Open Access

Identification of potential key genes in gastric cancer using bioinformatics analysis

  • Authors:
    • Wei Wang
    • Ying He
    • Qi Zhao
    • Xiaodong Zhao
    • Zhihong Li
  • View Affiliations

  • Published online on: February 20, 2020     https://doi.org/10.3892/br.2020.1281
  • Pages: 178-192
  • Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Gastric cancer (GC) is one of the most common types of cancer worldwide. Patients must be identified at an early stage of tumor progression for treatment to be effective. The aim of the present study was to identify potential biomarkers with diagnostic value in patients with GC. To examine potential therapeutic targets for GC, four Gene Expression Omnibus (GEO) datasets were downloaded and screened for differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were subsequently performed to study the function and pathway enrichment of the identified DEGs. A protein‑protein interaction (PPI) network was constructed. The CytoHubba plugin of Cytoscape was used to calculate the degree of connectivity of proteins in the PPI network, and the two genes with the highest degree of connectivity were selected for further analysis. Additionally, the two DEGs with the largest and smallest log Fold Change values were selected. These six key genes were further examined using Oncomine and the Kaplan‑Meier plotter platform. A total of 99 upregulated and 172 downregulated genes common to all four GEO datasets were screened. The DEGs were primarily enriched in the Biological Process terms: ‘extracellular matrix organization’, ‘collagen catabolic process’ and ‘cell adhesion’. These three KEGG pathways were significantly enriched in the categories: ‘ECM‑receptor interaction’, ‘protein digestion and absorption’, and ‘focal adhesion’. Based on Oncomine, expression of ATP4A and ATP4B were downregulated in GC, whereas expression of the other genes were all upregulated. The Kaplan‑Meier plotter platform confirmed that upregulated expression of the identified key genes was significantly associated with worse overall survival of patients with GC. The results of the present study suggest that FN1, COL1A1, INHBA and CST1 may be potential biomarkers and therapeutic targets for GC. Additional studies are required to explore the potential value of ATP4A and ATP4B in the treatment of GC.

Introduction

Gastric cancer (GC) is a malignant tumor that originates in the epithelium of the gastric mucosa and is one of the most common types of malignant tumors in the world (1). According to GLOBOCAN 2018, there were >1,000,000 new cases of GC and ~783,000 deaths in 2018, thus making it the cancer type with the fifth highest incidence rate and the third highest mortality in the world (2). The poor five-year survival rate of GC is primarily due the advanced stage of gastric tumors at the initial diagnosis in the majority of patients, and thus limits treatment opportunities (3). According to the Cancer Staging Manual, 8th edition, of the American Joint Committee on Cancer, only 30% of GC cases are diagnosed prior to metastasis, and the five-year survival for pathological Tumor-Node-Metastasis stage groups are between 68-80% for stage I, 46-60% for stage II, 8-30% for stage III and 5% for stage IV (4). Therefore, identifying potential biomarkers for patients with early GC is critical for improving patient outcomes.

In recent years, a variety of bioinformatics methods have contributed greatly to the discovery of biomarkers associated with tumor development, diagnosis and prognosis (5-8). The combined use of multiple databases of biological information for the analysis of cancer has also yielded certain breakthroughs. Yong et al (9) used Gene Expression Omnibus (GEO), Oncomine, Search Tool for Recurring Instances of Neighbouring Genes (STRING) and other databases for bioinformatic analysis, and concluded that PPP2CA may function as an oncogene and a prognostic biomarker or therapeutic target in the progression of colorectal cancer. Troiano et al (10) used the GEO database and Oncomine to examine the expression of BIRC5/Survivin in oral squamous cell carcinoma and showed that Survivin expression was upregulated compared with non-cancerous tissue. In addition, immunohistochemistry staining showed that cytoplasmic expression of Survivin was associated with poor overall survival in patients with oral squamous cell carcinoma. It may be beneficial to use multiple datasets and analysis tools to determine the potential mechanisms underlying development and progression of GC, and to identify potentially novel and specific diagnostic biomarkers for early detection of GC to improve the survival of patients.

In the present study, the expression profiles from four datasets (GSE13911, GSE19826, GSE54129 and GSE118916) in human GC and normal gastric tissue samples were obtained from the GEO database and analyzed to identify differentially expressed genes (DEGs). Gene Ontology (GO) and pathway enrichment analysis were performed to identify the biological functions and pathways of the DEGs. STRING and Cytoscape were used to construct a protein-protein interaction (PPI) network, and a total of six key genes were selected from the PPI network and DEGs. The value of the key genes was validated using the Oncomine and Kaplan-Meier platforms to further increase the reliability of the results and confirm the prognostic value of the key genes.

