OL Oncology Letters 1792-1074 1792-1082 D.A. Spandidos 10.3892/ol.2020.11961 OL-0-0-11961 Articles Identification and verification of the prognostic value of the glutathione S-transferase Mu genes in gastric cancer ChenYeyang 1 * LiBopei 1 * WangJunfu 1 LiuJinlu 1 WangZhen 1 MaoYuantian 1 LiuSiyu 1 LiaoXiwen 2 ChenJunqiang 1 Department of Gastrointestinal Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China Correspondence to: Dr Junqiang Chen, Department of Gastrointestinal Surgery, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi 530021, P.R. China, E-mail: gxhans@163.com

Contributed equally

 10 2020 07 08 2020 20 4 100 09102019 23062020 Copyright: © Chen et al. 2020 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Gastric cancer (GC) is one of the most frequently diagnosed gastrointestinal cancer types in the world. Novel prognostic biomarkers are required to predict the progression of GC. Glutathione S-transferase Mu (GSTM) belongs to a family of phase II enzymes that have been implicated in a number of cancer types. However, the prognostic value of the GSTM genes has not been previously investigated in GC. The Cancer Genome Atlas (TCGA) was used to evaluate mRNA expression levels of GSTMs in GC tissue samples. Overall survival (OS) rates, hazard ratios (HRs) and 95% CIs were calculated using the Cox logistic regression model and Kaplan-Meier (KM) analysis was performed. In addition, the KM plotter online database was used to validate mRNA expression and the prognostic value of GSMT family members in patients with GC. To predict the function of GSTM genes in these patients, several bioinformatics tools, including the Database for Annotation, Visualization and Integrated Discovery, gene multiple association network integration algorithm, Search Tool for the Retrieval of Interacting Genes/Proteins, Gene Set Enrichment Analysis (GSEA), nomogram and genome-wide co-expression analysis were used. In the present study, high expression of GSTM5 was indicated to be strongly associated with lower OS in patients with GC, according to the TCGA and KM plotter online databases (HR=1.47, 95% CI: 1.06-2.04, P=0.021; and HR=1.69, 95% CI: 1.42-2.01, P=1.6×10−9, respectively). The results from the GSEA and genome-wide co-expression analysis indicated that GSTM5 expression associated with several biological process terms, including ‘adhesion’, ‘angiogenesis’, ‘apoptotic process’, ‘cell growth’, ‘proliferation’, ‘migration’, ‘Hedgehog signaling’, ‘MAPK signaling’ and the ‘TGF-β signaling pathway’. In conclusion, the present results indicated that GSTM5 may serve as a biomarker for GC prognosis and may be a potential therapeutic target for GC.

 glutathione S-transferase Mu The Cancer Genome Atlas gastric cancer prognosis biomarker Introduction

According to Global Cancer Statistics for 2018, gastric cancer (GC) is the fifth most prevalent cancer type and the third leading cause of cancer-associated mortality worldwide (1). Of note, its prevalence is markedly elevated in Eastern Asia. The number of newly diagnosed GC cases in 2018 was 1,033,701 worldwide and the estimated number of deaths was 782,685, translating to 1 in every 12 deaths globally (1). Risk factors commonly associated with GC include chronic infection with Helicobacter pylori (H. pylori), environmental factors, low fruit and vegetable intake, consumption of preserved foods, smoking and alcohol use (2,3). At present, surgery and chemotherapy are the major therapeutic strategies used to treat GC (4). However, only a limited number of patients with GC are diagnosed at early stage, whereas the majority of patients are diagnosed at advanced stage (5). The 5-year overall survival (OS) rate for patients with GC was only 27.4% in China in 2010, and 29% in the United States in 2009 (6,7). Therefore, it is important to explore the molecular mechanisms involved in the tumorigenesis and progression of GC, which may identify novel prognostic biomarkers and treatment targets.

The glutathione S-transferase Mu (GSTM) gene family consists of five genes identified in humans that are numbered M1-5. These genes occur as a cluster on chromosome 1p13, which are arranged in tandem, spanning a 97-kb region, and encode one of eight distinct classes of glutathione transferases (8–10). The GSTM gene family is arranged in a 5′-GSTM4-M2-M1-M5-M3-3′ sequence (9). These genes share a sequence homology of 60–80% (11). GSTM genes are generally recognized as detoxifying enzymes involved in the deactivating conversion of carcinogenic reactive metabolites, suggesting that these enzymes may have a role in carcinogenesis (12). To date, there is a lack of studies focusing on the value of the GSTM family of genes as prognostic biomarkers in GC.

To investigate the prognostic value and potential functions of GSTM genes in patients with GC, gene expression data and survival information from The Cancer Genome Atlas (TCGA) were analyzed. Subsequently, the Kaplan-Meier (KM) plotter online database was used to validate mRNA expression levels and the prognostic value of individual GSTM genes in patients with GC. Several bioinformatics tools were also used to explore the potential functions of GSTM genes.

 Materials and methods <sec> <title>Functional and co-expression analyses

To analyze the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of GSTM genes, the Database for Annotation, Visualization and Integrated Discovery (DAVID) version 6.8; (https://david.ncifcrf.gov/home.jsp; accessed March 1, 2018) was used (13,14). The functional examination based on GO includes the categories molecular function (MF), biological process (BP) and cellular component (CC). To evaluate gene-gene networks, the Gene Multiple Association Network Integration Algorithm (GeneMANIA) version 3.6.0 (http://www.genemania.org; accessed May 20, 2019), which predicted gene functions, was used (15,16). The Search Tool for the Retrieval of Interacting Genes (STRING) version 11.0 (https://string-db.org; accessed May 20, 2019) database was used to search and analyze protein-protein interaction (PPI) networks (17).

