Nomograms combined with SERPINE1-related module genes predict overall and recurrence-free survival after curative resection of gastric cancer: a study based on TCGA and GEO data.

BACKGROUND: Serpin peptidase inhibitor, clade E, member 1 (SERPINE1) has been investigated as an oncogene and potential biomarker in several cancers, including gastric cancer (GC). This study aimed to investigate SERPINE1 expression and its diagnostic and prognostic value by analyzing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases.
METHODS: A meta-analysis was performed to investigate SERPINE1 expression levels in GC tissues and adjacent normal tissues. Gene set enrichment, multi experiment matrix (MEM), and protein-protein interaction (PPI) network analyses were performed to identify the most enriched signaling pathways and SERPINE1-related module genes. A Cox regression model was used to develop a nomogram that was able to predict the overall survival (OS) and recurrence-free survival (RFS) of individual patients.
RESULTS: Meta-analyses revealed an elevated trend in SERPINE1 expression levels in TCGA [standard mean difference (SMD) =0.95; 95% confidence interval (CI), 0.53-1.36; P<0.001]. The diagnostic meta-analysis results indicated that the area under the curve (AUC) of the summary receiver operating characteristic (SROC) was 0.80 (95% CI, 0.77-0.84). The factors identified to predict OS were age ≥60 years [hazard ratio (HR), 2.14; 95% CI, 1.45-3.16; P<0.01], R2 margins (HR, 2.70; 95% CI, 1.41-5.14; P<0.05), lymph node-positive proportion (HR, 3.38; 95% CI, 2.03-5.63; P<0.001), patient tumor status (HR, 3.33; 95% CI, 2.28-4.87; P<0.001), and OS risk score (HR, 2.72; 95% CI, 1.82-4.05; P<0.05). The following variables were associated with RFS: male sex (HR, 2.55; 95% CI, 1.46-4.45; P<0.01), R2 margins (HR, 13.08; 95% CI, 4.26-40.15; P<0.001), lymph node-positive proportion (HR, 2.55; 95% CI, 1.20-5.45; P<0.05), and RFS risk score (HR, 2.70; 95% CI, 1.82-4.06; P<0.001). The discriminative ability of the final model for OS and RFS was assessed using C statistics (0.755 for OS and 0.745 for RFS).
CONCLUSIONS: SERPINE1 was upregulated in GC, showed a high diagnostic value, and was associated with poorer OS and RFS. The OS and RFS risk for an individual patient could be estimated using these nomograms, which could lead to individualized therapeutic choices. 2020 Translational Cancer Research. All rights reserved.

Introduction

Gastric cancer (GC) is the fourth most common malignancy and ranks as the second leading cause of cancer death worldwide (1). The highest GC incidence and mortality rates occur in East Asia, especially in China. Like other cancers, prognosis is mainly dependent upon tumor stage. Unfortunately, most GC patients are diagnosed at an advanced stage and the 5-year survival rate is significantly lower than that of patients diagnosed at an early stage (2). Although various biomarkers including carcinoembryonic antigen (CEA), alpha-fetoprotein (AFP), cancer antigen 125 (CA125), and carbohydrate antigen 199 (CA199) have been used in clinical practice, their reliability in the identification of early stage GC remains unsatisfactory (3). Therefore, the identification of reliable biomarkers related to tumor diagnosis, treatment, and prognostic evaluation is urgently needed.

Serpin peptidase inhibitor, clade E, member 1 (SERPINE1), also known as endothelial plasminogen activator inhibitor (PAI), serpin E1, PLANH1, and PAI-1, encodes PAI-1, which is a primary member of the serpin superfamily and functions as a principal inhibitor of tissue plasminogen activator (tPA) and urokinase plasminogen activator (uPA). Although previous studies have mainly focused on the role of the SERPINE1 gene expression product PAI-1 in thrombosis, vascular diseases, obesity, and metabolic syndrome, accumulating evidence has highlighted the role of SERPINE1 in cancer progression (4). SERPINE1 has been identified as a key gene associated with prognosis by integrated bioinformatics analysis (5). SERPINE1 is generally accepted to not only play a key role in oncogenesis but also to serve as a new prognostic factor in certain cancers including breast cancer and head and neck squamous cell carcinoma (6,7). However, the molecular mechanism of SERPINE1 in GC, especially the vital signaling pathways involved in GC development, remains unclear. Furthermore, although surgical resection is a GC treatment, patients have a high risk of local relapse or distant metastasis after gastrectomy (8). Therefore, accurate data on the prognosis of postoperative GC patients are critical for treating physicians when making decisions regarding adjuvant treatment and follow-up frequency. Although the American Joint Committee on Cancer (AJCC) tumor-node-metastases (TNM) system, which has been widely used in clinical practice, may be helpful for the general prediction of GC survival, its use as a risk stratification system may not be suitable for predicting the survival and recurrence of an individual patient. The development of a reliable predictive model that incorporates factors associated with survival and recurrence based on postoperative clinicopathologic data combined with biological markers is urgently needed. A nomogram that can be widely and easily used could not only provide individualized, evidence-based, and highly accurate risk estimations, but could also aid in management-related decision making.