Materials and methods

Microarray data

The key word ‘gastric cancer’ was searched in the GEO database (ncbi.nlm.nih.gov/geo/), and a total of 9,224 datasets on human GC were retrieved. In the present study, four gene expression profiles from the GEO database were used, as they have not been studied together previously. The four datasets were: GSE13911(11), GSE19826(12), GSE54129 and GSE118916(13). Among these, GSE13911, GSE19826 and GSE54129 were based on the GPL570 platform [(HG-U133_Plus_2) Affymetrix Human Genome U133 Plus 2.0 Array]. GSE118916 was based on the GPL15207 platform [(PrimeView) Affymetrix Human Gene Expression Array].

Identification of DEGs

DEGs between GC samples and normal controls were identified using the GEO2R online analysis tool (ncbi.nlm.nih.gov/geo/geo2r); |log FC|≥1.0 and corrected P<0.05 were used as the cutoff criteria. The common DEGs of the four gene expression profiles were screened using Wayne analysis in Funrich (funrich.org/).

GO and KEGG enrichment analyses of DEGs

After obtaining the common DEGs, GO (14,15) and KEGG (16) analyses of the DEGs were performed using the Database for Annotation Visualization and Integrated Discovery (DAVID) online tool (17,18), with P<0.01 used as the threshold for significance. GO was used to identify the enrichment functions of three independent categories of genes; biological process (BP), cellular component (CC) and molecular function (MF). KEGG was used to search for the pathways associated with the identified genes (19). Only the top 10 BP, CC and MF terms, and the KEGG pathway with the smallest P-value were selected for further examination in the present study. The figures were generated using the OmicShare tools (omicshare.com/tools), a free online platform for data analysis.

PPI network construction

To explore the interaction between DEGs, the DEGs were analyzed using STRING (20) to generate a PPI network. PPI pairs with a combined score >0.4 were extracted, and disconnected nodes in the network were hidden. Subsequently, the PPI network was visualized using Cytoscape (21) and the degree of each protein node was calculated using the cytoHubba (22) plug-in in Cytoscape.

Identification of key genes

The two genes with the highest degree of connectivity in the PPI network, the two genes with the largest logFC values and the two genes with the smallest logFC among the shared DEGs were selected and considered key genes.

Analysis of key genes in Oncomine

The Oncomine database (oncomine.org/) was used to explore the mRNA expression differences of six key genes between GC and normal gastric tissue. Oncomine is a chip-based gene database and integrated data mining online cancer microarray database designed to facilitate the discovery of novel biomarkers from genome-wide expression analysis (23).

Survival analysis of key genes

The Kaplan-Meier plotter (24) is an online tool that can assess the effect of 54,000 genes on survival in 21 types of cancer. The largest datasets include breast (n=6,234), ovarian (n=2,190), lung (n=3,452) and gastric cancer (n=1,440) cancer. The primary purpose of the tool is to discover and validate biomarkers for survival. Online survival analysis of the selected key genes based on the GC database was performed using Kaplan-Meier Plotter. The hazard ratio (HR) with 95% confidence intervals (CIs) and log-rank P-values were calculated.

Results

Identification of DEGs

GSE13911 includes 38 GC samples and 31 normal samples, GSE19826 contains 12 GC samples and 15 normal samples, GSE54129 contains 111 GC samples and 21 normal samples, and GSE118916 contains 15 GC samples and 15 normal samples (Table I). In GSE13911, there are 26 intestinal, 4 mixed, 6 diffuse and 2 unclassified gastric carcinoma tissues, as well as 31 normal adjacent tissues. Unfortunately, information on the histological subtypes were not available in the other datasets. In the datasets, 1,001 upregulated and 2,304 downregulated DEGs were identified in GSE13911, 407 upregulated and 753 downregulated DEGs were identified in GSE19826, 1,852 upregulated and 2,083 downregulated DEGs were identified in GSE54129, and 977 upregulated and 903 downregulated DEGs were identified in GSE118916. Wayne analysis identified 99 common upregulated genes and 172 common downregulated genes were obtained from the 4 datasets (Table II; Fig. 1).

Table I

Information for four gene expression profiles from Gene Expression Omnibus.

Table I

Information for four gene expression profiles from Gene Expression Omnibus.

Dataset IDGastric cancerNormalTotal NumberPlatform
GSE13911383169GPL570
GSE19826121527GPL570
GSE5412911121132GPL570
GSE118916151530GPL15207

Table II

The differentially expressed genes identified from the four gene expression profiles, between gastric cancer and normal tissues.

Table II

The differentially expressed genes identified from the four gene expression profiles, between gastric cancer and normal tissues.