 Co-expression matrix

A co-expression matrix of GSTM genes was constructed using mRNA expression data from TCGA cohort of GC tumor tissues. Pearson's correlation coefficient analysis was used to analyze mRNA co-expression correlations. The co-expression matrix was constructed using the corrplot package in the R 3.4.4 platform (18). P<0.05 was considered to indicate a statistically significant difference.

 TCGA

RNA sequencing and clinical information (including tumor stage, age of patient and sex) linked with GC were downloaded from TCGA (https://portal.gdc.cancer.gov; accessed August 22, 2018). The TCGA data portal contained 407 patients diagnosed with GC, which included 375 tumor tissues and 32 adjacent normal tissues. After removing cases with missing follow-up profiles, a total of 351 patients with GC from TCGA were analyzed. Clinical data including clinical tumor-node-metastasis (TNM) stage (19), Lauren classification (20), differentiation grade, human epidermal growth factor receptor 2 (HER2) status and clinical treatment were also collected.

 Survival analysis

KM survival analyses and log-rank tests were used to calculate the OS rate and significance. Patients with GC were separated into high- and low-expression groups of GSTM based on the median values of expression. To perform univariate and multivariate survival analyses, the Cox proportional hazards regression model was used to calculate the hazard ratio (HR), 95% confidence interval (CI) and log-rank P-values. P<0.05 was considered to indicate a statistically significant difference.

 KM plotter online database

The associations between the mRNA levels of individual GSTM genes and OS rates were calculated using the KM plotter online database (http://kmplot.com/analysis/index.php?p=service&cancer=gastric) (21) based on gene expression data and survival information of 875 patients with GC downloaded from the Gene Expression Omnibus (datasets GSE14210, GSE15459, GSE22377, GSE29272, GSE51105 and GSE62254) (22). This tool was used for the identification and validation of survival biomarkers. In brief, GSTM1-5 were entered into the KM plotter online database and analyzed. Based on the median of mRNA expression for each GSTM according to the Gene Expression Omnibus, all GC patients were separated into two groups (high vs. low). Statistical parameters such as survival plot, HRs, 95% CIs and log-rank P-values were obtained from KM plotter. P<0.05 was considered to indicate a statistically significant difference.

 Nomogram and stratified analyses

Based on the survival analysis of TCGA and KM plotter, only GSTM5 was significantly associated with prognosis. A nomogram was developed and used to evaluate the contribution of GSTM5 expression and prognostic clinical parameters, including sex, age and tumor stage, in GC OS. Based on prognostic clinical indicators and the survival analysis of the Cox regression model, sex, age, stage and GSTM5 levels were entered into the risk model. The points against each factor were counted, and 1-, 3- and 5-year survival rates were also calculated (23). The nomogram was constructed using the rms package (https://CRAN.R-project.org/package=rms) (24).

To assess the prognostic value of GSTM5 in different GC strata, a stratified analysis method was performed. The association between GSTM5 and OS in TCGA and KM plotter online database were stratified in the GC cohort for sex, age, clinical stage, Lauren classification, differentiation grade, clinical treatment and HER2 status.

 Gene Set Enrichment Analysis (GSEA)

To investigate how the prognostic GSTM5 gene participates in GC, GSEA v.3.0 software (http://software.broadinstitute.org/gsea/msigdb/index.jsp) was used to identify the potential biological functions and signaling pathways associated with low vs. high expression levels of GSTM5. The Molecular Signatures Database of GSEA used the c2 (c2.cp.kegg.v6.2.symbols.gmt) and c5 (c5.all.v6.2.symbols.gmt) reference gene sets (25). A value of 1,000 was set as the number of permutations. P<0.05, normalized enrichment score >1 and false discovery rate <0.25 were considered to indicate a statistically significant difference.

 Genome-wide co-expression analysis of the prognostic GSTM5 gene and functional enrichment

To assess gene-gene co-expression interaction of prognostic genes at the mRNA level, Pearson's correlation coefficient was calculated using the cor function on the R 3.4.4 platform. Significant differences were defined as |r|>0.6 and P<0.05. In addition, DAVID version 6.8 was used to determine the GO functional term and KEGG pathway enrichment of GSTM and its co-expressed genes.

 Statistical analyses

All data were analyzed using SPSS version 25.0 software (IBM, Corp.). Vertical scatterplots and survival curves were generated using GraphPad Prism 7.0 software (GraphPad Software, Inc.). Vertical scatterplots were analyzed using independent t-tests. In addition, nomograms and correlation plots were generated using R software.

 Results <sec> <title>GSTM family functional enrichment and co-expression analysis

To evaluate the biological functions of GSTM genes, GO functional terms were determined in the categories BP, MF and CC and a KEGG pathway analysis was performed using DAVID (Fig. 1). GO analysis indicated that genes of the GSTM family were enriched in ‘protein homodimerization activity’, ‘enzyme binding’ and in the ‘cytosol’ (Fig. 1A). The results from the KEGG analysis suggested that the functions of the GSTM gene family were enriched in ‘chemical carcinogenesis’, ‘metabolism of xenobiotics by cytochrome P450’ and ‘drug metabolism-cytochrome P450’ (Fig. 1B). Gene-gene interaction networks of GSTM genes are presented in Fig. 2A, which revealed that GSTM1-5 were co-expressed. In addition, the GSTM genes were associated with other genes with the relationship of predicted interactions, physical interactions, co-expression, and shared pathway. Based on the information in the STRING database, PPI interaction networks revealed that GSTM family members were directly and indirectly connected to one another (Fig. 2B). The PPI network revealed that GSTM1 was only associated with GSTM2, and GSTM2 was the only gene that associated with all other family members. In addition, co-expression of the GSTM genes was observed in GC tissues, although the correlation coefficients appear to be weak (<0.4) (Fig. 3).

