Identification of differentially expressed mRNAs as novel predictive biomarkers for gastric cancer diagnosis and prognosis

Abstract

BACKGROUND

Gastric cancer (GC) has a high mortality rate worldwide. Despite significant progress in GC diagnosis and treatment, the prognosis for affected patients still remains unfavorable.

AIM

To identify important candidate genes related to the development of GC and identify potential pathogenic mechanisms through comprehensive bioinformatics analysis.

METHODS

The Gene Expression Omnibus database was used to obtain the GSE183136 dataset, which includes a total of 135 GC samples. The limma package in R software was employed to identify differentially expressed genes (DEGs). Thereafter, enrichment analyses of Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed for the gene modules using the clusterProfile package in R software. The protein-protein interaction (PPI) networks of target genes were constructed using STRING and visualized by Cytoscape software. The common hub genes that emerged in the cohort of DEGs that was retrieved from the GEPIA database were then screened using a Venn Diagram. The expression levels of these overlapping genes in stomach adenocarcinoma samples and non-tumor samples and their association with prognosis in GC patients were also obtained from the GEPIA database and Kaplan-Meier curves. Moreover, real-time quantitative polymerase chain reaction (RT-qPCR) and western blotting were performed to determine the mRNA and protein levels of glutamic-pyruvic transaminase (GPT) in GC and normal immortalized cell lines. In addition, cell viability, cell cycle distribution, migration and invasion were evaluated by cell counting kit-8, flow cytometry and transwell assays. Furthermore, we also conducted a retrospective analysis on 70 GC patients diagnosed and surgically treated in Wenzhou Central Hospital, Dingli Clinical College of Wenzhou Medical University, The Second Affiliated Hospital of Shanghai University between January 2017 to December 2020. The tumor and adjacent normal samples were collected from the patients to determine the potential association between the expression level of GPT and the clinical as well as pathological features of GC patients.

RESULTS

We selected 19214 genes from the GSE183136 dataset, among which there were 250 downregulated genes and 401 upregulated genes in the tumor samples of stage III-IV in comparison to those in tumor samples of stage I-II with a P-value < 0.05. In addition, GO and KEGG results revealed that the various upregulated DEGs were mainly enriched in plasma membrane and neuroactive ligand-receptor interaction, whereas the downregulated DEGs were primarily enriched in cytosol and pancreatic secretion, vascular smooth muscle contraction and biosynthesis of the different cofactors. Furthermore, PPI networks were constructed based on the various upregulated and downregulated genes, and there were a total 15 upregulated and 10 downregulated hub genes. After a comprehensive analysis, several hub genes, including runt-related transcription factor 2 (RUNX2), salmonella pathogenicity island 1 (SPI1), lysyl oxidase (LOX), fibrillin 1 (FBN1) and GPT, displayed prognostic values. Interestingly, it was observed that GPT was downregulated in GC cells and its upregulation could suppress the malignant phenotypes of GC cells. Furthermore, the expression level of GPT was found to be associated with age, lymph node metastasis, pathological staging and distant metastasis (P < 0.05).

CONCLUSION

RUNX2, SPI1, LOX, FBN1 and GPT were identified key hub genes in GC by bioinformatics analysis. GPT was significantly associated with the prognosis of GC, and its upregulation can effectively inhibit the proliferative, migrative and invasive capabilities of GC cells.

Key Words: Gastric cancer, Differentially expressed genes, Bioinformatics, Hub genes, Prognosis

Core Tip: Five hub genes, including RUNX2, SPI1, LOX, FBN1 and GPT, were found to display prognostic values. The oncogenic role of all of these hub genes, except GPT, have been previously reported in gastric cancer. Glutamic-pyruvic transaminase (GPT) expression was significantly associated with age, lymph node metastasis, pathological staging and distant metastasis in patients with gastric cancer. Additionally, GPT was downregulated in gastric cancer cells, and its overexpression inhibited the proliferative, migrative and invasive capabilities of gastric cancer cells. Consequently, we have identified five hub genes (RUNX2, SPI1, LOX, FBN1 and GPT) as potential biomarkers and therapeutic targets for gastric diagnosis and treatment.