Differentially expressed genesGene terms
UpregulatedINHBA CST1 COL11A1 FAP COL10A1 FNDC1 COL8A1 SERPINH1 CDH3 THBS2 CLDN1 TNFRSF11B SPP1 COL1A2 SFRP4 SULF1 CPXM1 BMP1 MFAP2 COL1A1 CTHRC1 BGN RARRES1 IGF2BP3 THBS4 COL6A3 SRPX2 OSR2 HOXB7 TIMP1 ASPN THY1 FKBP10 PRRX1 SDS APOE PMEPA1 COL12A1 GPNMB FBN1 ADAM12 C3 APOC1 COL5A1 SPARC EPHB2 NID2 CMTM3 PLEKHO1 TNFRSF10B EHD2 FN1 MMP11 COCH AMIGO2 COL5A2 OLFML2B KLHL23 SPOCK1 CDH11 TWIST1 RAB31 SULF2 FGD6 VCAN ITGBL1 PCOLCE HAVCR2 THBS1 DNM1 IGFBP7 PLAU TMEM158 COL3A1 FLNA EDNRA LEF1 LIPG FZD2 GXYLT2 S100A10 LGALS1 NRP2 SIRPA ANTXR1 CD9 LIF COL4A2 TGM2 COL6A1 PDPN KCNJ8 ACTN1 GPR161 ZAK RCN3 BAG2 BHLHE40 COL4A1
DownregulatedATP4A ATP4B KCNE2 AQP4 GIF LIPF GKN1 GKN2 DPCR1 PGC SOSTDC1 ESRRG MUC6 SST FBP2 CPA2 VSIG1 CXCL17 PDIA2 CCKBR TMED6 CHGA TFF2 PSCA FUT9 CA9 SCNN1G GUCA2B C16orf89 SLC26A9 KLK11 CWH43 DNER PSAPL1 CNTN3 ALDH3A1 GATA5 SCGB2A1 UGT2B15 RDH12 CLIC6 NRG4 CLDN18 CAPN9 SLC16A7 SSTR1 FBXL13 TCN1 VSIG2 AKR1B10 B3GNT6 FOLR1 MUM1L1 CHGB MAL TRIM50 AKR7A3 KIAA1324 PAIP2B SULT2A1 PTPRZ1 ARX LIFR ALDH1A1 HYAL1 BEX5 CA2 CYP2C18 ME1 SCNN1B ADH7 GCNT2 ACER2 FMO5 HPGD RASSF6 TFF1 TMEM171 CA4 KCNJ16 LDHD KCNJ15 GABRB3 HOMER2 TMPRSS2 LYPD6B KLHDC7A ARHGAP42 PLAC8 IGFBP2 CAPN13 SYTL5 PDGFD RNASE1 RORC CYP2C9 EPN3 PBLD METTL7A ZBTB7C UBL3 SH3RF2 RNASE4 ARHGEF37 ALDH6A1 RAB27B SULT1B1 PKIB PXMP2 GPRC5C RIMBP2 ATP8A1 FAM20A PIGR GOLM1 CYP3A5 FAM46C C9orf152 COBLL1 FA2H SORBS2 DGKD SGK2 TMEM220 ANG PLLP MYCN C1orf116 FGD4 SLC41A2 ADAM28 MAGI1 GRAMD1C IQGAP2 GULP1 SYTL2 DHRS7 OASL RNF128 DBT ELL2 RAB27A NOSTRIN NEDD4L PPFIBP2 AKR1C3 PELI2 SMPD3 PTPRN2 RASEF TMEM92 ABCC5 GALNT12 LMO4 NTN4 TMEM116 ID4 ELOVL6 ALDOB EPB41L4B CD36 GALNT5 SH3BGRL2 MAGI3 MICALL1 HIPK2 MAOA WWC1 SLC7A8 CDC14B FAM107B SUCLG2

[i] Upregulated genes are listed from largest to smallest fold change values. Downregulated genes are listed from smallest to largest fold change values.

GO and KEGG pathway enrichment analyses of DEGs

GO and KEGG pathway enrichment analyses of the DEGs was performed using the online tool DAVID, and the results are presented in Table III. GO analysis showed that in BP, the DEGs were primarily enriched for the GO terms: ‘extracellular matrix organization’, ‘collagen catabolic process’, ‘cell adhesion’, ‘collagen fibril organization’ and ‘digestion’ (Table III; Fig. 2A). CC analysis revealed that the DEGs were significantly enriched for the terms: ‘extracellular space’, ‘extracellular matrix’, ‘extracellular exosome’, ‘extracellular region’ and ‘endoplasmic reticulum lumen’ (Table III; Fig. 2B). For MF, the DEGs were enriched for the GO terms: ‘platelet-derived growth factor binding’, ‘collagen binding’, ‘extracellular matrix binding’, ‘inward rectifier potassium channel activity’ and ‘SMAD binding’ (Table III; Fig. 2C). According to KEGG pathway analysis, the DEGs were primarily enriched for the pathway terms: ‘ECM-receptor interaction’, ‘protein digestion and absorption’, ‘focal adhesion’, ‘amoebiasis’ and ‘gastric acid secretion’ (Table III; Fig. 2D).