 Survival analysis

Significant differences were obtained in the vertical scatterplots between high and low expression of GSTM genes obtained from TCGA (all P<0.001; Fig. 4). A survival analysis comparing patients with GC with different expression levels of GSTM is presented in Fig. 5. High GSTM5 expression was significantly associated with a worse prognosis for patients with GC (HR=1.47, 95% CI: 1.06-2.04, P=0.021). A significant association between high GSTM4 expression and favorable OS was observed (HR=0.63, 95% CI: 0.45-0.87, P=0.006).

Multivariate survival analysis was also performed to investigate the prognostic value of GSTM in GC. Age and tumor stage were two factors identified to be significantly associated with prognosis, as an age of ≥65 years and advanced stages were significantly associated with a worse OS (HR=1.56, 95% CI: 1.12-2.17, P=0.011; HR=1.92, 95% CI: 1.37-2.70, P<0.001, respectively; Table I). To examine survival, tumor stage and age were analyzed using the multivariate Cox proportional hazards regression model. These analyses revealed that high GSTM5 expression was significantly associated with worse OS (HR=1.48, 95% CI: 1.05-2.08, adjusted P=0.027; Table II). These results were consistent with those of the univariate survival analysis (Table II). However, no significant differences were identified for GSTM4 in the multivariate survival analysis (Table II), which was not consistent with the results obtained in the univariate survival analysis (HR=0.63, 95% CI: 0.45-0.87, P=0.006; Table II and Fig. 5D).

 Validation of the GSTM cohort using the KM plotter online database

The GSTM cohort was validated using the KM plotter online database. KM curves of GSTM genes are presented in Fig. 6. These analyses revealed that high GSTM1, GSTM2, GSTM4 and GSTM5 mRNA levels were associated with a significantly worse OS for patients with GC (HR=1.44, 95% CI: 1.21-1.71, P=2.6×10−5; HR=1.57, 95% CI: 1.32-1.86, P=2×10−7; HR=1.23, 95% CI: 1.04-1.46, P=0.015; and HR=1.69, 95% CI: 1.42-2.01, P=1.6×10−9, respectively). The only result consistent with the TCGA analyses was that GSTM5 was significantly associated with a worse prognosis (HR=1.47, 95% CI: 1.06-2.04, P=0.021; Table II and Fig. 5E).

 Nomogram and stratified analysis

A nomogram for GC was developed based on GSTM5 expression levels, sex, age, tumor stage and 1-, 3- and 5-year survival rate (Fig. 7). The 1-, 3- and 5-year survival rates were higher for patients with a lower number of total points compared with those with a higher number of total points. In accordance with the nomogram, it was observed that the contribution of GSTM5 expression in prognosis prediction was lower compared with that of age and tumor stage, but higher compared with that of sex. The nomogram analyses revealed that GSTM5 contributes to the prognosis prediction for patients with GC. In addition, the prognostic value of GSTM5 in GC was analyzed using stratification analysis and the association between GSTM5 and OS was analyzed using the TCGA (Table III) and KM plotter online database (Table IV) GC cohorts, which included analyses for sex, age, clinical stage, Lauren classification, differentiation grade, clinical treatment and HER2 status. As presented in Table III, high GSTM5 mRNA levels were significantly associated with poor prognosis in patients with GC who were males and <65 years old based on the TCGA GC cohort (HR=1.59, 95% CI: 1.07-2.36, P=0.020; HR=1.86, 95% CI: 1.07-3.25, P=0.030, respectively). As presented in Table IV, high GSTM5 mRNA levels were significantly associated with worse survival for both female and male patients with GC, clinical stages I, II and III, all Lauren classifications, treatment by surgery and other adjuvant therapies, as well as a negative HER2 status, according to the KM plotter analysis.

 GSEA analysis of GSTM5 in GC cases

To explore the mechanisms underlying GSTM5 function, GSEA was used to assess differences in relative GSTM5 expression levels among TCGA specimens (Fig. 8, Tables SI and SII). In the GSEA, high levels of GSTM5 were significantly enriched in the following processes: ‘regulation of cell matrix adhesion’ (P<0.001), ‘angiogenesis’ (P<0.001), ‘regulation of cell growth’ (P<0.001), ‘regulation of endothelial cell apoptotic process’ (P=0.004), ‘extracellular matrix’ (P<0.001), ‘smoothened signaling pathway’ (P<0.001), ‘Hedgehog signaling pathway’ (P<0.001), ‘mitogen-activated protein kinases (MAPK) signaling pathway’ (P<0.001), ‘transforming growth factor (TGF)-β signaling pathway’ (P=0.025), ‘calcium signaling pathway’ (P<0.001) and other KEGG pathways associated with cancer.