INTRODUCTION

As a malignant tumor of the digestive tract, gastric cancer (GC) represents the fifth most prevalent malignancy and the third leading cause of cancer-related mortality worldwide[1]. According to the global cancer statistics in 2018, there were approximately one million new cases and 78.3 million deaths from GC[2]. Helicobacter pylori infection, alcohol consumption, tobacco smoking, salt-preserved foods, and low fruit intake are considered as the primary risk factors associated with GC[3,4]. Currently, early GC diagnosis is not feasible in majority of patients despite significant advancements in GC treatment, including surgery, radiotherapy, neoadjuvant, chemotherapy, and immunotherapy. Moreover, only 5%-20% of patients diagnosed with advanced GC survive unto five years due to the development chemoresistance and metastasis[5-7]. Therefore, elucidation of the mechanisms underlying GC progression and finding novel as well as effective prognostic factors and targets can aid to improve long-term survival rates.

Messenger RNAs (mRNAs) have been reported to play important role in cycle regulation[8], cell adhesion[9], angiogenesis[10], and tumorigenesis[11]. For instance, Cho et al[12] have revealed six distinct mRNAs were associated with prognosis in GC patients. Chen et al[13] have identified three specific mRNAs associated with GC survival. Wang et al[14] have discovered a 25-mRNA signature associated with prognosis in GC. These reports have limitations due to small sample sizes and lack of validation datasets. Therefore, identification of the differentially expressed genes (DEGs) as new predictive targets is of great significance.

In this study, the mRNA expression profile in the GSE183136 dataset was first downloaded from the Gene Expression Omnibus (GEO) database. Thereafter, novel mRNA signatures were constructed through the publicly available databases. Our results may provide a new reference for both predicting and understanding the prognosis in GC patients.

MATERIALS AND METHODS

Data collection

An expression profile dataset for the mRNAs (GSE183136) was obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo). This dataset contains a total of 135 GC samples, including 41 stage I-II tumor samples and 94 stage III-IV tumor samples.

Screening of DEGs

For the GEO data, the DEGs were identified through the limma package in R software. P < 0.05 was considered as statistically significant. The R tool ggplot2 was used to construct a volcano plot to visualize the DEGs. The various highlighted genes were differentially expressed at a default adjusted P value cutoff of 0.05. The log2 fold change vs average log2 expression values were shown in a mean difference plot. The top ten upregulated and downregulated genes have been respectively shown in Supplementary Tables 1 and 2.

Functional annotation analysis

The DAVID database (http://david.ncifcrf.gov/) provides functional annotation of genes obtained from the various genomic resources. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEEG) analyses were performed using the DAVID and R software. GO analysis was then divided into molecular function (MF), cellular component (CC), and biological process (BP) categories. A false discovery rate < 0.05 was set as the screening threshold for the significant enrichment. The ggplot2 package of R was employed to visualize the results of the functional enrichment analysis.

Protein-protein interaction network

The protein-protein interaction (PPI) network of the various target genes was constructed using the STRING website (http://string-db.org/) with the medium confidence of 0.4. The molecular interaction network was thereafter visualized through the Cytoscape (version 3.4.0). The hub genes have been listed in Supplementary Table 3.

Identification of the association between the hub genes and prognosis in GC patients

The upregulated and downregulated genes were obtained from the GEPIA database (http://gepia.cancer-pku.cn/). The intersection between the hub genes and the differential genes obtained from the GEPIA database was screened using the Venn Diagram package of R. The overlapping gene expression profile in stomach adenocarcinoma (STAD) samples and non-tumor samples and their potential association with prognosis in GC patients were also collected from the GEPIA database and Kaplan-Meier curves (https://kmplot.com/analysis/index.php?p=miRnaAnalysis).

