Abstract

Purpose

   The current treatment methods available for advanced gastric cancer are
   not very promising. Hence, it is important to explore novel biomarkers
   and potential therapeutic agents to treat gastric cancer (GC). This
   study aimed to identify hub genes associated with GC prognosis and
   explore potential drugs for its treatment.

Materials and Methods

   Three gene expression data of GC and normal tissues were downloaded
   from the Gene Expression Omnibus (GEO) and processed to identify the
   differentially expressed genes (DEGs). We conducted a comprehensive
   analysis of DEGs, including functional enrichment analysis,
   construction of protein–protein interaction (PPI) network,
   identification of hub genes, survival analysis and expression
   verification of hub genes. Finally, we constructed the network of
   miRNA–mRNA, and predicted the drugs that might be effective for GC
   treatment.

Results

   A total of 340 DEGs, including 94 up-regulated and 246 down-regulated
   genes, were identified. Among the up-regulated DEGs, the enrichment
   terms were primarily related to tumorigenesis and tumor progression,
   extracellular matrix organization, and collagen catabolic process.
   Additionally, 10 hub genes (FN1, COL3A1, COL1A2, BGN, THBS2, COL5A2,
   THBS1, COL5A1, SPARC, and COL4A1) were identified, out of which 7 genes
   were significantly associated with poor overall survival (OS) in GC.
   The expression levels of these 7 hub genes were verified using
   real-time PCR, immunohistochemistry, and the GEPIA2 (Gene Expression
   Profiling Interactive Analysis) server. A regulatory network of
   miRNA–mRNA was also constructed, and the top 4 interactive miRNAs
   (hsa-miR-29b-3p, hsa-miR-140-3p, hsa-miR-29a-3p, and hsa-miR-29c-3p)
   that targeted the most hub genes were identified. Finally, fourteen
   small molecules were predicted to be effective in treating GC.

Conclusion

   The identification of the hub genes, miRNA–mRNA network, and potential
   candidate drugs associated with GC provides new insights into the
   molecular mechanisms and treatment of GC.

   Keywords: gastric cancer, prognosis, bioinformatics, biomarkers

Introduction

   Gastric cancer (GC) is one of the leading causes of death
   worldwide,[36]^1–3 with the highest incidence in East Asia, followed by
   eastern Europe.[37]^4 Early gastric cancer lacks specific symptoms,
   which leads to the fact that many gastric cancer patients are already
   in advanced stage when diagnosed.[38]^5 Heterogeneity of GC is also one
   of the factors leading to the low survival rate of patients with
   GC.[39]^6 Molecular diagnosis and targeted therapy are effective means
   to improve the early diagnosis rate of gastric cancer,[40]^7 and
   molecular diagnosis must be based on the search for specific tumor
   biomarkers.[41]^8

   With the completion of the Human Genome Project, cancer has
   increasingly been studied at the genetic level. DNA microarrays play an
   important role in identifying of genes leading to cancer and have the
   characteristics of high throughput, accuracy, sensitivity, and low
   cost. The technology is already being widely used in disease diagnosis
   and drug screening.[42]^9^,[43]^10

   In this study, three gene expression profiles of GC and normal tissues
   were downloaded from the Gene Expression Omnibus (GEO) and processed to
   identify the differentially expressed genes (DEGs). Then we conducted a
   comprehensive analysis of DEGs, including functional enrichment
   analysis, construction of protein–protein interaction (PPI) network,
   identification of hub genes, survival analysis and expression
   verification of hub genes. Finally, we constructed the network of
   miRNA-hub genes, and predicted the drugs that might be effective for GC
   treatment through the online database.

Materials and Methods

Acquisition of Data

   Three microarray datasets ([44]GSE54129, [45]GSE79973, and
   [46]GSE13911) containing GC and normal tissues were downloaded from GEO
   online database ([47]https://www.ncbi.nlm.nih.gov/gds/, accessed time:
   April 24, 2021) using GEOquery and preprocessed using affy R packages.
   All microarray datasets based on [48]GPL570 platform (Affymetrix Human
   Genome U133 Plus 2.0 Array) contained more than 10 samples.

Screening of DEGs

   The probe set IDs were converted into gene symbols using the annotation
   package hgu133plus2.db (3.2.3). If multiple probe sets corresponded to
   the same gene, the mean value of those probe sets expression was used.
   R software (version 4.0.3) was used to process all data using the Limma
   (3.46.0) package and Biobase (2.50.0).[49]^11^,[50]^12 Fold change was
   considered significant for P<0.05 and threshold values >1.0 or <−1.0.

GO and KEGG Enrichment Analysis of DEGs

   The Database for Annotation, Visualization and Integrated Discovery
   (DAVID) ([51]https://david.ncifcrf.gov/, version 6.8, accessed time:
   April 28, 2021) provides a comprehensive set of functional annotation
   tools for investigators to understand the biological significance of a
   list of genes. The results of functional enrichment analysis were
   visualized by by R software package ggplot2.[52]^13

PPI Network Construction and Module Analysis

   PPI network analysis was performed using the online tool Search Tool
   for the Retrieval of Interacting Genes/Proteins (STRING,
   [53]https://string-db.org/, accessed time: May 11, 2021), and
   visualized using Cytoscape software (version 3.8.2). Then several major
   modules of the network were analyzed using MCODE plug-in of Cytoscape
   software, and hub genes were identified using Cytoscape plugin
   cytoHubba.

Survival Analysis and Online Validation of Hub Gene Expression

   The Kaplan–Meier plots of the top 10 hub genes were drawn using the
   Kaplan-Meier plotter ([54]http://kmplot.com/analysis/, accessed time:
   May 14, 2021), and the survival prognosis forest map was visualized
   using the randomForest package.

   The expression levels of hub genes between GC and normal tissues were
   compared using Gene Expression Profiling Interactive Analysis (GEPIA2;
   [55]http://gepia2.cancer-pku.cn/, accessed time: May 15, 2021).
   Furthermore, the genetic mutation information for hub genes was
   explored using the online tool of cBio Cancer Genomics Portal
   ([56]https://www.cbioportal.org/, accessed time: May 16, 2021).

Real-Time PCR

   We collected GC and para-cancer tissues from thirteen patients who had
   been first diagnosed with GC at the Endoscopy Center of the First
   Affiliated Hospital of Guangxi Medical University from July to November
   2021. None of these patients had received any tumor-related treatment
   or had any other malignancy. Total RNA was isolated using TRIzol
   (R0016, Beyotime). Complementary DNA (cDNA) was synthesized using
   PrimeScript™ RT reagent Kit (RR047A, Takara). The qPCR reaction was set
   up using SYBR green PCR master mix containing 0.5 µL of cDNA. The
   quantitative real-time PCR analysis was performed using the real-time
   PCR system ABI 7500 Fast (Applied Biosystems). Data were normalized to
   GAPDH, and fold change in mRNA expression relative to controls was
   calculated using the 2^−ΔΔCt method.

Immunohistochemistry (IHC)

   We retrospectively studied the postoperative tumor tissues and normal
   tissues from 60 patients diagnosed with GC in The First People’s
   Hospital of Suqian from 2019 to 2020. The patients did not have any
   other known tumors, and no radiotherapy or chemotherapy had been
   performed before surgery. After removing paraffin, hydration, and
   sealing, the specimens were mixed with the antibodies anti-FN1 (#26836,
   Cell Signaling Technology), anti-COL1A2 (ab270994, Abcam), anti-BGN
   (ab209234, Abcam), anti-THBS2 (ab112543, Abcam), anti-COL5A1 (D264482,
   Sangong Biotech), anti-SPARC (ab225716, Abcam), and anti-COL4A1
   (D260202, Sangong Biotech), and incubated overnight at 4 °C (with
   dilution ratios of 1:500, 1:2000, 1:2000, 1:1000, 1:200, 1:100, and
   1:100, respectively). DAB (3, 3ʹ-diaminobenzidine) was used for signal
   detection. Additionally, we performed statistical analysis of images
   from immunohistology studies using Image-Pro Plus 6.0 software.
   Finally, we use GraphPad Prism 8 to visualize the statistical results.

Diagnostic Value of Hub Genes Based on TCGA Data

   We downloaded the RNA sequencing dataset of stomach adenocarcinoma
   (STAD) from The Cancer Genome Atlas (TCGA,
   [57]https://cancergenome.nih.gov, accessed time: May 17, 2021). We
   compared the expression of hub genes in tumor tissues and non-tumor
   tissues of GC patients to evaluate their diagnostic value in GC, and
   the ROC curve was visualized using the online tool Helix
   ([58]https://www.helixlife.cn/, accessed time: May 18, 2021).

Construction of miRNAs-mRNA Network

   The Encyclopedia of RNA Interactomes (ENCORI,
   [59]http://starbase.sysu.edu.cn/; version 3.0, accessed time: May 21,
   2021) is a public platform that identifies more than 2.5 million
   miRNA–mRNA interactions from multi-dimensional sequencing data. The
   target miRNAs of hub genes were screened based on the following
   criteria: SLIP-DATA ≥ 5, Degradome-Data ≥ 0, pan-Cancer ≥ 6, and
   programNum ≥ 3. Additionally, the miRNAs-hub genes network was
   visualized using Cytoscape software (version 3.8.2).

Drug-Hub Gene Interaction

   The DGIdb ([60]http://www.dgidb.org/search_interactions, accessed time:
   May 25, 2021) contains over 40,000 genes and 10,000 drugs involved in
   over 100,000 drug-gene interactions and belonging to one of the 42
   potentially druggable gene categories. The drugs with potential
   therapeutic effect on the hub genes of GC were screened by DGIdb.
   Additionally, we used the online tool STITCH
   ([61]http://stitch.embl.de/cgi/, accessed time: May 25, 2021) to
   construct an interaction network between hub genes and potential drugs.