 Genome-wide co-expression analysis of GSTM5 in GC and potential functional enrichment

To determine the potential mechanism underlying GSTM5 function in GC, genome-wide co-expression analysis was performed. Regulatory networks of GSTM5 and its co-expressed correlated genes identified in GC tumor tissues from the TCGA cohort are presented in Fig. 9 and Table SIII. GO analysis suggested that GSTM5 and its co-expressed genes were significantly enriched in processes including ‘cell adhesion’ (P=1.97×10−5), ‘single organismal cell-cell adhesion’ (P=3.83×10−5), ‘focal adhesion’ (P=3.24×10−4), ‘positive regulation of cell-substrate adhesion’ (P=0.003), ‘angiogenesis’ (P=0.004), ‘apoptotic process involved in luteolysis’ (P=0.048), ‘negative regulation of cell growth’ (P=2.75×10−5), ‘negative regulation of cell proliferation’ (P=8.51×10−5), ‘negative regulation of cell migration’ (P=8.89×10−4) and ‘negative regulation of Wnt signaling pathway’ (P=0.010). Enrichment of GSTM5 and its co-expressed genes in the ‘plasma membrane’ (P=0.002) and ‘extracellular matrix’ (P=1.88×10−10) was observed by GO cell component analysis (Tables V and SIV). Furthermore, GSTM5 and its co-expressed genes were enriched in the following KEGG pathways associated with prognosis: ‘Cyclic guanosine monophosphate-protein kinase G (cGMP-PKG) signaling pathway’ (P=3.04×10−9), cyclic adenosine monophosphate (cAMP) signaling pathway’ (P=0.004), ‘calcium signaling pathway’ (P=0.008), ‘focal adhesion’ (P=0.017) and ‘cell adhesion molecules (CAMs)’ (P=0.026) (Table VI).

 Discussion

In the present study, the expression levels of GSTM genes and their prognostic value in GC were assessed. A positive correlation was observed between GSTM5 expression and poor OS in GC. In addition, GSEA analysis and genome-wide co-expression analysis were used to predict potential mechanistic roles of GSTM5 in GC.

There are five members of the GSTM class of genes that encode phase II metabolic enzymes found primarily in the cytosol that co-operate with phase I enzymes in carcinogen metabolism (26). These genes are involved in the detoxification of electrophilic compounds, including carcinogens, therapeutic drugs, environmental toxins and products of oxidative stress, by conjugation with glutathione (27).

Previous studies revealed that the GSTM family of genes have critical roles in several cancer types. GSTM1 is highly polymorphic in humans and is associated with multiple cancer types, such as bladder and breast cancer, metabolic disorders and autoimmune diseases, as well as anticancer drugs response and resistance (28) GSTM1 deletion in humans was indicated to have a key role in bladder (29) and breast cancer (30,31), multiple sclerosis (32), severe early-onset mental disorders including schizophrenia-spectrum disorder, bipolar disorder with psychotic symptoms or first-episode psychosis (33) and acute myeloid leukemia (34). Csejtei et al (35) identified that GSTM1 may be a prognostic biomarker in clinical diagnostics as well as a potential therapeutic candidate in colorectal cancer. A recent study detected null GSTM1 genotypes in the tumor area of GC, in contrast to the presence of both genes in the proximal and distal margins of the tumor (36). Therefore, GSTM1 polymorphisms may be a potential prognostic marker for certain types of cancer. A protective effect of GSTM2 on oxidative stress was identified in studies performed in hepatic carcinoma, colon cells and spontaneously hypertensive rat (37–39). Previous studies have revealed an association between GSTM3 and certain cancer types, including laryngeal (40), oral (41,42), esophageal (43), breast (44), bladder (45), multiple cutaneous basal cell (46) cancer and childhood acute lymphoblastic leukemia (12). GSTM4 is a less studied member of the GSTM family and has been demonstrated to recognize the same standard glutathione S-transferases substrate 1-chloro-2,4-dinitrobenzen as other GSTMs, only with lower specific activity (47). The possible role of GSTM5 in the development of cancer warrants further exploration. Peng et al (48) reported that GSTM5 is involved in the detoxification of reactive electrophiles and is associated with Barrett's adenocarcinoma. Pankratz et al (49) observed that high-risk tag single-nucleotide polymorphisms in GSTM5 and ATP binding cassette subfamily C member 4 genes may be a good combined predictor of mortality in low-stage non-small cell lung cancer. Gene-based analysis also revealed that the expression of GSTM5 was involved in the OS of patients diagnosed with low-stage non-small cell lung cancer (49). Similar results were observed in a study by Kap et al (50), reporting that GSTM5 was differentially expressed in colon cancer tissues compared with normal colon tissues. In addition, high expression of GSTM5 is associated with poor OS in patients with colorectal cancer treated with oxaliplatin (HR=1.50, 95% CI: 1.03-2.19) (50). These conclusions suggested that GSTM5 may be an important prognostic biomarker. Despite previous studies revealing an association between GSTM5 and OS, associations between GSTM5 and survival of patients with GC have remained to be determined. The present study revealed that among all GSTM genes, only GSTM5 was independently associated with poor OS in patients with GC. According to the present nomogram, it was indicated that the contribution of the tumor stage increased with more advanced stages and higher expression levels of GSTM5 were associated with a less favorable survival prognosis. Compared with the tumor stage, age, sex and GSTM5 expression, the tumor stage and age were more significantly associated with GSTM5 expression. Therefore, the OS rate of patients with GC may be predicted using this model. The nomogram indicated that the combination of GSTM5 and other indicators may be considered a novel method to predict the prognosis for patients with GC.