Table III

GO term and KEGG pathway enrichment analyses of the 271 differentially expressed genes.

Table III

GO term and KEGG pathway enrichment analyses of the 271 differentially expressed genes.

CategoryTermDescriptionCountP-Value
BP termGO:0030198Extracellular matrix organization23 1.28x10-13
BP termGO:0030574Collagen catabolic process14 7.06x10-12
BP termGO:0007155cell adhesion30 3.59x10-11
BP termGO:0030199Collagen fibril organization9 7.87x10-08
BP termGO:0007586Digestion10 3.19x10-07
BP termGO:0035987Endodermal cell differentiation7 2.13x10-06
BP termGO:0001501Skeletal system development11 3.42x10-05
BP termGO:0008202Steroid metabolic process7 3.60x10-05
BP termGO:0071230Cellular response to amino acid stimulus7 6.04x10-05
BP termGO:0006805Xenobiotic metabolic process8 1.45x10-04
BP termGO:0042060Wound healing8 1.70x10-04
BP termGO:0006081Cellular aldehyde metabolic process4 4.70x10-04
BP termGO:0030277Maintenance of gastrointestinal epithelium4 6.20x10-04
BP termGO:0010107Potassium ion import5 6.98x10-04
BP termGO:0007584Response to nutrient7 7.50x10-04
BP termGO:0002576Platelet degranulation8 7.99x10-04
BP termGO:0060021Palate development7 8.64x10-04
BP termGO:0010812Negative regulation of cell-substrate adhesion40.001003
BP termGO:0001503Ossification70.001131
BP termGO:0030168Platelet activation80.001523
BP termGO:0051216Cartilage development60.001703
BP termGO:0010628Positive regulation of gene expression120.001721
BP termGO:0001523Retinoid metabolic process60.001977
BP termGO:0016525Negative regulation of angiogenesis60.002125
BP termGO:0055114Oxidation-reduction process190.002857
BP termGO:0032964Collagen biosynthetic process30.003084
BP termGO:0008284Positive regulation of cell proliferation160.003752
BP termGO:0001649Osteoblast differentiation70.004274
BP termGO:0022617Extracellular matrix disassembly60.005144
BP termGO:0071711Basement membrane organization30.005647
BP termGO:0050891Multicellular organismal water homeostasis30.005647
BP termGO:0001525Angiogenesis100.005716
BP termGO:0042476Odontogenesis40.007007
BP termGO:0010575Positive regulation of vascular endothelial growth factor production40.007007
BP termGO:0050909Sensory perception of taste40.008568
BP termGO:0001937Negative regulation of endothelial cell proliferation40.008568
BP termGO:0040037Negative regulation of fibroblast growth factor receptor signaling pathway30.008901
BP termGO:0042572Retinol metabolic process40.009418
CC termGO:0005615Extracellular space63 9.65x10-17
CC termGO:0031012Extracellular matrix28 2.46x10-14
CC termGO:0070062Extracellular exosome87 1.68x10-12
CC termGO:0005576Extracellular region61 4.86x10-12
CC termGO:0005788Endoplasmic reticulum lumen20 4.73x10-11
CC termGO:0005581Collagen trimer15 5.56x10-11
CC termGO:0005604Basement membrane9 1.82x10-05
CC termGO:0005578Proteinaceous extracellular matrix22 3.57x10-10
CC termGO:0016324Apical plasma membrane16 2.29x10-05
CC termGO:0009986Cell surface20 3.51x10-04
CC termGO:0005887Integral component of plasma membrane340.004256
CC termGO:0005886Plasma membrane790.004569
CC termGO:0030141Secretory granule60.004319
CC termGO:0031093Platelet alpha granule lumen50.008125
CC termGO:0031090Organelle membrane60.008522
MF termGO:0048407Platelet-derived growth factor binding6 2.55x10-07
MF termGO:0005518Collagen binding8 2.37x10-05
MF termGO:0050840Extracellular matrix binding6 3.05x10-05
MF termGO:0005242Inward rectifier potassium channel activity40.002802
MF termGO:0046332SMAD binding50.003328
MF termGO:0005201Extracellular matrix structural constituent12 2.77x10-09
MF termGO:0001758Retinal dehydrogenase activity30.004132
MF termGO:0005178Integrin binding11 2.77x10-06
MF termGO:0005509Calcium ion binding27 1.47x10-05
MF termGO:0008201Heparin binding12 2.07x10-05
MF termGO:0016491Oxidoreductase activity90.008547
MF termGO:0008083Growth factor activity80.009105
KEGG pathwayhsa04512ECM-receptor interaction16 5.16x10-11
KEGG pathwayhsa04974Protein digestion and absorption14 7.73x10-09
KEGG pathwayhsa04510Focal adhesion18 2.67x10-07
KEGG pathwayhsa05146Amoebiasis10 1.63x10-04
KEGG pathwayhsa04971Gastric acid secretion8 4.23x10-04
KEGG pathwayhsa04151PI3K-Akt signaling pathway17 7.35x10-04
KEGG pathwayhsa00830Retinol metabolism70.00124
KEGG pathwayhsa00982Drug metabolism-cytochrome P45070.001703
KEGG pathwayhsa00980Metabolism of xenobiotics by cytochrome P45070.002628
KEGG pathwayhsa05204Chemical carcinogenesis70.003889