Cell culture and transfection

Human normal gastric mucosal cell line (GES1) and GC cell lines (AGS, MKN45, MKN28, HGC27) were obtained from the Type Culture Collection of Chinese Academy of Science (Shanghai, China). These cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% fetal bovine serum (FBS), 100 mg/mL streptomycin, and 100 U/mL penicillin (Procell, Wuhan, China) at 37 ℃ in the presence of 5% CO₂ in a humidified incubator.

The cells were divided into two different groups: control group and GPT overexpression group (GPT). AGS and HGC27 cells were seeded into 6-well plates (1 × 10⁵ cells/well). After the cells reached 80%–90% confluence, they were transfected using Lipofectamine 2000 (Sigma-Aldrich, Shanghai, China) with the vectors (pcDNA3.1-GPT overexpression vector and negative control empty vector) purchased from Shanghai Gene Biochemistry (Shanghai, China). After 24 h of transfection, the normal RPMI 1640 medium containing 10% FBS was used to replace the residual medium of each well, followed by incubation for 48 h at 37 ℃ with 5% CO₂.

RT-qPCR

The mRNA level of GPT was analyzed by RT-qPCR as previously described[15]. Total RNA was isolated from various GC cells (AGS, MKN45, MKN28 and HGC27) and the normal immortalized cells (GES1) by using TRIzol reagent (Absin, Shanghai, China), and then the total RNA (1 μg) was reverse transcribed into cDNA using a reverse transcription kit (Sigma-Aldrich). The various primers and Go Taq polymerase (Promega, Beijing, China) were used in the PCR reaction, which used the cDNA as a template. The RT-qPCR was performed on the thermal cycler (Bio-Rad, Richmond, CA, United States) and on the ABI PRISM 7000 Sequence Detection System using Sensi FAST SYBR Hi-ROX Mix (Bioline, London, United Kingdom). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an endogenous reference for the mRNA detection. The 2^-ΔΔCT method[16] was used to calculate the data. The experimental setup consisted of 40 cycles of 95 ℃ for 10 min, 95 ℃ for 5 s, and 60 ℃ for 60 s. The primer sequences used were as follows: GPT, forward 5′-GGACTACTACCTGGACGAAGA-3′, reverse 5’-CACATAGCCACCACGAAAC-3’; GAPDH, forward 5′-TCCTGCACCACCAACTGCTTAG-3′, reverse 5′-AGTGGCAGTGATGGCATGGACT-3′.

Western blotting

The protein level of GPT was measured using Western blotting as described previously[17]. The total protein was isolated from GC cells (AGS, MKN45, MKN28 and HGC27) and the normal immortalized cells (GES1) using RIPA lysis buffer containing protease phosphatase cocktail obtained from Beyotime (Shanghai, China). The extract was centrifuged at 10000 × g for 15 min to remove the cell debris. The protein concentration was quantified using a bicinchoninic acid (BCA) protein assay kit (Beyotime). Thereafter, the protein samples (20 μg per lane) were resolved using 10% Sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and transferred onto a polyvinylidene difluoride (PVDF) membrane. After blocking with 5% skimmed milk for 1 h, the membrane was incubated overnight with the primary antibodies against GPT (PAS-29600, 1:1000; Thermo Fisher Scientific, Shanghai, China) and GAPDH (ab8245, 1:2000; Abcam) at 4 ℃. Subsequently the membranes were incubated with secondary antibodies for 2 h at the room temperature. The bands were visualized using an enhanced chemiluminescent reagent (Yeasen, Shanghai, China). The immunoblot images were quantified by ImageJ software.