Statistical Analyses

   We conducted the paired samples t-test to analyze mRNA expression and
   immunohistological positive rate of hub genes in tumor tissues and
   paracancerous non-tumor gastric tissues with IBM SPSS Statistics v.24
   software (IBM, Chicago, IL, USA). A value of P <0.05 was regarded as
   statistically significant.

Results

Screening of DEGs

   The details of the three datasets downloaded from GEO are presented in
   [62]Table 1. As shown in [63]Figure 1, a total of 340 DEGs were
   identified in the three microarray datasets, of which 246 were
   down-regulated ([64]Figure 1A) and 94 up-regulated ([65]Figure 1B). The
   gene names of these 340 DEGs are shown in [66]Table 2.

Table 1.

   Detailed Information on the GEO Microarray Profiles of GC Patients
   GEO Profile Type Source Case Control Platform Annotation Platform
   [67]GSE54129 mRNA Gastric cancer 111 21 GPL 570 Affymetrix Human Genome
   U133 Plus 2.0 Array
   [68]GSE79973 mRNA Gastric cancer 10 10 GPL 570 Affymetrix Human Genome
   U133 Plus 2.0 Array
   [69]GSE13911 mRNA Gastric cancer 38 31 GPL 570 Affymetrix Human Genome
   U133 Plus 2.0 Array
   [70]Open in a new tab

   Abbreviation: GEO, Gene Expression Omnibus.

Figure 1.

   [71]Figure 1
   [72]Open in a new tab

   The 340 DEGs identified from the three microarray datasets: (A) 246
   downregulated genes; (B) 94 up-regulated genes.

Table 2.

   Details of the 340 DEGs
   DEGs Gene Names
   Up-regulated (94) RCN3, GREM1, LAMA5, COL3A1, WASF1, OLFML2B, APOC1,
   TGM2, AMIGO2, COL4A2, DUSP14, SRPX2, HOXC9, PLPPR4, COL5A1, FXYD5,
   SULF2, LEF1, LGALS1, FAM83D, BAG2, NNMT, THY1, HOXB6, CDH11, KLHL23,
   CEBPB, CPXM1, GAL, CST4, DNM1, COL4A1, COL1A2, FJX1, CDH13, THBS1,
   ITGA5, PI15, TIMP1, SPHK1, CLEC5A, TNFAIP6, SNX10, SPOCK1, SPARC,
   HOXC10, CST2, PLA2G7, COL5A2, HOXB7, PRRX1, IL13RA2, PLAU, NT5DC2,
   FCGR1B, COL6A3, IGF2BP3, CRISPLD1, ASPN, AJUBA, NID2, RARRES1, TREM2,
   PMEPA1, COL12A1, SFRP4, NOX4, BGN, MMP11, TMEM158, CTHRC1, CLEC11A,
   FN1, HOXC6, MFAP2, SULF1, THBS2, FKBP10, CLDN1, CCN4, COL11A1, CXCL8,
   LY6E, FAP, COL8A1, SPP1, ADAMTS2, SERPINH1, CEMIP, COL10A1, CDH3, CST1,
   FNDC1, INHBA
   Downregulated (246) ATP4B, CBLIF, ATP4A, AQP4, GKN1, LIPF, ESRRG,
   SOSTDC1, LTF, KCNE2, TRIM50, GKN2, KCNJ16, CHGA, MFSD4A, CWH43, CCKBR,
   PGC, TMED6, MAP7D2, GC, CXCL17, MUC5AC, VSIG2, MUCL3, FBP2, FAM3B,
   CLIC6, KRT20, VSIG1, ADH1C, SCNN1B, AKR1B10, RFX6, IGH, RDH12, CPA2,
   SLC26A9, DNER, FCGBP, ALDH3A1, ABCA8, MAL, SULT2A1, KCNJ15, SSTR1,
   HPGD, GUCA2B, HHIP, TNFRSF17, CNTN3, NKX2-3, LINC02381, ADAM28, LIFR,
   ELAPOR1, ADTRP, CAPN9, B3GNT6, SLC28A2, CA2, AADAC, ADH7, AKR1C2,
   PIK3C2G, PSAPL1, GATA6-AS1, GCNT2, HAPLN4, FBXL13, PKIB, ALDOB, ADGRG2,
   CYP2C18, AKR1C1, SMIM5, SULT1C2, HEPACAM2, CAPN13, UBE2QL1, SMIM24,
   LYPD6B, SCIN, RNASE1, SPINK7, AXDND1, VSTM2A, MGAM, LDHD, MST1L,
   RPS6KA6, ENPP6, SPINK2, UGT2B15, ZBTB7C, PAIP2B, UNC5B-AS1, PBLD,
   CYP3A5, ATP2A3, SPTSSB, CYP2C9, BCAS1, CA9, B4GALNT3, CAPN8, AMPD1,
   AKR1C3, INSC, PRDM16, JCHAIN, TRPA1, GCKR, METTL7A, ALDH1A1, CLTRN,
   ITPKA, MRAP2, LINC01133, VILL, ASPA, NRG4, PDGFD, ACKR4, ADRB2,
   APOBEC1, GPAT3, RAB27A, PROM2, TMEM220, SMIM6, AKR7A3, TENT5C, TREH,
   LRRC31, TMEM238L, SLC14A1, FMO5, SLC9A1, CYP3A7-CYP3A51P, TMPRSS2,
   TMEM38A, SELENBP1, ARHGEF37, SLC41A2, ADAMTS15, PLAAT2, FER1L4, NQO1,
   CSTA, RAB37, SULT1B1, MYRF, RAB27B, SYTL2, PPP1R36, UPK1B, DGKD, NTN4,
   SIDT2, RASSF6, CYP4F12, ATP13A4, RNASE4, ADH1A, FA2H, OASL, DDX60,
   KBTBD12, SLC7A8, CCL28, SH3BGRL2, CYSTM1, STYK1, NOSTRIN, TMEM266,
   ACE2, ANKRD36BP2, PWWP3B, LRRC66, GRAMD1C, MAOA, TMEM171, EPB41L4B,
   PPFIBP2, NKX2-2, XK, ANG, SALL1, FAM174B, ETFDH, STS, MIOS-DT, PCAT18,
   STX19, KLF4, S100P, IQGAP2, HGD, EPN3, PER3, PLLP, OSBPL7, CLDN23,
   CIDEC, DHRS7, NR3C2, PELI2, CYP2C19, GOLGA2P10, ABCC5, PLCXD1,
   C1orf116, DISP1, TMEM116, GALE, LNX1, UBL3, PTPRH, SMPD3, GGT6,
   C4orf19, PAQR8, KLHDC7A, ARHGEF28, COBLL1, ENTPD5, HAPLN1, GPRC5C,
   RNF128, TEX9, DAPK2, SLC22A23, CDHR2, GPD1L, KLB, MAGI3, KCNK10, ABCA5,
   TMEM92, LOC101928881, CYP3A43, FBP1, PXMP2, PTGR1, EIF2AK3
   [73]Open in a new tab

GO and KEGG Enrichment Analysis of DEGs

   To better understand the biological functions of overlapping DEGs,
   their enriched biological processes (BP), cellular components (CC), and
   molecular functions (MF) were studied. For the BP category, the
   up-regulated DEGs were involved in the extracellular matrix
   organization, collagen catabolic process, collagen fibril organization,
   cell adhesion, and endodermal cell differentiation ([74]Figure 2A),
   whereas downregulated DEGs were significantly involved in steroid
   metabolic process, xenobiotic metabolic process, oxidation-reduction
   process, digestion, and daunorubicin metabolic process ([75]Figure 2E).
   For the CC category, the up-regulated DEGs correlated with the
   extracellular region, extracellular matrix, extracellular space,
   proteinaceous extracellular matrix, and endoplasmic reticulum lumen
   ([76]Figure 2B), whereas the downregulated DEGs were associated with
   extracellular exosome, organelle membrane, integral component of
   membrane, apical plasma membrane, and basolateral plasma membrane
   ([77]Figure 2F). For the MF category, the up-regulated DEGs were
   enriched in extracellular matrix structural constituent, extracellular
   matrix binding, integrin binding, platelet-derived growth factor
   binding, and calcium ion binding ([78]Figure 2C), whereas the
   downregulated DEGs were related to oxidoreductase activity, retinol
   dehydrogenase activity, monooxygenase activity, oxygen binding, and
   aromatase activity ([79]Figure 2G). For the KEGG pathway, the
   up-regulated DEGs were enriched in ECM-receptor interaction, focal
   adhesion, protein digestion and absorption, and the amoebiasis PI3K-Akt
   signaling pathway ([80]Figure 2D), and the down-regulated DEGs were
   enriched in chemical carcinogenesis, metabolism of xenobiotics by
   cytochrome P450, retinol metabolism, drug metabolism-cytochrome P450,
   and gastric acid secretion ([81]Figure 2H). The detail results of GO
   and KEGG pathway analysis of up-regulated and down-regulated DEGs are
   shown in [82]Tables 3 and [83]4, respectively.

Figure 2.

   [84]Figure 2
   [85]Open in a new tab

   The top 10 significant terms in GO analysis (Biological Process,
   Cellular Components, and Molecular Function) and KEGG enrichment
   analysis of DEGs. (A–D) Down-regulated DEGs; (E–H) Up-regulated DEGs.

   Abbreviations: GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and
   Genomes; DEGs, differentially expressed genes.