In the stratified analyses with the KM plotter online database, higher levels of GSTM5 were associated with worse OS in female and male patients, clinical stages I–III, all Lauren classifications, treatment by surgery, administration of other adjuvant therapies, as well as a negative HER2 status. However, OS was not associated with female sex, age >65 years or clinical stage in the TCGA GC cohort. There were 375 cases of GC in the TCGA, but there were 875 cases of GC in the KM plotter online database. As TCGA appeared to have a shortage of GC samples, another prospective study should be performed in the future. The comprehensive survival analysis with stratification suggested that GSTM5 has value as an independent prognostic indicator for GC.

The mechanisms underlying the functions of GSTM5 in GC have remained largely elusive. In the present study, the outcomes of the GSEA analysis indicated that GSTM5 is involved in ‘regulation of cell matrix adhesion’, ‘angiogenesis’, ‘regulation of cell growth’, ‘regulation of endothelial cell apoptotic process’, ‘Hedgehog signaling’, ‘MAPK signaling’, ‘TGF-β signaling’ and other cancer-associated pathways. According to the results of the genome-wide co-expression matrix, co-expressed genes were enriched in ‘cell adhesion’, ‘angiogenesis’, ‘apoptotic process involved in luteolysis’, ‘cGMP-PKG signaling pathway’ and ‘cAMP signaling pathway’. In the GSEA analysis as well as the genome-wide co-expression matrix, enrichment in adhesion, angiogenesis and apoptosis was determined. During tumorigenesis, recurrence, invasion and metastasis are significantly affected by adhesion molecules (48). Aberrant expression of cell adhesion molecules may lead to abnormal proliferation of normal cells, and aberrant expression of these molecules is frequently associated with general carcinogenesis (51). Angiogenesis allows the tumor to grow, infiltrate and metastasize (52). Du et al (53) demonstrated that the synthesis of vascular endothelial growth factor is modulated by the cAMP-protein kinase A-cAMP response element-binding pathway. Zhang et al (54) reported that loss of the regulative effect of dimethylarginine dimethylaminohydrolase 1 in the nitric oxide-cGMP-PKG pathway may lead to decreased angiogenesis. These studies demonstrated that cAMP and cGMP biosynthesis are associated with angiogenesis.

The Hedgehog pathway serves a crucial role in cell proliferation and differentiation in adult organisms, and dysregulation of this pathway is associated with multiple cancer types. Saze et al (55) demonstrated that Hedgehog signaling is a prognostic indicator for patients with GC. Qin et al (56) revealed that Hedgehog signaling is associated with the apoptosis of GC cells. The MAPK signaling pathway serves a vital role in controlling cellular processes, including proliferation, differentiation and apoptosis (57). As a multifunctional growth factor, TGF-β regulates several cellular processes, including proliferation, development, homeostasis of stem cells, enhancing fibrosis and modulating the immune response through its downstream targets (58). TGF family of proteins are involved in tumor initiation and progression, cell proliferation, angiogenesis, epithelial to mesenchymal transition, invasion and inflammation (59). The results of the present GSEA analysis and genome-wide co-expression matrix demonstrated that high expression of GSTM5 was associated with the prognosis of GC. However, the exact mechanisms underlying the function of GSTM5 in the development of GC require to be elucidated.

The present study has certain limitations. First, since the clinical parameters were obtained from a public database, some clinical data were not available such as chemotherapy, smoking and infection with Helicobacter pylori, and therefore, a comprehensive analysis was not performed. Furthermore, the results were obtained with the dataset of the public TCGA database. Therefore, further experiments such as immunohistochemistry for GSTM5 should be designed and performed to validate the prognostic value in GC. In addition, the association between GSTM proteins and GC prognosis was not investigated, since GSTM protein expression data were not available. Despite these limitations, the present study was the first to systematically investigate the association between the expression of GSTM genes and OS in GC, to the best of our knowledge. The present results indicated that high expression of GSTM5 in GC is associated with poor prognosis, suggesting that GSTM5 may be a novel prognostic indicator for GC.

In conclusion, the present study indicated that GSTM5 mRNA expression is associated with unfavorable OS in patients with GC. This suggests that GSTM5 may be a prognostic marker and potential target for GC therapy. Furthermore, the potential mechanisms underlying GSTM5 function in GC were also explored using the GSEA and genome-wide co-expression analyses. To predict the OS of patients with GC, a nomogram composed of GSTM5 and tumor stage was also constructed. However, additional studies are required to further validate the results of the present study.

 Supplementary Material Supporting Data Acknowledgements

Not applicable.

 Funding

The study was supported by the Natural Science Foundation of Guangxi Zhuang Autonomous Region (grant no. 2017AB45153), the Scientific Research and Technology Development Program of Guangxi (grant no. 1598011-4), the Natural Science Foundation of Guangxi (grant no. 2016GXNSFAA380180), the Guangxi Zhuang Autonomous Region Health and Family Planning Commission (grant no. Z2015526) and the Youth Science Foundation of Guangxi Medical University (grant no. GXMUYSF201502).

 Availability of data and materials

The datasets generated and analyzed during the current study are available in TCGA repository, https://portal.gdc.cancer.gov.

 Authors' contributions

JC, YC and BL conceived and designed the study. XL was responsible for the acquisition of data. YM performed the statistical analysis. JW, JL, ZW and SL, analysed and interpreted the data, drafted the manuscript and revised it critically for important intellectual content. All authors read and approved the final manuscript.

 Ethics approval and consent to participate

Not applicable.

