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45:W98–102. doi: 10.1093/nar/gkx247. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Chandrashekar DS, Bashel B, Balasubramanya SAH, et al. UALCAN: A portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 2017;19:649–58. doi: 10.1016/j.neo.2017.05.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Zhang H, Liu J, Fu X, Yang A. Identification of key genes and pathways in tongue squamous cell carcinoma using bioinformatics analysis. Med Sci Monit. 2017;23:5924–32. doi: 10.12659/MSM.905035. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Gumuslu E, Cine N, Ertan Gökbayrak M, et al. Exenatide alters gene expression of neural cell adhesion molecule (NCAM), intercellular cell adhesion molecule (ICAM), and vascular cell adhesion molecule (VCAM) in the hippocampus of type 2 diabetic model mice. Med Sci Monit. 2016;22:2664–69. doi: 10.12659/MSM.897401. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Quan R, Ning Z, Wang Y, et al. Prognostic value of upregulation of myristoylated alanine-rich C-kinase substrate in gastric cancer. Med Sci Monit. 2019;25:279–87. doi: 10.12659/MSM.913558. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Chow CR, Ebine K, Knab LM, et al. Cancer cell invasion in three-dimensional collagen is regulated differentially by Gγ 13, protein and discoidin domain receptor 1-Par3 protein signaling. J Biol Chem. 2016;291(4):1605–18. doi: 10.1074/jbc.M115.669606. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Carbone L, Harris RA, Gnerre S. Gibbon genome and the fast karyotype evolution of small apes. Nature. 2014;513(7517):195–201. doi: 10.1038/nature13679. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Quan Wang JY. MiR-129-5p suppresses gastric cancer cell invasion and proliferation by inhibiting COL1A1. Biochem Cell Biol. 2018;96(1):19–25. doi: 10.1139/bcb-2016-0254. [DOI] [PubMed] [Google Scholar]
- 26.Ma H-P, Chang H-L, Bamodu OA, et al. Collagen 1A1 (COL1A1) is a reliable biomarker and putative therapeutic target for hepatocellular carcinogenesis and metastasis. Cancers (Basel) 2019;11(6):786. doi: 10.3390/cancers11060786. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Zou X, Feng B, Dong T, et al. Up-regulation of type I collagen during tumorigenesis of colorectal cancer revealed by quantitative proteomic analysis. J Proteomics. 2013;94(Complete):473–85. doi: 10.1016/j.jprot.2013.10.020. [DOI] [PubMed] [Google Scholar]
- 28.Liang Y, Diehn M, Bollen AW, et al. Type I collagen is overexpressed in medulloblastoma as a component of tumor microenvironment. J Neurooncol. 2018;86(2):133–41. doi: 10.1007/s11060-007-9457-5. [DOI] [PubMed] [Google Scholar]
- 29.Wang XQ, Tang ZX, Yu D, et al. Epithelial but not stromal expression of collagen alpha-1(iii) is a diagnostic and prognostic indicator of colorectal carcinoma. Oncotarget. 2016;7(8):8823–38. doi: 10.18632/oncotarget.6815. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Zhang Q-N, Zhu H-L, Xia M-T, et al. A panel of collagen genes are associated with prognosis of patients with gastric cancer and regulated by microRNA-29c-3p: An integrated bioinformatics analysis and experimental validation. Cancer Manag Res. 2019;11:4757–72. doi: 10.2147/CMAR.S198331. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Miyake M, Hori S, Morizawa Y, et al. Collagen type IV alpha 1 (COL4A1) and collagen type XIII alpha 1 (COL13A1) produced in cancer cells promote tumor budding at the invasion front in human urothelial carcinoma of the bladder. Oncotarget. 2017;8(22):36099–114. doi: 10.18632/oncotarget.16432. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Zhao X, Cai H, Wang X, Ma L. Discovery of signature genes in gastric cancer associated with prognosis. Neoplasma. 2016;63(2):239–45. doi: 10.4149/209_150531N303. [DOI] [PubMed] [Google Scholar]