Cell counting kit-8 assay

Cell counting kit-8 (CCK-8) assay was employed to determine cell viability as described previously[18]. AGS and HGC27 cell lines were seeded into 96-well plates (1 × 10⁴ cells/well) and incubated at 37 ℃ with 5% CO₂. Thereafter, on days 0-7 respectively, 100 μL medium containing 10 μL CCK-8 solution (Beyotime) was added to each well followed by additional 2 h of incubation at 37 ℃ in 5% CO₂. Finally, absorbance was measured at 450 nm using a microplate reader (Molecular Devices, Shanghai, China).

Cell cycle analysis

The progression of cell cycle was analyzed by flow cytometry as described previously[19]. The cells were harvested after 72 h of transfection by trypsin and washed thrice with PBS. Thereafter, the cells were fixed in ice-cold 70% ethanol for 4 h at 4 ℃. Subsequently, the supernatant was discarded, and the cells were incubated with propidium iodide (Sigma-Aldrich) for 30 min at the room temperature. The cell cycle was detected using FACS Calibur (BD Biosciences, San Jose, CA, United States), and data was analyzed by FlowJo software (TreeStar Inc., Ashland, OR, United States).

Transwell assays

The cell migration and invasion were examined by Transwell assays as previously described[20]. A suspension of AGS and HGC27 cells in RPMI 1640 medium was added at a density of 2 × 10⁴ cells into a transwell insertion (with 8 mm pore size; Corning, Shanghai, China). The transwell membrane was precoated with Matrigel (Corning), and 500 μL of RPMI-1640 medium containing 5% FBS was then added to the bottom chambers as an inducer. After 48 h of incubation, invaded cells on the bottom of the transwell membrane were fixed by methanol and stained with 0.1% crystal violet, whereas the non-invaded cells were removed using a cotton swab. A light microscope (Olympus, Tokyo, Japan) was employed to visualize the invaded cells. The cell migration was also measured as described above without the Matrigel. The number of cells per field was counted using the ImageJ software.

Patients and samples

A retrospective analysis was performed on 70 GC patients diagnosed and surgically treated in Wenzhou Central Hospital, Dingli Clinical College of Wenzhou Medical University, The Second Affiliated Hospital of Shanghai University from January 2017 to December 2020. Inclusion criteria were as following: (1) Pathology-diagnosed cancer tissue specimens, and adjacent tissues surrounding tissues that were found to be free of cancer or inflammatory cell infiltration; and (2) patients with complete medical records and follow-up data. Exclusion criteria: (1) Patients who had undergone radiotherapy and chemotherapy prior to the collection of specimens; (2) pregnant or lactating patients; (3) patients with other severe diseases; and (4) patients with communication or cognitive disorders. During the operation, GC and the adjacent tissues (5 cm away from the cancerous tissues) were collected and stored at -80℃. This study was approved by the Ethics Committee of Wenzhou Central Hospital, Dingli Clinical College of Wenzhou Medical University, The Second Affiliated Hospital of Shanghai University (No. 202401302247000596077). All the patients who participated in this research had signed informed consents. The potential correlation between the expression level of GPT and clinicopathological features in 70 GC patients were analyzed and have been presented in Table 1.

Table 1 Associated of glutamic-pyruvic transaminase expression with clinical and pathological features of gastric cancer patients.

Clinical and pathological features	n = 70	GPT expression		P value
		Low (35)	High (35)
Gender				0.631
Male	38	18	20
Female	32	17	15
Age (yr)				0.015a
≤ 50	28	9	19
> 50	42	26	16
Lymph node metastasis				0.016a
Yes	51	30	21
No	19	5	14
Pathological staging				0.006b
Sage I-II	25	7	18
Stage III-IV	45	28	17
Distant metastasis				0.001c
Yes	43	28	15
No	27	7	20
Location classification				0.685
Gastric fundus and cardia cancer	31	14	17
Gastric corpus cancer	21	12	9
Gastric antrum cancer	18	9	10

^aP < 0.05.

^bP < 0.01.

^cP < 0.001.

GPT: Glutamic-pyruvic transaminase.