 Patient consent for publication

Not applicable.

 Competing interests

The authors declare that they have no competing interests.

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GO and KEGG analysis of GSTM genes. (A) GO enrichment analysis of GSTM genes. (B) KEGG enrichment analysis of GSTM genes. GSTM, glutathione S-transferase Mu; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DAVID, Database for Annotation, Visualization and Integrated Discovery; hsa, Homo sapiens.

Gene and protein interaction networks of GSTM genes. (A) GSTM genes multiple association network integration algorithm. The size of the circle represents the strength of the co-expression. (B) Protein-protein interaction networks. GSTM, glutathione S-transferase Mu.

Co-expression heatmap of GSTM genes. The numbers presented are the r-values (Pearson correlation coefficients). The size of the circle represents the strength of the correlation. GSTM, glutathione S-transferase Mu.

Scatterplots for GSTM gene family expression levels in The Cancer Genome Atlas. Red, high expression; blue, low expression. GSTM, glutathione S-transferase Mu.

Prognostic graphs illustrating the impact of GSTM expression on overall survival in The Cancer Genome Atlas. Kaplan-Meier survival curves for patients with gastric cancer according to median expression of GSTM1-5 (n=351). (A) Survival curves of GSTM1. (B) Survival curves of GSTM2. (C) Survival curves of GSTM3. (D) Survival curves of GSTM4. (E) Survival curves of GSTM5.GSTM, glutathione S-transferase Mu; HR, hazard ratio (95% CI).

Prognostic graphs of GSTM median expression for overall survival generated using the Kaplan-Meier plotter online database. (A) Survival curves of GSTM1. (B) Survival curves of GSTM2. (C) Survival curves of GSTM3. (D) Survival curves of GSTM4. (E) Survival curves of GSTM5. GSTM, glutathione S-transferase Mu; HR, hazard ratio (95% CI).

Nomogram for the association between clinicopathological data and risk score. GSTM, glutathione S-transferase Mu.

Gene set enrichment analysis results of c2 and c5 reference gene sets for higher expression of GSTM5 in gastric cancer tissues. (A) Regulation of cell matrix adhesion. (B) Cell-cell adhesion via plasma membrane adhesion molecules. (C) Angiogenesis. (D) Regulation of cell growth. (E) Negative regulation of cell growth. (F) Regulation of endothelial cell apoptotic process. (G) Negative regulation of leukocyte apoptotic process. (H) Extracellular matrix. (I) Extracellular matrix component. (J) Smoothened signaling pathway. (K) Cell adhesion molecule. (L) Hedgehog signaling pathway. (M) MAPK signaling pathway. (N) TGF-β signaling pathway. (O) Calcium signaling pathway. (P) Pathways in cancer. GSTM, glutathione S-transferase Mu; MAPK, mitogen-activated protein kinase; TGF, transforming growth factor; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; NES, normalized enrichment score; NOM, nominal; FDR, false discovery rate.

Regulation network of GSTM5 and its co-expressed genes in gastric cancer tumor tissue in The Cancer Genome Atlas cohort. GSTM5, glutathione S-transferase Mu 5.

Demographic and clinical data in The Cancer Genome Atlas gastric cancer cohort (n=351).

Variable N Events, n (%) MST, days HR (95% CI) Log-rank P-value
Sex 0.152
  Male 226 99 (43.8) 869 Ref.
  Female 125 44 (35.2) 1,043 0.77 (0.55-1.09)
Age (years) 0.011
  <65 148 50 (33.8) 1,811 Ref.
  ≥65 197 92 (46.7) 779 1.56 (1.12-2.17)
  NA 6
Tumor stage <0.001
  Early (I+II) 156 44 (28.2) 1,811 Ref.
  Advanced (III+IV) 180 89 (49.4) 675 1.92 (1.37-2.70)
  NA 15
Tumor stage <0.001
  I 47 11 (23.4) 2,197 Ref.
  II 109 33 (30.3) 1,686 1.55 (0.78-3.08)
  III 145 67 (46.2) 782 2.38 (1.26-4.51)
  IV 35 22 (62.9) 476 3.81 (1.85-7.86)
  NA 15

MST, median survival time; HR, hazard ratio; Ref., reference; NA, not available.

Analysis of the association between GSTM genes and the risk of death in The Cancer Genome Atlas gastric cancer cohort (n=351).

Gene expression status Median expression N Events, n (%) MST, days Crude HR (95% CI) Crude P-value Adjusted HRa (95% CI) Adjusted P-value a
GSTM1 36.0 0.544 0.877
  Low 176 68 (38.6) 869 Ref. Ref.
  High 175 75 (42.9) 1,095 1.11 (0.80-1.54) 0.97 (0.69-1.38)
GSTM2 349.7 0.257 0.263
  Low 176 66 (37.5) 2,100 Ref. Ref.
  High 175 77 (44.0) 869 1.2 (0.87-1.68) 1.22 (0.86-1.71)
GSTM3 2105.3 0.391 0.247
  Low 176 70 (39.8) 881 Ref. Ref.
  High 175 73 (41.7) 940 1.15 (0.83-1.60) 1.23 (0.87-1.74)
GSTM4 2570.1 0.006 0.067
  Low 176 84 (47.7) 712 Ref. Ref.
  High 175 59 (33.7) 1,686 0.63 (0.45-0.87) 0.72 (0.50-1.02)
GSTM5 107.5 0.021 0.027
  Low 176 64 (36.4) 1,153 Ref. Ref.
  High 175 79 (45.9) 794 1.47 (1.06-2.04) 1.48 (1.05-2.08)

Adjusted for age and tumor stage. GSTM, glutathione S-transferase Mu; MST, median survival time; HR, hazard ratio; Ref., reference.