Table 3.

   GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes)
   Enrichment Analysis Results of Up-Regulated DEGs
   Category Term Count P value Genes
   GO_BP Extracellular matrix organization 20 4.17E-19 LAMA5, OLFML2B,
   SPARC, COL11A1, BGN, FN1, NID2, THBS1, COL3A1, COL1A2, COL4A2, COL5A1,
   COL4A1, COL5A2, MFAP2, SPP1, COL6A3, COL8A1, COL10A1, ITGA5
   GO_BP Collagen catabolic process 13 5.87E-16 COL11A1, COL12A1, MMP11,
   COL3A1, ADAMTS2, COL1A2, COL4A2, COL5A1, COL4A1, COL5A2, COL6A3,
   COL8A1, COL10A1
   GO_BP Collagen fibril organization 10 4.06E-13 GREM1, MMP11, COL3A1,
   ADAMTS2, COL1A2, COL5A1, COL11A1, COL12A1, COL5A2, SERPINH1
   GO_BP Cell adhesion 17 2.12E-09 TNFAIP6, COL12A1, FN1, THY1, THBS2,
   NID2, THBS1, CDH3, COL5A1, FAP, CDH11, SPP1, SPOCK1, COL6A3, COL8A1,
   CDH13, ITGA5
   GO_BP Endodermal cell differentiation 7 5.06E-09 COL4A2, COL11A1,
   COL12A1, FN1, COL8A1, INHBA, ITGA5
   GO_CC Extracellular region 37 1.27E-15 LAMA5, CEMIP, SPARC, OLFML2B,
   CXCL8, COL11A1, COL12A1, TREM2, THBS2, PLA2G7, NID2, THBS1, ADAMTS2,
   PLAU, SPP1, COL10A1, TIMP1, IL13RA2, CLEC11A, FN1, BGN, INHBA, SFRP4,
   MMP11, COL3A1, GAL, COL1A2, COL4A2, COL5A1, FNDC1, CRISPLD1, COL4A1,
   COL5A2, MFAP2, APOC1, COL6A3, COL8A1
   GO_CC Extracellular matrix 19 4.93E-15 LAMA5, COL12A1, BGN, FN1, THBS2,
   NID2, THBS1, ASPN, MMP11, COL3A1, LGALS1, COL1A2, COL4A2, COL5A1,
   COL4A1, COL5A2, COL6A3, COL8A1, TGM2
   GO_CC Extracellular space 32 1.11E-13 LAMA5, SPARC, CXCL8, TNFAIP6,
   COL12A1, THBS1, CST4, CST2, CST1, SRPX2, LGALS1, PLAU, SERPINH1, SPP1,
   SPOCK1, CPXM1, TIMP1, IL13RA2, CTHRC1, CLEC11A, FN1, SULF1, SULF2,
   GREM1, SFRP4, COL3A1, GAL, COL1A2, FAP, COL6A3, CDH13, FJX1
   GO_CC Proteinaceous extracellular matrix 17 2.48E-13 OLFML2B, SPARC,
   COL11A1, BGN, FN1, ASPN, MMP11, ADAMTS2, LGALS1, COL1A2, COL5A1,
   COL5A2, SPOCK1, COL6A3, COL10A1, TIMP1, CTHRC1
   GO_CC Endoplasmic reticulum lumen 15 5.78E-13 FKBP10, COL11A1, COL12A1,
   THBS1, RCN3, COL3A1, COL1A2, COL4A2, COL5A1, COL4A1, COL5A2, SERPINH1,
   COL6A3, COL8A1, COL10A1
   GO_MF Extracellular matrix structural constituent 8 4.72E-08 COL3A1,
   COL1A2, COL4A2, COL5A1, COL4A1, COL11A1, COL5A2, BGN
   GO_MF Extracellular matrix binding 6 1.84E-07 SPARC, OLFML2B, COL11A1,
   SPP1, BGN, THBS1
   GO_MF Integrin binding 8 1.06E-06 LAMA5, COL3A1, COL5A1, FAP, FN1,
   THY1, ITGA5, THBS1
   GO_MF Platelet-derived growth factor binding 4 2.05E-05 COL3A1, COL1A2,
   COL5A1, COL4A1
   GO_MF Calcium ion binding 14 6.53E-05 FKBP10, SPARC, SULF1, THBS2,
   NID2, THBS1, ASPN, SULF2, RCN3, MMP11, CDH3, CDH11, SPOCK1, CDH13
   KEGG ECM-receptor interaction 14 3.44E-16 LAMA5, COL11A1, FN1, THBS2,
   THBS1, COL3A1, COL1A2, COL4A2, COL5A1, COL4A1, COL5A2, SPP1, COL6A3,
   ITGA5
   KEGG Focal adhesion 14 2.99E-11 LAMA5, COL11A1, FN1, THBS2, THBS1,
   COL3A1, COL1A2, COL4A2, COL5A1, COL4A1, COL5A2, SPP1, COL6A3, ITGA5
   KEGG Protein digestion and absorption 10 7.28E-10 COL3A1, COL1A2,
   COL4A2, COL5A1, COL4A1, COL11A1, COL12A1, COL5A2, COL10A1, COL6A3
   KEGG Amoebiasis 10 3.91E-09 LAMA5, COL3A1, CXCL8, COL1A2, COL4A2,
   COL5A1, COL4A1, COL11A1, COL5A2, FN1
   KEGG PI3K-Akt signaling pathway 14 1.76E-08 LAMA5, COL11A1, FN1, THBS2,
   THBS1, COL3A1, COL1A2, COL4A2, COL5A1, COL4A1, COL5A2, SPP1, COL6A3,
   ITGA5
   [86]Open in a new tab

Table 4.

   GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes)
   Enrichment Analysis Results of Down-Regulated DEGs
   Category Term Count P value Genes
   GO_BP Steroid metabolic process 10 1.40E-09 SULT1B1, CYP2C9,
   CYP3A7-CYP3A51P, AKR1B10, UGT2B15, AKR1C3, AKR1C2, CYP2C19, CYP3A5,
   SULT2A1
   GO_BP Xenobiotic metabolic process 12 1.72E-09 NQO1, SULT1B1, CYP2C9,
   ALDH3A1, CYP3A7-CYP3A51P, AKR7A3, AADAC, AKR1C1, UGT2B15, CYP2C19,
   CYP3A5, CYP2C18
   GO_BP Oxidation-reduction process 26 2.99E-08 HPGD, MAOA, HHIP, ADH7,
   CYP2C19, CYP2C18, LIPF, AKR7A3, RDH12, LDHD, NQO1, HGD, AKR1C1,
   CYP3A43, AKR1C3, AKR1C2, PTGR1, CYP4F12, FMO5, CYP2C9, ALDH3A1, FA2H,
   DHRS7, AKR1B10, ALDH1A1, GPD1L
   GO_BP Digestion 10 4.80E-08 CAPN8, CAPN9, AKR1B10, CCKBR, AKR1C1, GKN1,
   AKR1C2, SSTR1, PGC, SULT2A1
   GO_BP Daunorubicin metabolic process 4 8.53E-05 AKR1B10, AKR1C1,
   AKR1C3, AKR1C2
   GO_CC Extracellular exosome 67 3.73E-08 CLIC6, FCGBP, TREH, JCHAIN,
   SULT1C2, UPK1B, UBL3, AKR7A3, CA2, FAM3B, SH3BGRL2, TMEM38A, ENPP6,
   BCAS1, HGD, PLLP, ENTPD5, LIFR, RNASE4, PTGR1, RNASE1, PGC, MUC5AC,
   EPN3, ACE2, C1ORF116, SLC7A8, SCNN1B, CDHR2, ALDH1A1, ANG, ALDOB, FBP1,
   ASPA, GPRC5C, FBP2, LTF, SMIM24, CSTA, HPGD, TMPRSS2, IQGAP2, SLC9A1,
   PBLD, SELENBP1, ADGRG2, SCIN, COBLL1, PDGFD, METTL7A, GC, PROM2, NQO1,
   MGAM, GGT6, AKR1C1, AKR1C3, GUCA2B, RAB27A, RAB27B, GALE, AKR1B10,
   S100P, CYSTM1, GPD1L, SLC26A9, CCL28
   GO_CC Organelle membrane 11 9.94E-08 FA2H, CYP2C9, CYP3A7-CYP3A51P,
   AADAC, CYP3A43, UGT2B15, CYP2C19, CYP4F12, CYP3A5, CYP2C18, FMO5
   GO_CC Integral component of membrane 93 1.38E-05 KLB, HEPACAM2, ATP2A3,
   KLHDC7A, AQP4, CWH43, TMEM92, TMEM220, SMPD3, TMEM266, UPK1B, STS,
   VSIG1, FAM174B, DNER, CLDN23, CA9, TMEM38A, TMED6, PAQR8,
   CYP3A7-CYP3A51P, KCNK10, PLLP, DAPK2, ABCC5, CYP3A43, UGT2B15, LIFR,
   TMEM171, DISP1, SLC22A23, ACE2, ALDH3A1, SLC7A8, RNF128, CCKBR, B3GNT6,
   NRG4, SCNN1B, MAL, ADTRP, VSTM2A, GPRC5C, SIDT2, GRAMD1C, SMIM24,
   SMIM5, KCNE2, MYRF, MAOA, SLC41A2, STX19, PELI2, ETFDH, TMPRSS2, ADRB2,
   SMIM6, CYP3A5, SLC9A1, MFSD4A, ADAM28, ADGRG2, GCNT2, TNFRSF17, SPTSSB,
   GPAT3, METTL7A, B4GALNT3, PROM2, SLC14A1, NQO1, MGAM, GGT6, FER1L4,
   TRPA1, ABCA5, STYK1, KCNJ15, KCNJ16, ABCA8, LRRC66, FMO5, CYP4F12,
   FA2H, ATP4A, XK, AADAC, TMEM116, PXMP2, ATP13A4, CYSTM1, SLC26A9, MRAP2
   GO_CC Apical plasma membrane 13 0.000236875 MGAM, RAB27A, RAB27B,
   ADRB2, CYP4F12, SLC9A1, UPK1B, ADGRG2, SCNN1B, CDHR2, MAL, SLC26A9,
   PROM2
   GO_CC Basolateral plasma membrane 10 0.000359953 SLC14A1, HPGD, SLC7A8,
   CA2, KCNJ16, CA9, AQP4, DISP1, SLC9A1, PROM2
   GO_MF Oxidoreductase activity 15 8.51E-08 HPGD, ADH1C, MAOA, AKR1C1,
   AKR1C3, ETFDH, AKR1C2, ADH7, PTGR1, CYP2C19, CYP3A5, CYP2C9, ALDH3A1,
   DHRS7, ALDH1A1
   GO_MF Retinol dehydrogenase activity 5 4.65E-05 RDH12, ADH1C, ADH1A,
   AKR1C3, ADH7
   GO_MF Monooxygenase activity 7 5.40E-05 CYP2C9, CYP3A7-CYP3A51P,
   CYP3A43, CYP2C19, CYP4F12, CYP3A5, CYP2C18
   GO_MF Oxygen binding 6 0.000202147 CYP2C9, CYP3A7-CYP3A51P, CYP2C19,
   CYP4F12, CYP3A5, CYP2C18
   GO_MF Aromatase activity 5 0.000246051 CYP3A7-CYP3A51P, CYP3A43,
   CYP4F12, CYP3A5, CYP2C18
   KEGG Chemical carcinogenesis 12 9.25E-09 CYP2C9, ALDH3A1,
   CYP3A7-CYP3A51P, ADH1C, ADH1A, CYP3A43, UGT2B15, ADH7, CYP2C19, CYP3A5,
   CYP2C18, SULT2A1
   KEGG Metabolism of xenobiotics by cytochrome P450 11 5.41E-08 CYP2C9,
   ALDH3A1, AKR7A3, ADH1C, ADH1A, AKR1C1, UGT2B15, AKR1C2, ADH7, CYP3A5,
   SULT2A1
   KEGG Retinol metabolism 10 1.83E-07 CYP2C9, CYP3A7-CYP3A51P, RDH12,
   ADH1C, ADH1A, UGT2B15, ALDH1A1, ADH7, CYP3A5, CYP2C18
   KEGG Drug metabolism-cytochrome P450 10 3.14E-07 CYP2C9, ALDH3A1,
   ADH1C, MAOA, ADH1A, UGT2B15, ADH7, CYP2C19, CYP3A5, FMO5
   KEGG Gastric acid secretion 9 6.32E-06 ATP4B, ATP4A, KCNE2, CCKBR,
   KCNK10, CA2, KCNJ15, KCNJ16, SLC9A1
   [87]Open in a new tab