Statistical analysis

All the experiments were performed at least in three independent repeats. The Statistical analysis was conducted using GraphPad Prism 8 (GraphPad Software, San Diego, CA, United States). The data was described as the mean ± SD. One-way analysis of variance followed by Tukey’s post hoc analysis and student’s t-test were used for the comparison analyses. P < 0.05 was considered as statistically significant.

RESULTS

Identification of DEGs

To explore the possible role of systems biology in GC pathogenesis, we downloaded the mRNA expression array (GSE183136). Thereafter, in order to extract DEGs, we screened the GSE183136 dataset using the limma program (P value < 0.05). The Volcano plots showed the differential expression of multiple genes from 41 tumor samples of stage I-II and 94 tumor samples of stage III-IV. Overall, a total of 651 DEGs were screened, including 250 downregulated and 401 upregulated genes (Figure 1). The top ten upregulated and downregulated genes have been displayed in Supplementary Tables 1 and 2 respectively. CEMIP, KRT80, EMP2, APOL4, IPMK, C1orf127, ECM1, GPR143, CCL13, and KLF4 were identified as upregulated genes in the tumor samples of stage III-IV, whereas the downregulated genes included TM4SF4, FAM3D, MT1M, GC, RPL21, MT1IP, MSRA, SLC25A23, CCDC115, and R3HCC1.

Figure 1 Volcano plots of differentially expressed genes between the tumor samples of stage III to stage IV and the tumor samples of stage I to stage II.

Functional enrichment analysis of upregulated genes

We performed GO function and KEGG enrichment analysis by DAVID to gain a deeper understanding of the biological functions of the elevated DEGs shown in Figure 1. Functional enrichment analysis was conducted on the DEGs. GO results revealed that the DEGs were significantly enriched in the signal transduction and G-protein coupled receptor signaling pathway of BP, plasma membrane and integral component of membrane of CC, as well as RNA polymerase II core promoter proximal region sequence-specific DNA binding and G-protein coupled receptor activity of MF (Figure 2A). Moreover, KEGG analysis demonstrated that the upregulated DEGs were primarily enriched in neuroactive ligand-receptor interaction and cytokine-cytokine receptor interaction (Figure 2B).

Figure 2 Functional analysis of upregulated genes. A: Upregulated genes were analyzed by Gene Ontology terms; B: The top 12 pathways enriched with upregulated genes. BP: Biological process; CC: Cellular component; MF: Molecular function.

Functional enrichment analysis of downregulated genes

For all the downregulated DEGs identified in Figure 1, it was found that the protein autophosphorylation and skeletal system morphogenesis were the most enriched in the BP, cytosol and cytoplasm were the most enriched gene terms in the CC, whereas the protein kinase activity, protein serine/threonine kinase activity and hydrolase activity were the most enriched gene terms in the MF (Figure 3A). Furthermore, the downregulated DEGs were enriched in the pancreatic secretion, vascular smooth muscle contraction and biosynthesis of cofactors (Figure 3B).

Figure 3 Functional analysis of downregulated genes. A: Downregulated genes were analyzed by Gene Ontology terms; B: The top 9 pathways enriched with downregulated genes. BP: Biological process; CC: Cellular component; MF: Molecular function.

Construction of PPI network

To identify the key hub genes, the PPI networks were created based on the selected DEGs from STRING and visualized by Cytoscape (Figures 4 and 5). The hub genes have been listed in Supplementary Table 3. The various up-regulated hub genes were identified with node degree ≥ 10, including IL6, SMAD2, RUNX2, POMC, ACAN, CD28, IL1A, CCR2, SPI1, ELN, FBN1, IL2RA, LOX, PDYN, and SOX3. In addition, 10 downregulated hub genes were screened with node degree ≥ 5, including SERPINC1, GC vitamin D binding protein, SLC2A2, NLE1, RPL21, FETUB, PLG, KCNQ1, HPX, and GPT.