- 33.Bryce LF, Jordan M, Alison M, et al. Beyond the matrix: The many non-ECM ligands for integrins. Int J Mol Sci. 2018;19(2) doi: 10.3390/ijms19020449. pii: E449. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Biddle A, Mackenzie IC. Cancer stem cells and EMT in carcinoma. Cancer Metastasis Rev. 2012;31(1–2):285–93. doi: 10.1007/s10555-012-9345-0. [DOI] [PubMed] [Google Scholar]
- 35.Rongkun L, Chun Z, Shuheng J, et al. ITGBL1 predicts a poor prognosis and correlates EMT phenotype in gastric cancer. J Cancer. 2017;8(18):3764–73. doi: 10.7150/jca.20900. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Bornstein P, Armstrong LC, Hankenson KD, et al. Thrombospondin 2, a matricellular protein with diverse functions. Matrix Biol. 2000;19(7):557–68. doi: 10.1016/s0945-053x(00)00104-9. [DOI] [PubMed] [Google Scholar]
- 37.Sun R, Wu J, Chen Y, et al. Downregulation of thrombospondin2 predicts poor prognosis in patients with gastric cancer. Mol Cancer. 2014;13(1):225–34. doi: 10.1186/1476-4598-13-225. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Song G, Xu S, Zhang H, et al. TIMP1 is a prognostic marker for the progression and metastasis of colon cancer through FAK-PI3K/AKT and MAPK pathway. J Exp Clin Cancer Res. 2016;35(1):148. doi: 10.1186/s13046-016-0427-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Wang Y-Y, Li L, Zhao Z-S, et al. Clinical utility of measuring expression levels of KAP1, TIMP1 and STC2 in peripheral blood of patients with gastric cancer. World J Surg Oncol. 2013;11:81. doi: 10.1186/1477-7819-11-81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Insua-Rodríguez J, Pein M, Hongu T, et al. Stress signaling in breast cancer cells induces matrix components that promote chemoresistant metastasis. EMBO Mol Med. 2018;10(10):e9003. doi: 10.15252/emmm.201809003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Li T, Gao X, Han L, et al. Identification of hub genes with prognostic values in gastric cancer by bioinformatics analysis. World J Surg Oncol. 2018;16(1):114–20. doi: 10.1186/s12957-018-1409-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Seeruttun SR, Cheung WY, Wang W, et al. Identification of molecular biomarkers for the diagnosis of gastric cancer and lymph-node metastasis. Gastroenterol Rep. 2019;7(1):57–66. doi: 10.1093/gastro/goy023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Heldin CH, Westermark B. Mechanism of action and in vivo role of platelet-derived growth factor. Physiol Rev. 1999;79:1283–316. doi: 10.1152/physrev.1999.79.4.1283. [DOI] [PubMed] [Google Scholar]
- 44.Chen J, Wang X, Hu B, et al. Candidate genes in gastric cancer identified by constructing a weighted gene co-expression network. Peer J. 2018;6:e4692. doi: 10.7717/peerj.4692. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Wang JX, Zhou JF, Huang FK, et al. Gli2 induces pdgfrb expression and modulates cancer stem cell properties of gastric cancer. Eur Rev Med Pharmacol Sci. 2017;21(17):3857–65. [PubMed] [Google Scholar]
- 46.Fogh BS, Multhaupt HAB, Couchman JR. Protein kinase C, focal adhesions and the regulation of cell migration. J Histochem Cytochem. 2014;62(3):172–84. doi: 10.1369/0022155413517701. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Bach CTT, Schevzov G, Bryce NS, et al. Tropomyosin isoform modulation of focal adhesion structure and cell migration. Cell Adh Migr. 2010;4(2):226–34. doi: 10.4161/cam.4.2.10888. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Lu P, Weaver VM, Werb Z. The extracellular matrix: A dynamic niche in cancer progression. J Cell Biol. 2012;196(4):395–406. doi: 10.1083/jcb.201102147. [DOI] [PMC free article] [PubMed] [Google Scholar]