PPI Network and Module Analysis

   The PPI network contained 321 nodes and 664 edges, and its PPI
   enrichment P-value was lower than 1.0e-16 ([88]Figure 3). In addition,
   an interaction network of 340 DEGs and their neighboring genes was also
   constructed ([89]Figure 4A). According to the ranking of MCODE scores,
   we selected the top three modules for visualization ([90]Figure 4B,
   module 1, MCODE score=16.111; [91]Figure 4C, module 2, MCODE
   score=4.167; and [92]Figure 4D, module 3, MCODE score=4). As shown in
   [93]Figure 4B, module 1 consisted of 19 nodes and 145 edges. KEGG
   enrichment analysis showed that genes in module 1 were mainly enriched
   in ECM-receptor interaction, focal adhesion, protein digestion and
   absorption, PI3K-Akt signaling pathway, and amoebiasis pathway
   ([94]Table 5). Module 2 consisted of 13 nodes and 25 edges ([95]Figure
   4C), with genes enriched in retinol metabolism, chemical
   carcinogenesis, metabolism of xenobiotics by cytochrome P450, steroid
   hormone biosynthesis, and drug metabolism-cytochrome P450 pathway
   ([96]Table 5). Module 3 contained 4 nodes and 6 edges ([97]Figure 4D),
   with no gene enriched in any pathway ([98]Table 5). The PPI enrichment
   P-value of each module was lower than 0.05.

Figure 3.

   [99]Figure 3
   [100]Open in a new tab

   A PPI network of the 340 DEGs in GC. The red represents up-regulated
   genes, and the blue represents down-regulated genes.

   Abbreviations: PPI, protein–protein interaction; DEGs, differentially
   expressed genes; GC, gastric cancer.

Figure 4.

   [101]Figure 4
   [102]Open in a new tab

   (A) Interaction network of the 340 DEGs and their neighboring genes.
   Red represents selected genes, and greed represents interacting genes.
   (B–D) The top 3 significant clusters of modules.

   Abbreviations: PPI, protein–protein interaction; DEGs, differentially
   expressed genes.

Table 5.

   GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes)
   Enrichment Analysis of Each Module
   Category Term Count P value Genes
   Module 1
   GO_BP Extracellular matrix organization 16 4.67E-27 SPARC, COL11A1,
   BGN, FN1, NID2, THBS1, COL3A1, COL1A2, COL4A2, COL5A1, COL4A1, COL5A2,
   COL6A3, COL8A1, COL10A1, ITGA5
   GO_BP Collagen catabolic process 11 1.32E-20 COL3A1, COL1A2, COL4A2,
   COL5A1, COL4A1, COL11A1, COL12A1, COL5A2, COL10A1, COL6A3, COL8A1
   GO_BP Collagen fibril organization 7 1.91E-12 COL3A1, COL1A2, COL5A1,
   COL11A1, COL12A1, COL5A2, SERPINH1
   GO_BP Endodermal cell differentiation 6 6.13E-11 COL4A2, COL11A1,
   COL12A1, FN1, COL8A1, ITGA5
   GO_BP Cell adhesion 9 1.01E-08 COL5A1, COL12A1, FN1, COL6A3, COL8A1,
   ITGA5, THBS2, THBS1, NID2
   GO_CC Endoplasmic reticulum lumen 13 2.31E-20 COL11A1, COL12A1, THBS1,
   COL3A1, COL1A2, COL4A2, COL5A1, COL4A1, COL5A2, SERPINH1, COL6A3,
   COL8A1, COL10A1
   GO_CC Extracellular matrix 14 3.33E-20 COL12A1, BGN, FN1, THBS2, NID2,
   THBS1, COL3A1, COL1A2, COL4A2, COL5A1, COL4A1, COL5A2, COL6A3, COL8A1
   GO_CC Extracellular region 17 1.64E-15 SPARC, COL11A1, COL12A1, BGN,
   FN1, THBS2, NID2, THBS1, COL3A1, COL1A2, COL4A2, COL5A1, COL4A1,
   COL5A2, COL6A3, COL8A1, COL10A1
   GO_CC Collagen trimer 9 1.30E-14 COL3A1, COL1A2, COL5A1, COL11A1,
   COL12A1, COL5A2, SERPINH1, COL10A1, COL6A3
   GO_CC Proteinaceous extracellular matrix 9 7.59E-11 SPARC, COL1A2,
   COL5A1, COL11A1, COL5A2, BGN, FN1, COL10A1, COL6A3
   GO_MF Extracellular matrix structural constituent 8 3.45E-13 COL3A1,
   COL1A2, COL4A2, COL5A1, COL4A1, COL11A1, COL5A2, BGN
   GO_MF Platelet-derived growth factor binding 4 1.67E-07 COL3A1, COL1A2,
   COL5A1, COL4A1
   GO_MF Extracellular matrix binding 4 2.61E-06 SPARC, COL11A1, BGN,
   THBS1
   GO_MF Integrin binding 5 4.04E-06 COL3A1, COL5A1, FN1, ITGA5, THBS1
   GO_MF Collagen binding 4 3.35E-05 SPARC, SERPINH1, FN1, NID2
   KEGG ECM-receptor interaction 12 5.27E-20 COL3A1, COL1A2, COL4A2,
   COL5A1, COL4A1, COL11A1, COL5A2, FN1, COL6A3, ITGA5, THBS2, THBS1
   KEGG Focal adhesion 12 9.83E-16 COL3A1, COL1A2, COL4A2, COL5A1, COL4A1,
   COL11A1, COL5A2, FN1, COL6A3, ITGA5, THBS2, THBS1
   KEGG Protein digestion and absorption 10 4.12E-15 COL3A1, COL1A2,
   COL4A2, COL5A1, COL4A1, COL11A1, COL12A1, COL5A2, COL10A1, COL6A3
   KEGG PI3K-Akt signaling pathway 12 3.09E-13 COL3A1, COL1A2, COL4A2,
   COL5A1, COL4A1, COL11A1, COL5A2, FN1, COL6A3, ITGA5, THBS2, THBS1
   KEGG Amoebiasis 8 2.69E-10 COL3A1, COL1A2, COL4A2, COL5A1, COL4A1,
   COL11A1, COL5A2, FN1
   Module 2
   GO_BP Xenobiotic metabolic process 7 3.74E-12 CYP2C9, ALDH3A1, AKR1C1,
   UGT2B15, CYP3A5, CYP2C18, CYP3A7
   GO_BP Steroid metabolic process 4 2.54E-06 CYP2C9, UGT2B15, CYP3A5,
   CYP3A7
   GO_BP Oxidation-reduction process 5 0.000414041 CYP2C9, ALDH3A1,
   AKR1C1, ALDH1A1, CYP2C18
   GO_BP Drug catabolic process 2 0.003924595 CYP2C9, CYP3A5
   GO_BP Lipid hydroxylation 2 0.003924595 CYP3A5, CYP3A7
   GO_CC Organelle membrane 5 1.56E-07 CYP2C9, UGT2B15, CYP3A5, CYP2C18,
   CYP3A7
   GO_CC Endoplasmic reticulum membrane 5 0.001255792 CYP2C9, UGT2B15,
   CYP3A5, CYP2C18, CYP3A7
   GO_CC Intracellular membrane-bounded organelle 4 0.003920426 CYP2C9,
   ALDH3A1, UGT2B15, CYP3A5
   GO_CC Extracellular space 4 0.042447579 ALDH3A1, CXCL8, PLAU, ANG
   GO_MF Oxygen binding 4 3.29E-06 CYP2C9, CYP3A5, CYP2C18, CYP3A7
   GO_MF Oxidoreductase activity 5 5.91E-06 CYP2C9, ALDH3A1, AKR1C1,
   ALDH1A1, CYP3A5
   GO_MF Monooxygenase activity 4 6.23E-06 CYP2C9, CYP3A5, CYP2C18, CYP3A7
   GO_MF Heme binding 4 8.23E-05 CYP2C9, CYP3A5, CYP2C18, CYP3A7
   GO_MF Iron ion binding 4 0.000114202 CYP2C9, CYP3A5, CYP2C18, CYP3A7
   KEGG Retinol metabolism 6 1.45E-08 CYP2C9, UGT2B15, ALDH1A1, CYP3A5,
   CYP2C18, CYP3A7
   KEGG Chemical carcinogenesis 6 4.52E-08 CYP2C9, ALDH3A1, UGT2B15,
   CYP3A5, CYP2C18, CYP3A7
   KEGG Metabolism of xenobiotics by cytochrome P450 5 2.47E-06 CYP2C9,
   ALDH3A1, AKR1C1, UGT2B15, CYP3A5
   KEGG Steroid hormone biosynthesis 4 6.55E-05 AKR1C1, UGT2B15, CYP3A5,
   CYP3A7
   KEGG Drug metabolism-cytochrome P450 4 0.000105515 CYP2C9, ALDH3A1,
   UGT2B15, CYP3A5
   KEGG Metabolic pathways 7 0.003353172 CYP2C9, ALDH3A1, UGT2B15,
   ALDH1A1, CYP3A5, CYP2C18, CYP3A7
   Module 3
   GO_BP Anterior/posterior pattern specification 4 1.04E-07 HOXC9, HOXB7,
   HOXB6, HOXC6
   GO_BP Embryonic skeletal system morphogenesis 3 1.57E-05 HOXC9, HOXB7,
   HOXB6
   GO_BP Transcription, DNA-templated 4 0.001575955 HOXC9, HOXB7, HOXB6,
   HOXC6
   GO_BP Regulation of transcription, DNA-templated 3 0.022617461 HOXC9,
   HOXB7, HOXB6
   GO_CC Nucleus 4 0.026223769 HOXC9, HOXB7, HOXB6, HOXC6
   GO_MF Sequence-specific DNA binding 4 2.87E-05 HOXC9, HOXB7, HOXB6,
   HOXC6
   GO_MF Transcription factor activity, sequence-specific DNA binding 3
   0.009344922 HOXB7, HOXB6, HOXC6
   [103]Open in a new tab