Figure 4 Protein-protein interaction network of upregulated genes.

Figure 5 Protein-protein interaction network of downregulated genes.

Selection of the candidate mRNAs

The mRNA expression levels of 15 upregulated and 10 downregulated key hub genes were validated by GEPIA database, and the results indicated that the expression levels of ACAN. RUNX2, SPI1, LOX, FBN1, and IL2RA were significantly higher but the expression levels of GC vitamin D binding protein and GPT were significantly lower in STAD samples in comparison to those in the normal tissues (Figure 6).

Figure 6 The intersection between the hub genes and differential expression profiles from the GEPIA database. A: The overlapping upregulated hub genes; B: The overlapping downregulated hub genes.

Candidate targets for diagnostic application of GC treatment

We observed that six upregulated genes, including ACAN. RUNX2, SPI1, LOX, FBN1, and IL2RA, as well as the two downregulated genes, including GC vitamin D binding protein and GPT, could serve as the potential biomarkers for GC diagnosis and prognosis. Subsequently, based on the GEPIA database, the levels of ACAN. RUNX2, SPI1, LOX, FBN1, and IL2RA were found to be significantly higher in STAD samples in comparison to those in the non-tumor samples (Figure 7). The expression levels of RUNX2, SPI1, LOX, and FBN1 were positively correlated with the various clinical stages (Figure 8). As depicted by Kaplan-Meier curves, high expression levels of FBN1, LOX, RUNX2, and SPI1 were positively correlated with the poor overall survival (Figure 9). The expression levels of GC vitamin D binding protein and GPT were noted to be downregulated in STAD samples compared to those in the non-tumor samples (Figure 10A and B). Moreover, high expression of GPT predicted better overall survival (Figure 10C).

Figure 7 The expression levels of common hub genes in the stomach adenocarcinoma samples and nontumor samples based on the GEPIA database. A: ACAN; B: RUNX2; C: SPI1; D: LOX; E: FBN1; F: IL2RA. ^aP < 0.05.

Figure 8 The expression levels of common hub genes in the tumor samples of different clinical stages. A: RUNX2; B: SPI1; C: LOX; D: FBN1.

Figure 9 The prognosis of upregulated genes in the patients with STAD. A: Kaplan-Meier survival curves for the FBN1; B: The prognosis mediated by LOX; C: The correlation between RUNX2 expression and overall survival rate; D: The correlation between OF (SPI1) expression and overall survival rate.

Figure 10  The expression levels and prognosis of downregulated genes. A: The expression levels of gastric cancer vitamin D binding protein in stomach adenocarcinoma samples and nontumor samples; B: The expression levels of glutamic-pyruvic transaminase (GPT); C: The correlation between GPT expression and overall survival rate. ^aP < 0.05.

GPT upregulation suppressed the proliferative, migrative and invasive capabilities of GC cells

The oncogenic role of all these hub genes (RUNX2, SPI1, LOX and FBN1), except GPT, have been previously reported in GC progression[21-24]. As displayed in Table 1, GPT expression was significantly associated with age, lymph node metastasis, pathological staging and distant metastasis in GC patients. Thereafter, we detected the potential effect of GPT on the various malignant phenotypes of GC cells. RT-qPCR and western blotting assays were conducted to determine the mRNA and protein levels of GPT in GC cells (AGS, MKN45, MKN28 and HGC27) and the normal gastric mucosal cells (GES1). The results revealed that the expression of GPT was significantly downregulated in GC cells in comparison to that in the normal gastric mucosal cells. Interestingly, AGS and HGC27 cells exhibited lower expression of GPT than both MKN45 and MKN28 cells; hence, we used AGS and HGC27 cells for the subsequent experiments (Figure 11A-C). Considering the downregulation of GPT in GC cells, we significantly overexpressed GPT expression in AGS and HGC27 cells by transient transfection. The overexpression efficiency was verified by RT-qPCR and western blotting assays (Figure 11D-F). Thereafter, we investigated the biological functions of GPT overexpression. As demonstrated by CCK-8 assay, GPT upregulation caused a significant inhibition in GC cell viability (Figure 11G-H). The results of flow cytometry also suggested that GPT upregulation exhibited substantial adverse effects on GC cell cycle progression (Figure 11I-J). Finally, transwell assays were performed to evaluate the cell migration and invasion potential of GC cells, and the results revealed that GPT upregulation suppressed both the migrative and invasive abilities of GC cells (Figure 11K-L). Collectively, GPT upregulation could significantly inhibit GC cell proliferation, migration and invasion.