[i] GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological processes; CC, cellular component; MF, molecular function.

PPI network construction

Based on the STRING prediction results, a PPI network with 211 nodes and 741 sides was constructed in Cytoscape (Fig. 3), and the number of segments connected to each gene in the figure represents its degree.

Identification of six key genes

The two genes with the most nodes were FN1 and COL1A1. In the PPI network, FN1 was the most prominent, with the highest degree of connectivity at 52. The degree of connectivity of COL1A1 is 43 (Table IV). Expression of these two genes is upregulated in GC tissues. Additionally, of those DEGs shared among the four gene expression profiles, the two DEGs with the largest logFC and the two DEGs with the smallest logFC values were selected. The higher the logFC in the upregulated DEGs, the greater the increase in expression of the gene. Similarly, the lower the logFC values in the downregulated DEGs, the greater the decrease in expression of the gene. When sorting DEGs according to logFC, the logFC of GSE19826 was used as the standard, as chip GSE19826 represented a homogenous cancer tissue population at each Tumor-Node-Metastasis stage (25), which increases the accuracy of the expression profile (Table V). The two DEGs with the largest logFC values were INHBA (logFC=4.35) and CST1 (logFC=4.18) (Table VI). The two DEGs with the smallest logFC values were ATP4A (logFC=-6.46) and ATP4B (logFC=-5.91) (Table VII). Therefore, these six genes were selected as key genes.

Table IV

The 10 genes with the largest degree of connectivity in the protein-protein-interaction network.

Table IV

The 10 genes with the largest degree of connectivity in the protein-protein-interaction network.

RankGeneDegree
1FN152
2COL1A143
3COL1A238
4COL3A137
5FBN135
6BGN32
6COL5A232
8TIMP131
9SPARC30
10THBS228

Table V

The expression data from GSE19826 in gastric cancer.

Table V

The expression data from GSE19826 in gastric cancer.

Tissue typeAccession no.TitleStage
Noncancer tissueGSM495051CB2008210-1Nn/a
Gastric cancer tissueGSM495052CB2008210-1TII
Noncancer tissueGSM495053CB2008210-2Nn/a
Gastric cancer tissueGSM495054CB2008210-2TIV
Noncancer tissueGSM495055CB2008210-3Nn/a
Gastric cancer tissueGSM495056CB2008210-3TI
Noncancer tissueGSM495057CB2008210-4Nn/a
Gastric cancer tissueGSM495058CB2008210-4TII
Noncancer tissueGSM495059CB2008210-5Nn/a
Gastric cancer tissueGSM495060CB2008210-5TIII
Noncancer tissueGSM495061CB2008210-6Nn/a
Gastric cancer tissueGSM495062CB2008210-6TIV
Noncancer tissueGSM495063CB2008210-7Nn/a
Gastric cancer tissueGSM495064CB2008210-7TIV
Noncancer tissueGSM495065CB2008210-9Nn/a
Gastric cancer tissueGSM495066CB2008210-9TIII
Noncancer tissueGSM495067CB2008210-12Nn/a
Gastric cancer tissueGSM495068CB2008210-12TII
Noncancer tissueGSM495069CB2008210-13Nn/a
Gastric cancer tissueGSM495070CB2008210-13TI
Noncancer tissueGSM495071CB2008210-14Nn/a
Gastric cancer tissueGSM495072CB2008210-14TIII
Noncancer tissueGSM495073CB2008210-15Nn/a
Gastric cancer tissueGSM495074CB2008210-15TI
Normal gastric tissueGSM495075CB2008210-3Cn/a
Normal gastric tissueGSM495076CB2008210-5Cn/a
Normal gastric tissueGSM495077CB2008210-9Cn/a

Table VI

The 10 genes with the largest logFC values in GSE19826.

Table VI

The 10 genes with the largest logFC values in GSE19826.