   Ten up-regulated genes (FN1, COL3A1, COL1A2, BGN, THBS2, COL5A2, THBS1,
   COL5A1, SPARC, and COL4A1) were identified as core genes based on the
   ranking of scores ([104]Table 6). Furthermore, we also constructed a
   PPI network of hub genes with 10 nodes and 44 edges ([105]Figure 5A)
   and interaction network between hub genes and their related genes
   ([106]Figure 5B). The clustering coefficient of the PPI network was
   0.978, and PPI enrichment P-value was lower than 1.0e-16.

Table 6.

   Top 10 Hub Genes in the PPI Network Ranked by the Degree Method
   Rank  Name  Score
   1    FN1    42
   2    COL3A1 32
   3    COL1A2 31
   4    BGN    27
   5    THBS2  26
   5    COL5A2 26
   7    THBS1  24
   7    COL5A1 24
   9    SPARC  23
   10   COL4A1 22
   [107]Open in a new tab

Figure 5.

   [108]Figure 5
   [109]Open in a new tab

   (A) The interaction network of hub genes and their related genes. Red
   represents selected genes, and greed represents interacting genes. (B)
   PPI network of the hub genes constructed via the STRING online
   database.

   Abbreviations: PPI, protein–protein Interaction; STRING, Search Tool
   for the Retrieval of Interacting Genes/Proteins.

Survival Analysis and Online Validation of Hub Gene Expression

   As can be seen from the forest map ([110]Figure 6A) and survival curve
   ([111]Figure 6B–K) we constructed, among the 10 hub genes, 7 genes
   (FN1, COL1A2, BGN, THBS2, COL5A1, SPARC, COL4A1) were significantly
   associated with the OS of GC patients ([112]Figure 6). High expression
   levels of all hub genes except COL3A1, COL5A2, and THBS1, were
   significantly related to poor OS in GC patients.

Figure 6.

   [113]Figure 6
   [114]Open in a new tab

   (A) A forest map of the 10 hub genes in all stages. 7 hub genes (FN1,
   COL1A2, BGN, THBS2, COL5A1, SPARC, and COL4A1) were significantly
   associated with the OS of GC patients. (B–K) The Kaplan-Meier survival
   curves of 10 hub genes.

   Abbreviations: OS, overall survival; GC, gastric cancer.

   Subsequently, genetic alteration information of the 10 hub genes was
   determined via cBioPortal, as illustrated in [115]Figure 7. The genes
   COL1A2 and COL4A1 were altered most often (14% and 12%, respectively).
   These alterations included amplifications, missense mutations, deep
   deletions, truncating mutations, inframe mutations, and splice-site
   mutations ([116]Figure 7A). Gene amplification is the most common of
   all types of genetic alterations ([117]Figure 7B).

Figure 7.

   [118]Figure 7
   [119]Open in a new tab

   (A) Genetic alteration information of the 10 hub genes. (B) Alterations
   in the 10 hub genes from datasets of GC in the TCGA database.

   Abbreviations: GC, gastric cancer; TCGA, The Cancer Genome Atlas.

   Additionally, we verified the relative expression levels of hub genes
   using gene expression profiles of GC and normal tissues in TCGA and
   GTEx. As shown in [120]Figure 8A–J, except THBS1, the expression levels
   of other hub genes were significantly up-regulated in GC compared with
   normal tissues (P < 0.05). The expression of THBS1 in GC tissues was
   up-regulated, but the difference was not statistically significant (P >
   0.05). We further analyzed the expression levels of hub genes in
   different stages of GC ([121]Figure 9). There were significant
   variations in the expression levels of BGN [Pr (>F) < 0.001]
   ([122]Figure 9A), COL1A2 [Pr (>F) = 0.004] ([123]Figure 9B), COL3A1 [Pr
   (>F) = 0.001] ([124]Figure 9C), COL5A1 [Pr (>F) = 0.012] ([125]Figure
   9E), COL5A2 [Pr (>F) = 0.049] ([126]Figure 9F), SPARC [Pr (>F) = 0.015]
   ([127]Figure 9H), THBS1 [Pr (>F) = 0.011] ([128]Figure 9I), and THBS2
   [Pr (>F) = 0.025] ([129]Figure 9J) in GC patients from different
   stages. These results strongly suggest that the hub genes identified
   hub genes may play an important role in the GC progression.

Figure 8.

   [130]Figure 8
   [131]Open in a new tab

   Comparison of expression levels of 10 hub genes in gastric cancer and
   normal tissues using the GEPIA2 database. We use red for gastric cancer
   tissues and gray for normal tissues. (A–J) respectively shows the BGN,
   COL1A2 COL3A1, COL4AI, COL5A1, COL5A2, FN1, SPARC, THBS1 and THBS2
   expression level. Except for THBS1, the expression levels of the other
   nine hub genes in gastric cancer are significantly higher than those in
   normal tissues.

   Abbreviation: GEPIA2, Gene Expression Profiling Interactive Analysis 2.

Figure 9.

   [132]Figure 9
   [133]Open in a new tab

   A comparison of expression levels of the 10 hub genes in different
   stages of GC. (A–J) respectively shows the BGN, COL1A2 COL3A1, COL4AI,
   COL5A1, COL5A2, FN1, SPARC, THBS1 and THBS2 expression level. There
   were significant variations in the expression levels of BGN (A), COL1A2
   (B), COL3A1 (C), COL5A1 (E), COL5A2 (F), SPARC (H), THBS1 (I), and
   THBS2 (J) in GC patients from different stages.

   Abbreviation: GC, gastric cancer.

Real-Time PCR and Immunohistochemistry

   As shown in [134]Figure 10A–N, immunohistochemical staining revealed
   that the expression of core genes in GC tissue was significantly higher
   than that in normal tissue. Statistical analysis confirmed that the
   relative expression levels in tumor tissues of mRNA of these 7 hub
   genes were significantly higher than in normal tissues ([135]Figure
   10O), and the average optical density (AOD) value of
   immunohistochemical staining was also significantly higher in tumor
   tissues ([136]Figure 10P). The primer sequences used are shown in
   [137]Table 7.