Figure 11  Glutamic-pyruvic transaminase upregulation suppresses GC cell proliferation, migration and invasion. A: Real-time quantitative polymerase chain reaction (RT-qPCR) of glutamic-pyruvic transaminase (GPT) mRNA level in gastric cancer cell lines (AGS, MKN45, MKN28 and HGC27) and normal immortalized cell line (GES1); B and C: Western blotting of GPT protein level; D-F: Overexpression efficiency of GPT overexpression vector was verified by RT-qPCR and western blotting; G and H: CCK-8 assays of cell viability; I and J: Flow cytometry of cell cycle; K and L: Transwell assays of cell migration and invasion. ^aP < 0.05; ^bP < 0.01; ^cP < 0.001.

DISCUSSION

GC is a malignant tumor characterized by high mortality. The progression of GC has been found to be complicated due to the involvement of various genetic and epigenetic modifications[25]. The delayed diagnosis could be attributed to asymptomatic GC at an early stage, and the diagnosis made at an advanced stage (tumor, node, and metastasis) often contributes to its high mortality[26]. Despite efforts to molecularly classify GC, treatment options for this malignancy remain limited[27]. Therefore, identification of the novel therapeutic targets for better clinical applications is critical for both GC diagnosis and treatment. We have analyzed 19214 genes from the GSE183136 dataset, among which there were 250 downregulated genes and 401 upregulated genes with P-value < 0.05. Moreover, the DEGs were divided into BP, CC, and MF groups using the GO functional annotation. In addition, the various enriched pathways of DEGs were detected through the KEGG pathway analysis. Furthermore, PPI networks were constructed based on the various upregulated and downregulated genes, and in total 15 upregulated hub genes and 10 downregulated were found among all the hub genes screened. Finally, a differential expression profile was obtained from the GEPIA database, and a Venn diagram was created to identify the common genes in the hub genes and the differential expression profile. After a comprehensive analysis, five hub genes, including RUNX2, SPI1, LOX, FBN1 and GPT, were found to display prognostic values.

It has been demonstrated that RUNX2 plays an oncogenic role in GC by increasing colony and sphere formation as well as tumorigenesis in GC cells[28]. GC cell invasion and migration are facilitated in vitro by RUNX2 overexpression[29]. Accumulating evidences have suggested a critical role of RUNX2 in GC progression[30-32]. SPI1 is a key factor in regulating the prognosis for GC patients and has been suggested as a possible target for immunotherapy[33]. SPI1 is involved in several pathways associated with proliferation, apoptosis, and angiogenesis in GC[34]. LOX is associated with poor prognosis in GC patients[35-37]. Furthermore, inhibition of FBN1 can suppress proliferation and mobility of in GC cells[24]. In addition, several studies have identified FBN1 as a candidate gene in GC[38,39]. However, the biological functions of GPT in GC progression have not been revealed yet. In the current study, we found that GPT expression was significantly associated with age, lymph node metastasis, pathological staging as well as distant metastasis in GC patients, and GPT upregulation could significantly inhibit the proliferative, migrative and invasive capabilities of GC cells.