Stratified analysis of the association between GSTM5 expression levels and overall survival in The Cancer Genome Atlas gastric cancer cohort (n=351).

Variable Total, n Low GSTM5, n High GSTM5, n HR (95% CI) Log-rank P-value
Sex
  Male 226 113 113 1.59 (1.07-2.36) 0.020
  Female 125 63 62 1.06 (0.58-1.91) 0.860
Age, years
  <65 148 74 74 1.86 (1.07-3.25) 0.030
  ≥65 197 99 98 1.11 (0.74-1.688) 0.603
  NA 6
Stage
  Early 156 78 78 1.48 (0.82-2.68) 0.189
  Advanced 180 90 90 1.34 (0.88-2.04) 0.164
  NA 15
Stage
  I 47 24 23 1.31 (0.40-4.29) 0.651
  II 109 55 54 1.52 (0.77-3.02) 0.217
  III 145 73 72 1.49 (0.92-2.42) 0.098
  IV 35 18 17 1.15 (0.49-2.68) 0.744
  NA 15

GSTM5, glutathione S-transferase Mu 5; HR, hazard ratio; NA, not available.

Stratified analysis of the association between GSTM5 expression levels and overall survival in the Kaplan-Meier plotter gastric cancer cohort (n=875).

Variable Total, n Low GSTM5, n High GSTM5, n HR (95% CI) Log-rank P-value
Sex
  Female 236 120 116 1.87 (1.31-2.68) <0.001
  Male 544 275 269 1.84 (1.48-2.29) 2.4×10−8
  NA 95
Stage
  I 67 34 33 3.02 (0.96-9.55) 0.048
  II 140 71 69 2.20 (1.17-4.15) 0.012
  III 305 152 153 1.47 (1.11-1.96) 0.008
  IV 148 74 74 1.41 (0.96-2.08) 0.075
  NA 215
Lauren classification
  Intestinal 320 161 159 2.42 (1.74-3.36) 7.1×10−8
  Diffuse 241 121 120 1.95 (1.37-2.76) <0.001
  Mixed 32 16 16 4.26 (1.34-13.55) 0.007
  NA 282
Differentiation
  Poor 165 82 83 0.71 (0.47-1.05) 0.085
  Moderate 67 34 33 1.01 (0.52-1.94) 0.978
  Well 32 16 16 2.04 (0.84-4.94) 0.106
  NA 611
Treatment
  Surgery alone 380 190 190 1.81 (1.35-2.43) 5.9×10−5
  5-Fu based adjuvant 152 76 76 0.86 (0.61-1.22) 0.389
  Other adjuvant 76 38 38 7.37 (2.15-25.21) <0.001
  NA 267
HER2 status
  Negative 532 266 266 1.93 (1.53-2.44) 1.3×10−8
  Positive 343 172 171 1.22 (0.94-1.58) 0.139

GSTM5, glutathione S-transferase Mu 5; HR, hazard ratio; NA, not available; HER2, human epidermal growth factor receptor 2; 5-Fu, fluorouracil.

GO term enrichments of co-expressed genes of glutathione S-transferase Mu 5 in gastric cancer.