Figure 10.

   [138]Figure 10
   [139]Open in a new tab

   The results of real-time PCR and IHC. (A–N) IHC images of the 7 core
   genes in GC tissues and normal tissues under a 400 X microscope. (O)
   The relative hub gene mRNA levels in normal and GC tissues. (P) The AOD
   of hub genes expression. **P < 0.01; ***P < 0.001.

   Abbreviations: GC, gastric cancer; IHC, immunohistochemistry; AOD,
   average optical density.

Table 7.

   Primer Sequences Used in PCR Experiments
     Gene          Sequences
   FN1-F    GGAACAAGCATGTCTCTCTGC
   FN1-R    TGCTTGGAGAAGCTGTGAGT
   COL1A2-F ACTGTAAGAAAGGGCCCAGC
   COL1A2-R AGCAAAGTTCCCACCGAGAC
   BGN-F    TGACTGGCATCCCCAAAGAC
   BGN-R    GAGTAGCGAAGCAGGTCCTC
   THBS2-F  ATCACACGCATCCGTCTCTG
   THBS2-R  TAAACAGCCATTTGGGCATGG
   COL5A1-F TAGTCAGTACAAGCGGGGGTC
   COL5A1-R GTCATCTGTACCACACCCACA
   SPARC-F  GCTTCGGCATCAAGCAGAGATA
   SPARC-R  CATTGGGGGAAACACGAAGG
   COL4A1-F GGACCCCCGGGAGAAATAGG
   COL4A1-R GATTTGAAAAAGCAATGGCACTCC
   GAPDH-F  GTCAGCCGCATCTTCTTT
   GAPDH-R  CGCCCAATACGACCAAAT
   [140]Open in a new tab

Diagnostic Value of Hub Genes Based on TCGA Data

   A total of 348 GC patients were enrolled in the project. RNA sequencing
   data was available for 343 tumor tissue samples and 30 non-tumor tissue
   samples from 348 patients. We selected 7 hub genes with significant
   differences in survival analysis for diagnostic value evaluation. The
   ROC (receiver operating characteristic) curve ([141]Figure 11) showed
   that these 7 hub genes had a high diagnostic value for GC: FN1 (AUC =
   0.724, CI = 0.616–0.831), COL1A2 (AUC = 0.874, CI = 0.831–0.917), BGN
   (AUC = 0.949, CI = 0.925–0.973), THBS2 (AUC = 0.866, CI = 0.816–0.916),
   COL5A1 (AUC = 0.808, CI = 0.744–0.873), SPARC (AUC = 0.879, CI =
   0.835–0.924), and COL4A1 (AUC = 0.861, CI = 0.783–0.939).

Figure 11.

   [142]Figure 11
   [143]Open in a new tab

   ROC curves of the 7 hub genes in the diagnosis of GC. Different colored
   curves represent the different core genes.

   Abbreviations: ROC, receiver operating characteristic; GC, gastric
   cancer.

miRNA–mRNA Network

   As illustrated in [144]Figure 12, the miRNA-hub gene interaction
   network consists of 10 hub genes and 293 miRNAs. The four interactive
   hub genes that most miRNAs would target were THBS1 (degree score = 95),
   THBS2 (degree score = 81), COL4A1 (degree score = 42), and COL5A1
   (degree score = 31). These were followed by COL1A2 (degree score = 30),
   SPARC (degree score = 21), COL3A1 (degree score = 11), BGN (degree
   score = 11), FN1 (degree score = 8), and COL5A2 (degree score = 7).
   Furthermore, hsa-miR-29b-3p (degree score = 6), hsa-miR-140-3p (degree,
   score = 5), hsa-miR-29a-3p (degree, score = 5), and hsa-miR-29c-3p
   (degree, score = 5) were the top four interactive miRNAs that targeted
   the most hub genes ([145]Figure 12). Additionally, the detailed
   relationship between miRNAs and hub genes is shown in [146]Table 8.

Figure 12.

   [147]Figure 12
   [148]Open in a new tab

   The interaction network between hub genes and target miRNAs. Hub genes
   are presented in yellow circles, whereas target miRNAs are shown in
   green circles. The interaction between hub genes and their related
   miRNAs is shown in the form of arrows.

Table 8.

   The Respective miRNAs Targeting the 10 Hub Genes
   Gene mi-RNA
   BGN hsa-miR-6758-5p, hsa-miR-6856-5p, hsa-miR-3125, hsa-miR-6859-5p,
   hsa-miR-4476, hsa-miR-3916, hsa-miR-4533, hsa-miR-6876-5p,
   hsa-miR-6828-5p, hsa-miR-8485, hsa-miR-4311
   COL1A2 hsa-miR-4271, hsa-miR-6090, hsa-miR-4747-5p, hsa-miR-5196-5p,
   hsa-miR-4653-3p, hsa-miR-4424, hsa-miR-4781-5p, hsa-miR-6780b-5p,
   hsa-miR-4725-3p, hsa-miR-7111-5p, hsa-miR-4525, hsa-miR-6870-5p,
   hsa-miR-5698, hsa-miR-4723-5p, hsa-miR-625-5p, hsa-miR-1275,
   hsa-miR-569, hsa-miR-5010-5p, hsa-miR-6803-5p, hsa-miR-6751-5p,
   hsa-miR-7109-5p, hsa-miR-4665-5p, hsa-miR-4533, hsa-miR-26b-5p,
   hsa-miR-29c-3p, hsa-miR-1343-5p, hsa-miR-939-5p, hsa-miR-29a-3p,
   hsa-let-7 g-5p, hsa-miR-25-3p
   COL3A1 hsa-miR-767-5p, hsa-miR-335-5p, hsa-miR-29c-3p, hsa-let-7b-5p,
   hsa-miR-143-3p, hsa-miR-455-3p, hsa-miR-29a-3p, hsa-miR-29a-5p,
   hsa-let-7d-5p, hsa-miR-29b-1-5p, hsa-miR-29b-3p
   COL4A1 hsa-miR-218-5p, hsa-miR-124-3p, hsa-miR-767-5p, hsa-miR-3185,
   hsa-miR-542-3p, hsa-miR-4504, hsa-miR-5682, hsa-miR-6808-3p,
   hsa-miR-495-3p, hsa-miR-5688, hsa-miR-6871-3p, hsa-miR-562,
   hsa-miR-4716-5p, hsa-miR-21-5p, hsa-miR-7-1-3p, hsa-miR-7-2-3p,
   hsa-miR-125a-5p, hsa-miR-335-3p, hsa-miR-4318, hsa-miR-4693-5p,
   hsa-miR-7159-5p, hsa-miR-505-3p, hsa-miR-421, hsa-miR-8070,
   hsa-miR-498, hsa-miR-1305, hsa-miR-892a, hsa-miR-4272, hsa-miR-4709-5p,
   hsa-miR-29c-3p, hsa-miR-758-3p, hsa-miR-193b-3p, hsa-miR-18a-3p,
   hsa-miR-637, hsa-miR-769-3p, hsa-miR-16-5p, hsa-miR-92a-3p,
   hsa-miR-99a-5p, hsa-miR-100-5p, hsa-miR-29a-3p, hsa-miR-140-3p,
   hsa-miR-29b-3p
   COL5A1 hsa-miR-3689a-3p, hsa-miR-30b-3p, hsa-miR-1273 h-5p,
   hsa-miR-6778-5p, hsa-miR-1233-5p, hsa-miR-6799-5p, hsa-miR-6883-5p,
   hsa-miR-6785-5p, hsa-miR-4728-5p, hsa-miR-6760-5p, hsa-miR-149-3p,
   hsa-miR-6878-5p, hsa-miR-7106-5p, hsa-miR-5703, hsa-miR-4516,
   hsa-miR-4434, hsa-miR-3934-5p, hsa-miR-764, hsa-miR-6780a-5p,
   hsa-miR-6779-5p, hsa-miR-3689c, hsa-miR-3689b-3p, hsa-miR-543,
   hsa-miR-26b-5p, hsa-miR-143-3p, hsa-miR-145-5p, hsa-miR-125a-3p,
   hsa-miR-6825-5p, hsa-miR-744-5p, hsa-miR-29b-1-5p, hsa-miR-29b-3p
   COL5A2 hsa-miR-767-5p, hsa-miR-21-5p, hsa-miR-222-3p, hsa-miR-29c-3p,
   hsa-miR-143-3p, hsa-miR-29a-3p, hsa-miR-29b-3p
   FN1 hsa-miR-218-5p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-26b-5p,
   hsa-miR-1-3p, hsa-miR-615-3p, hsa-let-7 g-5p, hsa-miR-140-3p
   SPARC hsa-miR-432-5p, hsa-miR-767-5p, hsa-miR-10a-5p, hsa-miR-192-5p,
   hsa-miR-29c-3p, hsa-miR-297, hsa-miR-675-3p, hsa-miR-3149,
   hsa-miR-2681-3p, hsa-miR-646, hsa-miR-4710, hsa-miR-4792,
   hsa-miR-7855-5p, hsa-miR-3653-3p, hsa-miR-4693-3p, hsa-miR-591,
   hsa-miR-29a-3p, hsa-miR-29a-5p, hsa-miR-433-5p, hsa-miR-211-5p,
   hsa-miR-29b-3p
   THBS1 hsa-miR-3163, hsa-miR-548c-3p, hsa-miR-132-3p, hsa-miR-182-5p,
   hsa-miR-27b-3p, hsa-miR-155-5p, hsa-miR-19a-3p, hsa-miR-17-5p,
   hsa-miR-20a-5p, hsa-let-7a-5p, hsa-miR-98-5p, hsa-miR-3065-5p,
   hsa-miR-380-3p, hsa-miR-5003-5p, hsa-miR-202-3p, hsa-miR-567,
   hsa-miR-5096, hsa-miR-4500, hsa-let-7i-5p, hsa-let-7e-5p,
   hsa-miR-379-3p, hsa-miR-411-3p, hsa-miR-4458, hsa-let-7c-5p,
   hsa-let-7f-5p, hsa-miR-1252-3p, hsa-miR-7-1-3p, hsa-miR-7-2-3p,
   hsa-miR-4495, hsa-miR-3529-3p, hsa-miR-30a-5p, hsa-miR-30a-3p,
   hsa-miR-1-3p, hsa-let-7b-5p, hsa-miR-297, hsa-miR-675-3p, hsa-miR-3149,
   hsa-miR-92a-3p, hsa-miR-410-3p, hsa-miR-648, hsa-miR-6733-3p,
   hsa-miR-190a-3p, hsa-miR-4528, hsa-miR-4302, hsa-miR-4276,
   hsa-miR-4418, hsa-miR-509-5p, hsa-miR-509-3-5p, hsa-miR-211-3p,
   hsa-miR-4756-5p, hsa-miR-4739, hsa-miR-1321, hsa-miR-125a-3p,
   hsa-miR-6754-5p, hsa-miR-4441, hsa-miR-4270, hsa-miR-3665,
   hsa-miR-1205, hsa-miR-1273 g-3p, hsa-miR-6756-5p, hsa-miR-4450,
   hsa-miR-766-5p, hsa-miR-657, hsa-miR-198, hsa-miR-92a-2-5p,
   hsa-miR-6766-5p, hsa-miR-19b-3p, hsa-miR-3609, hsa-miR-6857-5p,
   hsa-miR-491-5p, hsa-miR-5680, hsa-miR-4637, hsa-miR-8084,
   hsa-miR-3121-3p, hsa-miR-1827, hsa-miR-377-5p, hsa-miR-4537,
   hsa-miR-6499-3p, hsa-miR-3612, hsa-miR-6086, hsa-miR-2467-3p,
   hsa-miR-455-3p, hsa-miR-650, hsa-miR-6825-5p, hsa-miR-6516-5p,
   hsa-miR-4655-5p, hsa-miR-4257, hsa-miR-6777-5p, hsa-miR-1910-3p,
   hsa-miR-6511a-5p, hsa-miR-6889-5p, hsa-let-7d-5p, hsa-let-7 g-5p,
   hsa-miR-487b-3p, hsa-miR-320c
   THBS2 hsa-miR-27b-3p, hsa-miR-4719, hsa-miR-6814-5p, hsa-miR-662,
   hsa-miR-6793-3p, hsa-miR-4281, hsa-miR-133a-3p, hsa-miR-365a-3p,
   hsa-miR-365b-3p, hsa-miR-105-5p, hsa-miR-7853-5p, hsa-miR-6839-5p,
   hsa-miR-3183, hsa-miR-4667-3p, hsa-miR-6756-3p, hsa-miR-1470,
   hsa-miR-361-3p, hsa-miR-3127-3p, hsa-miR-378j, hsa-miR-6769b-3p,
   hsa-miR-4469, hsa-miR-4723-3p, hsa-miR-3191-5p, hsa-miR-1236-3p,
   hsa-miR-6826-3p, hsa-miR-6824-3p, hsa-miR-6515-3p, hsa-miR-518c-5p,
   hsa-miR-6764-3p, hsa-miR-4782-5p, hsa-miR-5706, hsa-miR-6767-5p,
   hsa-miR-6798-5p, hsa-miR-510-5p, hsa-miR-670-3p, hsa-miR-1343-5p,
   hsa-miR-4771, hsa-miR-4518, hsa-miR-1266-5p, hsa-miR-6762-5p,
   hsa-miR-939-5p, hsa-miR-6845-5p, hsa-miR-3922-5p, hsa-miR-4326,
   hsa-miR-3193, hsa-miR-1228-3p, hsa-miR-5572, hsa-miR-4491,
   hsa-miR-129-2-3p, hsa-miR-129-1-3p, hsa-miR-938, hsa-miR-299-5p,
   hsa-miR-4657, hsa-miR-6812-5p, hsa-miR-6737-5p, hsa-miR-4536-5p,
   hsa-miR-193b-5p, hsa-miR-6855-5p, hsa-miR-3170, hsa-miR-6819-5p,
   hsa-miR-6895-3p, hsa-miR-6873-3p, hsa-miR-593-3p, hsa-miR-6844,
   hsa-miR-4753-3p, hsa-miR-4768-5p, hsa-miR-6818-3p, hsa-miR-6809-3p,
   hsa-miR-6833-3p, hsa-miR-1307-3p, hsa-miR-6741-3p, hsa-miR-6845-3p,
   hsa-miR-130b-5p, hsa-miR-6817-3p, hsa-miR-3615, hsa-miR-1908-5p,
   hsa-miR-744-5p, hsa-miR-6787-5p, hsa-miR-663a, hsa-miR-135b-5p,
   hsa-miR-29b-3p
   [149]Open in a new tab