CONCLUSION

In conclusion, this study was able to integrate the gene expression profile of GSE183136 dataset. Five distinct hub genes (RUNX2, SPI1, LOX, FBN1 and GPT) displayed significant prognostic values, and GPT expression was negatively associated with age, lymph node metastasis, pathological staging and distant metastasis of GC patients. in addition, GPT upregulation inhibited the malignant phenotypes of GC cells. However, there are several limitations associated with this study. First, this is a pilot study, and further studies are required to validate the potential biological role of DEGs and hub genes in GC development. Second, in vivo experiments were not conducted in this study, and the possible role of GPT overexpression in GC xenograft models needs to be investigated. Furthermore, for clinical significance and prognosis, protein expression of the hub genes in larger clinical samples should be analyzed. Despite these limitations, this study has discovered several potential biomarkers for GC diagnosis and treatment.

ARTICLE HIGHLIGHTS

Research background

Being one of the most prevalent and lethal cancers globally, gastric cancer (GC) contributes significantly to the overall cancer burden. Due to chemoresistance and metastasis, only 5%–20% of patients with advanced GC survive for five years after diagnosis, and most patients are not able to receive an early diagnosis. The findings of this study shed light on the fundamental mechanisms of GC pathogenesis and has identified new mRNA biomarkers and targets for GC.

Research motivation

The current study was designed to explore the molecular mechanisms involved in GC progression using the bioinformatics methods. We have identified five distinct hub genes that were associated with the development of GC. This study has provided several potential targets regarding GC, which could form the basis of novel strategy to diagnose and treat GC.

Research objectives

The main objectives of this study were to identify the key candidate genes linked with development of GC and to determine the potential pathogenic mechanisms by using integrated bioinformatics analysis. Five distinct hub genes (RUNX2, SPI1, LOX, FBN1 and GPT) were identified as novel biomarkers and targets for GC diagnosis and treatment. The possible effect of GPT on the malignant phenotypes of GC cells and the possible correlation between GPT expression as well as the clinical and pathological features of GC patients were also analyzed. Our results provide a sound theoretical basis for the pathogenesis, clinical diagnosis and therapeutic targets of GC.

Research methods

The GSE183136 dataset that contains 135 GC samples was downloaded from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) were then identified using the limma package in R software. Thereafter, gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes analyses were performed for enrichment analyses using the clusterProfile package in R software. Subsequently, the protein-protein interaction networks of DEGs were established by STRING and visualized by Cytoscape software. The key hub genes were identified as the overlapping genes that appeared in the cohort of above mentioned DEGs as well the cohort of the DEGs obtained from the GEPIA database Following that, the expression levels of these hub genes and their association with prognosis in GC patients were obtained from the GEPIA database and Kaplan-Meier curves. In addition, cell counting kit-8 assays, flow cytometry as well as transwell assays and a retrospective analysis on 70 GC patients were performed to detect the potential effect of GPT on cell viability, cell cycle, migration and invasion. The association between the expression level of GPT and the clinical and pathological features of GC patients was also examined.

Research results

We have identified 250 downregulated and 401 upregulated DEGs. After a comprehensive analysis, five different hub genes (RUNX2, SPI1, LOX, FBN1 and GPT) were selected. In this study, we have mainly focused on GPT, and observed that GPT expression was significantly associated with age, lymph node metastasis, pathological staging and distant metastasis in GC patients. Moreover, based on in vitro analysis, GPT upregulation was able to suppress the proliferative, migrative and invasive capabilities of GC cells. Our results might provide potential targets for GC diagnosis and treatment. The problems that remain to be solved include: (1) Additional studies are required to examine the potential effect of DEGs and hub genes on GC development; (2) in vivo experiments were not performed in this study, and the impact of GPT overexpression in GC xenograft models needs further investigation; and (3) the expression of the hub genes in larger clinical samples to establish the clinical relevance and prognosis are required.

Research conclusions

This study has identified several hub genes related to GC development. The methods used in this study have been previously described.

Research perspectives

Further investigation is required to detect the effect of GPT on GC development.