Term Count P-value Genes
GO:0007155~cell adhesion 22 1.97×10−5 SELP, SVEP1, CYP1B1, ATP1B2, MPDZ, IGFBP7, MCAM, EMILIN1, ITGA9, PGM5, SRPX, S1PR1, COL19A1, ITGA7, RHOB, TGFB1I1, MFAP4, BOC, PARVA, FEZ1, AOC3, SPON1
GO:0016337~single organismal cell-cell adhesion 10 3.83×10−5 COL14A1, LIMS2, COL19A1, FOXF1, PIP5K1C, NLGN2, JAM2, COL8A2, NTN1, NEGR1
GO:0005925~focal adhesion 18 3.24×10−4 TLN1, LIMS2, PDLIM7, FHL1, FERMT2, PIP5K1C, CSRP1, MCAM, FLNA, ANXA6, TNS2, TNS1, PGM5, LAYN, ILK, RHOB, TGFB1I1, PARVA
GO:0010811~positive regulation of cell-substrate adhesion 5 0.003 SMOC2, FOXF1, CCDC80, ABI3BP, EMILIN1
GO:0001525~angiogenesis 11 0.004 CYP1B1, S1PR1, MEOX2, RHOB, TMEM100, TNFSF12, MCAM, COL8A2, MEIS1, JAM3, MMRN2
GO:0061364~apoptotic process involved in luteolysis 2 0.048 SLIT2, SLIT3
GO:0031012~extracellular matrix 25 1.88×10−10 LTBP1, IGFBP7, DCN, ABI3BP, MMRN2, OGN, ILK, COL8A2, MYOC, SPON1, MGP, CPXM2, SOD3, FLNA, EMILIN1, PRELP, FBLN1, COL14A1, SERPINF1, SFRP1, FBLN5, TGFB1I1, MFAP4, SSC5D, CLEC14A
GO:0030198~extracellular matrix organization 18 2.11×10−8 RECK, ELN, CCDC80, DCN, ABI3BP, EMILIN1, ITGA9, SMOC2, FBLN1, COL14A1, COL19A1, CRISPLD2, FOXF1, FBLN5, ITGA7, JAM2, JAM3, COL8A2
GO:0005578~proteinaceous extracellular matrix 21 2.62×10−8 LTBP1, PODN, SPARCL1, ADAMTSL3, ELN, MGP, SLIT2, PRELP, SLIT3, EMILIN1, OGN, SMOC2, FBLN1, COL14A1, COL19A1, SFRP1, CRISPLD2, FBLN5, COL8A2, MYOC, SPON1
GO:0005576~extracellular region 52 6.02×10−6 NXPH3, TLN1, A2M, LTBP1, FGF7, CXORF36, IGFBP7, TNFSF12, OLFML1, OGN, ST3GAL3, RSPO1, RSPO3, ITIH5, LGI4, GHR, SERPING1, SLIT2, FLNA, SLIT3, PRELP, CHRDL1, PTGDS, SERPINF1, MFAP4, ELN, C1R, DCN, C1S, C1QTNF7, METTL24, ANGPTL7, IL17B, CRISPLD2, COL8A2, VSTM4, SVEP1, EFEMP2, PTGFR, FRZB, NTN1, SOD3, PLAC9, EMILIN1, FBLN1, COL14A1, COL19A1, CCL14, SFRP1, FAM198A, LCN6, FBLN5
GO:0005201~extracellular matrix structural constituent 8 1.02×10−4 FBLN1, COL14A1, COL19A1, EFEMP2, ELN, MGP, COL8A2, PRELP
GO:0050840~extracellular matrix binding 4 0.008 SPARCL1, ELN, DCN, SSC5D
GO:0005886~plasma membrane 92 0.002 RHOJ, SLC9A9, GYPC, CADM3, TLN1, JPH2, ATP1B2, TACR2, ADCY5, GRIK5, NCS1, TNFSF12, FRRS1L, DMPK, EDNRA, ST6GALNAC6, S1PR1, SLC2A4, NMUR1, ILK, GUCY1A3, RHOB, ATP8B2, TMEM100, FAM129A, NEGR1, BOC, GHR, RAMP3, KCNMA1, RECK, AR, MAGI2, ACKR1, MRGPRF, COLEC12, FLNA, SLIT2, TNS2, CHRM2, ROR2, GUCY1B3, CLIP3, JAM2, JAM3, AOC3, PARVA, FXYD1, ABCA8, LIMS2, CYSLTR1, ARHGEF25, FHL1, GNG11, KCNA5, PLPP1, FXYD6, KCNMB1, RGMA, FAT3, PLIN4, PKD2, KLHL41, PRIMA1, EHD2, TRPC1, SELP, VSTM4, GNAO1, KLF9, EPM2A, MAP1B, NPR1, NPR2, ATP1A2, MAPK10, MCAM, PTGFR, ITPR1, ITGA9, ABCC9, TMEM47, PDE2A, SFRP1, PLSCR4, P2RY14, BNC2, ITGA7, SCN4B, APBB1, HTR2A, FEZ1
GO:0030308~negative regulation of cell growth 11 2.75×10−5 RERG, SFRP1, DACT3, CRYAB, FHL1, NPR1, WFDC1, FRZB, APBB1, SLIT2, SLIT3
GO:0008285~negative regulation of cell proliferation 19 8.51×10−5 AR, MAGI2, CYP1B1, PODN, ADARB1, NDN, IGFBP7, ZEB1, FRZB, PLPP1, KANK2, SLIT3, RERG, TNS2, SFRP1, SPEG, PKD2, ROR2, TGFB1I1
GO:0030336~negative regulation of cell migration 8 8.89×10−4 RECK, ADARB1, PODN, CYP1B1, MAGI2, SFRP1, RHOB, SLIT2
GO:0030178~negative regulation of Wnt signaling pathway 5 0.010 BARX1, SFRP1, DACT3, NFATC4, FRZB
GO:0035385~roundabout signaling pathway 3 0.016 OGN, SLIT2, SLIT3
GO:0007229~integrin-mediated signaling pathway 6 0.022 ITGA9, FBLN1, FERMT2, ITGA7, ILK, ADAM33
GO:0007224~smoothened signaling pathway 5 0.026 EVC, FOXF1, CC2D2A, ROR2, BOC

GO, gene ontology.

Kyoto Encyclopedia of Genes and Genomes term enrichments of co-expressed genes of glutathione S-transferase Mu 5 in gastric cancer.

Term Count P-value Genes
hsa04022: cGMP-PKG signaling pathway 17 3.04×10−9 KCNMA1, ATP1B2, ADCY5, MRVI1, NPR1, NPR2, ATP1A2, KCNMB1, ITPR1, MYL9, EDNRA, PDE2A, PLN, PDE5A, GUCY1A3, NFATC4, GUCY1B3
hsa04024: cAMP signaling pathway 10 0.004 EDNRA, FXYD1, ATP1B2, CHRM2, ADCY5, PLN, NPR1, MAPK10, ATP1A2, MYL9
hsa04020: Calcium signaling pathway 9 0.008 EDNRA, CYSLTR1, TACR2, CHRM2, PLN, PDE1A, PTGFR, ITPR1, HTR2A
hsa04510: Focal adhesion 9 0.017 ITGA9, TLN1, ITGA7, ILK, PPP1R12C, MAPK10, FLNA, PARVA, MYL9
hsa04514: Cell adhesion molecules (CAMs) 7 0.026 ITGA9, SELP, CADM3, NLGN2, JAM2, NEGR1, JAM3

Hsa, Homo sapiens.