Drug-Gene Interaction Analysis

   A total of 14 drugs related to 8 genes were selected, and the
   relationships between them were visualized through Cytoscape
   ([150]Figure 13). Based on the total score on the website, the top ten
   drugs with higher scores were selected. The results are shown in
   [151]Table 9.

Figure 13.

   [152]Figure 13
   [153]Open in a new tab

   The 14 predicted drugs associated with the hub genes. Red represents
   hub genes and blue represents potential drugs.

Table 9.

   Top 10 Drugs by Score Ranking in DGIdb
   Gene Drug Interaction Type and Directionality Interaction Score
   FN1 L19IL2 n/a 15.95
   FN1 L19TNFA n/a 15.95
   FN1 L19SIP 131I n/a 15.95
   THBS1 METHYLCELLULOSE n/a 7.97
   THBS2 BEVACIZUMAB n/a 3.87
   COL3A1 COLLAGENASE CLOSTRIDIUM HISTOLYTICUM n/a 2.28
   COL1A2 COLLAGENASE CLOSTRIDIUM HISTOLYTICUM n/a 2.28
   THBS1 VESNARINONE n/a 2.28
   THBS1 ABT-510 n/a 1.99
   FN1 OCRIPLASMIN Cleavage (inhibitory) 1.29
   [154]Open in a new tab

Discussion

   Gastric Cancer remains the fifth most frequently diagnosed neoplasm and
   the third most deadly tumor in the world.[155]^14 With the rapid
   developments in molecular biology, leading to novel treatments, the
   current survival rate of GC patients has improved. However, there are
   certain limitations to treatment, and the outcome of patients is not
   always promising. Therefore, it is important to discover novel
   biomarkers and better understand the underlying molecular mechanisms to
   enable earlier diagnosis, treatment, and a better prognosis for GC
   patients. In recent years, with the rapid developments in the field of
   bioinformatics, more and more microarray and sequencing data can be
   obtained from online platforms, which provides a feasible way to
   discover new tumor-related diagnostic and therapeutic biomarkers.

   In this study, three datasets for GC and normal tissue were downloaded
   from the GEO online database, and the differentially expressed genes
   were analyzed. A total of 340 common DEGs were identified, including 94
   up-regulated and 246 downregulated genes. Subsequently, functional
   enrichment analysis of the DEGs was performed using the DAVID online
   tool. The top 3 significant terms from the GO enrichment analysis
   showed that the up-regulated DEGs were involved in the extracellular
   matrix organization, collagen catabolic process, and cell adhesion in
   the BP category. It is interesting to note that about 90% percent of
   tumor-related deaths are due to metastasis.[156]^15 Collagen is the
   main component of the extracellular matrix and interstitial
   microenvironment. It provides a scaffold for the growth of tumor cells
   and induces their migration.[157]^16 Jang et al have reported that
   collagen synthesis increases significantly in GC tissue.[158]^17
   Additionally, changes in cell-cell adhesion and cell-matrix adhesion
   promote the chances of metastasis of the tumor cell.[159]^18 Collagen
   components are qualitatively and quantitatively reorganized in the
   tumor microenvironment. Collagen width has been identified as a useful
   prognostic indicator for GC.[160]^19 For KEGG pathway enrichment
   analysis, the up-regulated DEGs were enriched in ECM-receptor
   interaction, focal adhesion, and protein digestion and absorption. The
   structural integrity of ECM is crucial to maintain the normal and
   healthy activity of cells.[161]^20 Several studies have identified that
   the ECM plays an important role in various biological processes
   associated with tumor development, such as escaping apoptosis, cell
   growth regulation, tumor angiogenesis, invasion of cells, and
   metastasis.[162]^21–24 Thus, studying the remodeling of ECM that occurs
   in GC patients could lead to the discovery of potential therapeutic
   targets.

   The degree of connectivity of a gene in a PPI network reflects its
   association with GC. In this study, a PPI network of 340 DEGs was
   constructed that included 321 nodes and 664 edges, and 10 hub genes
   were identified based on the highest degrees of connectivity.
   Subsequently, survival analysis of these hub genes was done, and it
   demonstrated that 7 hub genes (FN1, COL1A2, BGN, THBS2, COL5A1, SPARC,
   COL4A1) were significantly associated with the OS of GC patients. Then,
   the expression levels of these hub genes in tumor and normal tissues
   were compared through the GEPIA2 database. The results were consistent
   with those from the GEO database. PCR and IHC also confirmed that,
   compared with normal tissues, the expression levels of all hub genes
   were significantly higher in GC tissue. It is reasonable to hypothesize
   that these hub genes play an important role in the occurrence and
   development of GC. The prognostic value of hub genes was also evaluated
   based on data from TCGA. The ROC curve showed that 7 hub genes had a
   high diagnostic value for GC. Although a large number of studies have
   already shown that these hub genes are associated with the diagnosis
   and prognosis of GC, their underlying molecular mechanisms have not yet
   been fully clarified.