RankNameLogFC
1INHBA4.35
2CST14.18
3COL11A14.11
4FAP3.91
5COL10A13.72
6FNDC13.27
6COL8A13.17
8SERPINH12.97
9CDH32.95
10THBS22.94

[i] FC, fold change.

Table VII

The 10 genes with the smallest logFC values in GSE19826.

Table VII

The 10 genes with the smallest logFC values in GSE19826.

RankNameLogFC
1ATP4A-6.46
2ATP4B-5.91
3KCNE2-5.88
4AQP4-5.81
5GIF-5.75
6LIPF-5.53
6CHIA-5.51
8GKN1-5.49
9GKN2-5.44
10DPCR1-4.83

[i] FC, fold change.

Analysis of the six key genes in Oncomine

The Oncomine database was used to confirm the expression of the six key genes in 20 different types of cancer. The results showed that there were statistically significant differences in their expression. In the Oncomine database, there were no studies showing low expression of FN1, COL1A1, INHBA or CST1 in GC, but there were six, eight, seven and four studies showing increased expression, respectively. For ATP4A and ATP4B, the reverse was observed with no studies showing high expression, but seven and six studies, respectively, showing decreased expression (Fig. 4).

After comparing the expression levels of these six genes in cancerous and normal gastric tissue, the expression levels of FN1, COL1A1, INHBA and CST1 in GC tissues were significantly higher compared with the control group, and the expression levels of ATP4A and ATP4B in GC tissues were significantly lower compared with the control group (Table VIII; Fig. 5).

Table VIII

Additional information for the six key genes shown in Figure 5.

Table VIII

Additional information for the six key genes shown in Figure 5.

Author, yearGeneNormal tissue samplesGastric cancer samplesP-valueFold ChangePublished journal(Refs.)
Chen et al, 2003FN1288 5.73x10-147.441Molecular Biology of The Cell(26)
Cui et al, 2011COL1A18080 1.81x10-153.201Nucleic Acids Research(28)
Cui et al, 2011INHBA8080 5.17x10-133.043Nucleic Acids Research(28)
Cho et al, 2011CST11931 3.17x10-1321.525Clinical Cancer Research(27)
Cho et al, 2011ATP4A1920 4.73x10-17-100.911Clinical Cancer Research(27)
D'Errico et al, 2009ATP4B3126 6.15x10-19-246.630European Journal of Cancer(11)

In addition, meta-analyses of the six key genes in GC in the Oncomine database also supported the findings that expression of FN1, COL1A1, INHBA and CST1 is upregulated in GC, whereas expression of ATP4A and ATP4B is downregulated in GC (11,12,26-28). The studies and references involved are shown in Fig. 6. In the meta-analyses, P=-0.000, FC≥2.0 and gene rank ≤300 were selected as the cutoff criteria.

Survival analysis of the six key genes

To identify the prognostic value of the six potential key genes, overall survival curves based on differential expression of the six key genes were plotted using Kaplan-Meier plotter (Fig. 7). There were 1,440 patients with GC on the Kaplan-Meier plotter platform who were suitable for the analysis of overall survival. The curves indicate that overexpression of the six key genes is significantly associated with decreased overall survival times of patients with GC. However, it is worth noting that ATP4A and ATP4B were significantly downregulated in GC samples in the present study.

Discussion

GC is a complex heterogeneous disease with high incidence and mortality rates, and poses a serious threat to afflicted patients. Therefore, it is important to identify biomarkers that are meaningful for both diagnostic and prognostic assessment (29).

In the present study, 271 DEGs were screened, including 99 upregulated and 172 downregulated genes, by analyzing four gene expression profiles containing a combined 176 GC tissue samples and 82 normal gastric tissue samples. Of the causes of cancer-associated deaths, 90% are the result of metastasis (30). In the present study, GO enrichment results showed that the occurrence and development of GC was closely associated with metastasis. GO analysis indicated that DEGs were primarily associated with extracellular matrix organization, collagen catabolic process and cell adhesion. Collagen is the primary component of the extracellular matrix and of the interstitial microenvironment. Collagen can provide a scaffold for tumor cell growth and induce migration of tumor cells (31,32). There is evidence that collagen synthesis increases in the presence of a gastric tumor (33). Zhou et al (32) reported that collagen components are quantitatively and qualitatively reorganized in the tumor microenvironment of GC, and collagen width was identified as a useful prognostic indicator for GC (32). In addition, studies have shown that changes in cell-cell adhesion and cell-matrix adhesion can promote cancer cell metastasis (34). MF analysis showed that the DEGs were significantly enriched in platelet-derived growth factor binding. It has been demonstrated that inhibition of platelet-derived growth factor receptor-a can reduce the proliferation of gastrointestinal stromal tumor cells with mutant v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog (KIT) by affecting the KIT-dependent transcription factor ETV1(35).