   The SPARC gene encodes a cysteine-rich acidic matrix-associated protein
   which is required for collagen formation and is also involved in
   extracellular matrix synthesis and promotion of changes in cell
   shape.[163]^25 Previous studies have shown that the SPARC protein is
   associated with a variety of cancers, including GC;[164]^26 It may
   affect GC metastasis by regulating changes in the tumor
   microenvironment.[165]^27 These findings support the accuracy of our
   bioinformatics analyses to a certain extent.

   The FN1 gene encodes fibronectin, a glycoprotein found as either a
   dimer or a polymer on the cell surface and in the extracellular matrix.
   Studies have shown that FN1 is associated with a variety of tumor types
   and that its expression is higher in tumor tissues.[166]^28–30 The FN1
   protein probably interacts with vascular endothelial growth factor A
   and plays an important role in the development of non-small cell lung
   cancer (NSCLC). Hence, it can be used as a diagnostic or therapeutic
   target for NSCLC patients.[167]^31 However, no relevant studies on FN1
   have been done in GC patients to date. Our study shows that the
   downregulation of FN1 is associated with better overall survival in GC
   patients. Furthermore, we identified 8 potential miRNAs
   (hsa-miR-218-5p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-26b-5p,
   hsa-miR-1-3p, hsa-miR-615-3p, hsa-let-7 g-5p, and hsa-miR-140-3p) that
   might be targets for FN1.

   The THBS1 gene encodes thromboplastin 1, which is involved in
   angiogenesis and tumor progression, and its increased expression
   significantly correlates with tumor differentiation.[168]^32 Elevated
   expression of THBS1 protein has been associated with tumor growth and
   lymph node metastasis in GC patients.[169]^33 Interestingly, in this
   study, Kaplan-Meier’s plots showed that THBS1 overexpression was not an
   adverse prognostic factor for overall survival in patients with GC.
   Therefore, further studies on the effect of THBS1 on GC are needed.

   Thromboplastin 2 (THBS2) is a member of the thromboplastin family of
   proteins that mediates the cell-cell and cell-matrix interactions.
   Overexpression of THBS2 has been associated with poor survival in
   patients with lung adenocarcinoma.[170]^34 THBS2 expression was also
   significantly correlate with the TNM stage, AJCC stage, and clinical
   outcome (P < 0.05). It may also be an important prognostic indicator of
   colorectal cancer.[171]^35 Previous studies have found that the
   expressions of TGF-β and THBS2 in GC cells are in a time-dependent
   relationship with the survival of patients,[172]^36 suggesting that
   THBS2 may affect PI3K by up-regulating the TGF-β signaling pathway.

   BGN is a class I gene of the small-leucine-rich-proteoglycan (SLRP)
   family and encodes a leucine repeat-rich protein with a molecular
   weight of 42 kDa. BGN has been reported to be up-regulated in several
   cancer types and associated with tumor progression and worsening
   patient prognosis,[173]^37–39 which is consistent with our findings. As
   a potential new prognostic biomarker for GC, BGN can be easily
   translated into the clinical setting and provides an opportunity to
   develop new therapies.

   The COL family of genes is involved in the interactions of the
   ECM-receptor and local adhesion pathway, which play an important role
   in the invasion and metastasis of tumors. Collagen, laminin, and
   fibronectin are major components of the extracellular matrix, which
   provides the matrix for cancer cell growth, survival, and
   migration.[174]^40 Studies have shown that the COL1A2 mRNA levels are
   related to the histological types and lymph node status of GC, and may
   be involved in the invasion, metastasis, and carcinogenesis of
   GC.[175]^41^,[176]^42 All of these previous findings support the
   accuracy of our results. In addition, the expression of COL5A2 is also
   closely related to the occurrence and development of colon
   cancer.[177]^43 Overexpression of COL3A1 has been confirmed in a
   variety of cancers, such as bladder cancer,[178]^44^,[179]^45 but the
   effect of its expression levels in GC is not fully understood. COL5A1
   has been reported to be closely associated with breast cancer,[180]^46
   lung adenocarcinoma,[181]^47 oral squamous cell carcinoma,[182]^48 and
   GC.[183]^49 High expression of COL4A1 was found to be associated with
   an advanced tumor stage and poor overall survival in patients with
   hepatocellular carcinoma.[184]^50 Furthermore, in breast cancer cells,
   COL4A1 knockout resulted in reduced cell viability and cell cycle
   arrest.[185]^51 In this study, we found that COL1A2, COL3A1, COL4A1,
   COL5A1, and COL5A2 were significantly overexpressed in GC tissue, which
   is consistent with the TCGA results, indicating that the COL family of
   genes could make good biomarker panels for GC diagnosis.

   With the help of the DGIdb database, a total of 14 drugs with potential
   therapeutic effects on GC were identified. The top 10 drugs among
   these, based on the website’s total score, were L19IL2, L19TNFA, L19SIP
   131I, methylcellulose, bevacizumab, collagenase clostridium
   histolyticum, vesnarinone, ABT-510, and ocriplasmin. These drugs were
   mainly related to genes FN1, COL3A1, COL1A2, THBS2, COL5A2, THBS1,
   COL5A1, and COL4A1. The ability of the L19 antibody to preferentially
   target tumor sites has been widely demonstrated using nuclear medicine
   procedures with the preparation of radiologically labeled antibodies.
   L19IL2 is a recombinant fusion protein, containing the L19 antibody in
   diabody format fused to human IL2.[186]^52 In a study of the treatment
   of advanced metastatic melanoma, it was found that the use of L19IL2 in
   combination with dacarbazine (DTIC) significantly improved the overall
   response rates and median progression-free survival, as compared to the
   use of DTIC alone.[187]^53 The use of radioimmunotherapy with
   131I-L19SIP has been reported to induce a sustained partial response in
   patients with relapsed Hodgkin’s lymphoma.[188]^54 L19-TNFα/L19-IL2
   combination therapy could eradicate tumors in myeloma mice through
   TNFα-induced tumor necrosis and L19-TNFα/L19-IL2-mediated local
   cellular immune response.[189]^55 Surfactant-free solubilization of
   docetaxel is an efficient method for insoluble drug delivery to enhance
   the antitumor effect.[190]^56 Bevacizumab is expected to remain a key
   agent in cancer therapy, both due to its established efficacy in
   approved indications and its promise as a partner in novel targeted
   combination treatments.[191]^57 Combined bevacizumab can significantly
   reduce the risk of progression of advanced tumors and improve the
   overall survival rate in cases of metastatic colorectal cancer,[192]^58
   non-small cell lung cancer,[193]^59 and renal cell carcinoma.[194]^60
   Vesnarinone is a unique anti-proliferating, differentiation-inducing,
   and apoptosis-inducing drug against several human malignancies,
   including leukemia and several solid tumors.[195]^61 ABT-510 is a small
   mimetic peptide of a natural anti-angiogenic compound called
   thromboreactive protein-1. It has been shown to significantly reduce
   tumor growth and burden in preclinical mouse models and in naturally
   occurring dog tumors.[196]^62 Additionally, ABT-510 induced tumor cell
   apoptosis and inhibited ovarian tumor growth in an orthotopic,
   syngeneic model of epithelial ovarian cancer.[197]^62 It is interesting
   to note that all the above-mentioned drugs are gene-related and
   obtained through database predictions. They provide us with new
   potential strategies for the treatment of cancers. However, to
   determine whether they can be used in GC treatment, we need more
   experiments and clinical verification.

Conclusions and Limitations

   In this study, we have successfully screened 10 hub genes associated
   with GC using the GEO database and bioinformatics analyses and have
   established that 7 of these genes were associated with poor cancer
   prognosis. We have also studied the mRNA and protein expression levels
   through real-time PCR and IHC experiments. We demonstrated that
   searching for drugs related to these hub genes in the DGIdb has
   provided new insights for GC treatment. However, this study has some
   limitations. Due to the lack of further experimental studies and
   validation, we cannot explain exactly how the hub gene-miRNAs networks
   and therapeutic drugs play a role in the diagnosis and treatment of GC.
   Due to the short follow-up time and a small number of patients (n =
   60), clinicopathological data could not be analyzed conclusively. This
   issue will be addressed in future research.

   In summary, identifying the hub genes, miRNA–mRNA network, and
   potential candidate drugs associated with GC provides new insights into
   the molecular mechanisms and treatment of GC.

Funding Statement

   All funding for this study was provided by the National Natural Science
   Foundation of China (81970558) and Guangxi Natural Science Foundation
   (2020GXNSFAA259095).

Data Sharing Statement

   All data in the study is available from the submitting author, Shiyu
   Zhang.

Ethics Approval and Written Informed Consent

   The study has been approved by the Ethics Committee of the First
   Affiliated Hospital of Guangxi Medical University (No. 2021 KY-E-188)
   and the First People’s Hospital of Suqian (No. 2020-SL-0093). All
   participating gastric cancer patients were informed of the study
   purpose and signed informed consent forms.

Consent for Publication

   All authors agree to the publication of all texts, images and tables in
   this study.

Disclosure

   All authors state that they have no potential conflicts of interest in
   this work.

References