KEGG pathway analysis showed that the DEGs were primarily enriched in ECM-receptor interaction, protein digestion and absorption, and focal adhesion. ECM-receptor interaction serves a vital role in several types of cancer (36-38). The interaction between membrane receptors of tumor cells and ECM proteins serve an important role in tumor invasion and metastasis (39), and ECM-receptor interaction serve a crucial role in the process of tumor shedding, adhesion, degradation, movement and hyperplasia (38). In addition, the non-steroidal anti-inflammatory drug celecoxib may exhibit anti-GC effects by inhibiting the expression of various proteins and inhibiting leukocyte transendothelial migration and focal adhesion (40), which provides a possible mechanism for future investigations of the role of focal adhesion in GC and developing new anti-GC drugs.

The degree of connectivity of a gene in a PPI network reflects its association with GC. The greater the connectivity, the closer a gene is to the disease mechanism. The logFC values of DEGs reflects the level of up or downregulation of the gene. The higher the logFC values in the upregulated DEGs, the greater the degree of upregulation of the gene, and the lower the logFC values in the downregulated DEGs, the greater the degree of downregulation (41-43). Thus it was hypothesized that the DEGs with the highest and lowest logFC values would be the genes most closely associated with disease mechanisms.

In the present study, the two genes with the highest degree of connectivity in the PPI network, and the two DEGs with the largest and smallest logFC values, were all selected as key genes. These were FN1, COL1A1, INHBA, CST1, ATP4A and ATP4B. These six key genes were verified in the Oncomine database. Expression of FN1, COL1A1, INHBA and CST1 were upregulated in GC, and expression of ATP4A and ATP4B were downregulated, consistent with the results obtained from analysis of the GEO datasets. Furthermore, survival analysis showed that upregulation of the six key genes was significantly associated with worse overall survival, and downregulation of ATP4A and ATP4B expression predicted a more favorable prognosis for patients with GC, providing novel insights into potential GC treatment strategies.

FN1 was the gene with the highest degree of connectivity. It is expressed in a wide range of healthy plasmalemmas, lamina propria mucosae and smooth-muscle cell layers, and it is involved in a variety of cellular processes including embryogenesis, blood coagulation, wound healing, host defense and metastasis (44). As a glycoprotein involved in cell adhesion and migratory processes, FN1 is hypothesized to be associated with signaling pathways associated with cancer (13). Expression of FN1 is significantly increased in anti-chemotherapy osteosarcoma cell lines and tissues, and is associated with a poor prognosis (45). Knockdown of FN1 gene expression results in reduced cell proliferation, increased cellular senescence and apoptosis, and reduced migration and invasion, by blocking the activation of the PI3K/AKT signaling pathway (46). Furthermore, downregulation of FN1 inhibits proliferation, migration and invasion, and thus reduces progression of colorectal cancer (47). The results of the present study suggest that FN1 may be a potential biomarker and therapeutic target for diagnosis and treatment of GC, consistent with previous studies (13,48,49), and thus further confirming the significance of FN1 in GC.

COL1A1 is one of the most important components of the ECM, and it is highly expressed in most connective tissues and various human solid tumors (50). It is also the primary component of type I collagen, which serves a key role in tumor cell adhesion and invasion (51). A mechanistic study revealed that COL1A1 and COL1A2 affects angiogenesis in GC, and their expression is also significantly associated with progression of GC (52). In addition, Zhang et al (53) further confirmed that overexpression of COL1A1 promoted GC cell proliferation in vitro. These previous studies support the use of COL1A1 as a key potential GC biomarker in the present study.

INHBA is a member of the transforming growth factor-β (TGF-β) superfamily, which is closely associated with tumor proliferation and expression is upregulated in lung cancer (54), GC (12) and colon cancer (55), where INHBA expression is closely associated with their prognosis. In a study of GC, Chen et al (56) found that INHBA gene silencing reduced migration and invasion of GC cells by blocking the activation of the TGF-β signaling pathway. They suggested that INHBA was a potential target for GC therapy (56). Another study showed that INHBA mRNA expression in GC may be a useful prognostic biomarker for patients with stage II or III GC who receive adjuvant chemotherapy with S-1(57). The results of the present study support the conclusions drawn in these previous studies.

Cystatin SN (CST1) is a member of the type 2 cystatin superfamily, the primary role of which is to limit the proteolytic activity of cysteine proteases (58). The dysregulated expression of CST1 is hypothesized to be involved in several types of cancer, including cholangiocarcinoma (59), breast cancer (58), GC (60) and colorectal cancer (61). CST1 prevents cell aging and promotes cancer development by affecting the activity of cathepsin B (62). However, CST1 has not been analyzed using bioinformatics for survival prognosis in GC, to the best of our knowledge. Using multiple databases, the present study is the first to validate CST1 as a novel prognostic biomarker and a potential therapeutic target for treatment of GC.

ATP4A encodes the α subunit and ATP4B encodes the β subunit of the gastric H+, K+-ATPase, respectively. They regulate gastric acid secretion and, as a result, are targets for acid reduction (63). Fei et al (64) found that expression of ATP4A and ATP4B were significantly downregulated in patients with GC, but their expression was not significantly correlated with overall survival (64). In the present study, downregulation of ATP4A and ATP4B expression was associated with favorable overall survival in patients with GC. Downregulation of ATP4A and ATP4B mRNA expression in GC tissue is associated with the development of GC (65). Correa's Cascade is inversely associated with gastric acid secretion rate, the downregulation of ATP4A and ATP4B mRNA expression begins in the early stages of gastric mucosal lesions, and the expression of both is gradually decreased as Correa's cascade progresses (66). In addition, Helicobacter pylori (H. pylori) inhibits parietal acid secretion by downregulating the expression of ATP4A and ATP4B in gastric parietal cells prior to the formation of GC, suggesting that H. pylori is closely associated with the development of GC (67). Thus, it was hypothesized that ATP4A and ATP4B may inhibit the formation of GC. Survival analysis showed that ATP4A and ATP4B in GC are adverse prognostic factors for patients with GC, suggesting that upregulation is associated with progression of GC. However, studies have reported that the expression of ATP4A and ATP4B is not regulated by H. pylori in GC (68-70). Other studies have shown significant decreases in the abundance of Helicobacter and Neisseria, and significant increases in Achromobacter, Citrobacter, Phyllobacterium, Clostridium, Rhodococcus and Lactobacillus in gastric carcinoma in comparison with chronic gastritis (71,72). Additionally, the gastric microbiota composition in patients with gastric carcinoma is significantly different compared with patients with chronic gastritis (71). Therefore, it was hypothesized that the formation of an altered gastric microbiota composition may result in the expression of ATP4A and ATP4B to be passively upregulated as GC progresses. Further research is required to more accurately determine the biological function of ATP4A and ATP4B in GC.

Although several genes were identified as promising diagnostic and prognostic biomarkers for GC, the present study has the following limitations. First, the present study lacked experimental and clinical validation. Second, the possibility that different histological types may affect the accuracy of results cannot be eliminated. Thus, future bioinformatics analysis should be designed such that samples can be stratified by histological type. Finally, the sample size was relatively small for the RNA-Seq experiments, which may result in inaccuracies or results which are not completely representative of the wider populace. Therefore, it is necessary to use larger samples to perform bioinformatics analysis, and further experimental and clinical studies are required.

In conclusion, the present study used bioinformatics to analyze biological processes and signaling pathways closely associated with GC occurrence and development and identified FN1, COL1A1, INHBA and CST1 as promising diagnostic and prognostic biomarkers for GC patients. Additionally, the results of the survival analysis of ATP4A and ATP4B were inconsistent with other international studies. Therefore, further studies are required to assess the effects of ATP4A and ATP4B on GC initiation and development. Furthermore, experimental and clinical studies are required to validate the findings of the present study and determine the potential clinical value of these potential biomarkers.

Acknowledgements

Not applicable.

Funding

The present study was funded by the National Key R&D Program of China (grant nos. 2018YFC1704100 and 2018YFC1704106).

Availability of data and materials

The datasets used and/or analyzed during the present study are available from the corresponding author upon reasonable request.

Authors' contributions

WW and YH conceived of and designed the study. YH and QZ performed the bioinformatics analysis and analyzed the data. WW and QZ wrote the manuscript. WW and ZL revised the manuscript. XZ contributed to the design of the study and revised the manuscript. All authors read and approved the final 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.

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Wang W, He Y, Zhao Q, Zhao X and Li Z: Identification of potential key genes in gastric cancer using bioinformatics analysis. Biomed Rep 12: 178-192, 2020
APA
Wang, W., He, Y., Zhao, Q., Zhao, X., & Li, Z. (2020). Identification of potential key genes in gastric cancer using bioinformatics analysis. Biomedical Reports, 12, 178-192. https://doi.org/10.3892/br.2020.1281
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Wang, W., He, Y., Zhao, Q., Zhao, X., Li, Z."Identification of potential key genes in gastric cancer using bioinformatics analysis". Biomedical Reports 12.4 (2020): 178-192.
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Wang, W., He, Y., Zhao, Q., Zhao, X., Li, Z."Identification of potential key genes in gastric cancer using bioinformatics analysis". Biomedical Reports 12, no. 4 (2020): 178-192. https://doi.org/10.3892/br.2020.1281