Currently, microarray technology combined with bioinformatics analysis has provided an opportunity to comprehensively analyze the changes in gene transcription and posttranscriptional regulation during GC development and progression. Therefore, a meta-analysis was performed to evaluate SERPINE1 expression in GC and normal gastric tissues based on the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Furthermore, SERPINE1-related biological pathways involved in GC were detected using gene set enrichment analysis (GSEA) and multi experiment matrix (MEM) analysis. A nomogram combined with SERPINE1-related module genes was established to effectively predict the overall survival (OS) and recurrence-free survival (RFS) of patients after GC resection.

Methods

SERPINE1 expression profile mining

The gene expression data of gastric adenocarcinoma and corresponding clinical information were downloaded from the official TCGA website (http://cancergenome.nih.gov) in August 2019. These data included the SERPINE1 expression levels from 343 GC tissues and 30 tumor-adjacent normal control tissues. SERPINE1 values were carefully checked for each sample and values below single counts were treated as missing values. Gene expression level was normalized using the EdgeR package in R (version 3.6.1) and log2-transformed for further analysis. The clinical parameters of GC patients that were relevant to SERPINE1 were extracted and included age at the initial pathologic diagnosis, sex, anatomic location (cardia, fundus, antrum, or gastroesophageal junction), histologic grade [defined as poorly (G1), moderately (G2), or well-differentiated (G3)], resection margin status [negative (R0), microscopically positive (R1), or positive to the naked eye (R2)], lymph node-positive rate (defined as the number of lymph nodes that were positive by hematoxylin and eosin (HE) staining/the number of examined lymph nodes), patient tumor status (with tumor or tumor-free), and TNM stage. The relationship between SERPINE1 and the clinicopathological parameters in GC were determined based on TCGA database data. Then, the clinical diagnostic value of SERPINE1 was analyzed using a receiver operating characteristic (ROC) curve.

Meta-analysis

To strengthen the reliability of the results, all included datasets were combined to perform a meta-analysis using STATA 12.0 (STATA Corp., College Station, TX, USA). We screened GC microarray datasets from the GEO database (http://www.ncbi.nlm.nih.gov/gds/) up until August 2019 to perform a meta-analysis. The following keywords were used: gastric, GC, gastric carcinoma, stomach adenocarcinoma, SERPINE1, PAI, and PAI-1. Eligible microarrays were included if they met the following standards: (I) each dataset included GC tissues and peritumoral tissues and more than 10 samples were included in the study; (II) the expression profiling data of SERPINE1 from the GC case and their paired tumor-adjacent tissues controls were provided or could be calculated; and (III) the study subjects were human. Datasets with expression profiling data from animals or cell lines, or with no SERPINE1 expression profiling data were excluded. The expression data were log2-transformed. The SERPINE1 expression mean value, standard deviation (SD), and sample size of the tumor and control groups were calculated using SPSS version 24.0 (IBM Corp., Armonk, NY, USA). Continuous outcomes obtained from GEO datasets were estimated as the standard mean difference (SMD) with a 95% confidence interval (CI). Effect sizes were pooled using a random- or fixed-effects model. Heterogeneity across studies was assessed with I2; when I2<50%, a fixed-effects model was used and when I2≥50%, a random-effects model was selected. The number of true-positives (tps), true-negatives (tns), false-positives (fps), and false-negatives (fns) was extracted from the following basic formulae:

or

To calculate the incidence. A P value <0.05 was considered indicative of a statistically significant difference.

Gene set enrichment analysis

To identify the potential Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways underlying the influence of SERPINE1 expression on GC prognosis, GSEA was performed to detect the potential differentially expressed SERPINE1 KEGG pathways SERPINE1 between the high expression and low expression groups. The number of gene set permutations was 1,000 times for each analysis. SERPINE1 expression level SERPINE1was considered a phenotype label. Gene sets with a nominal P value <0.05 and a false discovery rate (FDR) <0.05 were considered significantly enriched.

Genes co-expressed with SERPINE1

Adler developed the MEM query engine (https://biit.cs.ut.ee/mem/) that detects co-expressed genes in large platform-specific microarray collections (9). MEM was used to identify genes that were co-expressed with SERPINE1 in large platform-specific microarray collections. First, SERPINE1 was input as a single query gene that acted as the template pattern for the co-expression search. Two probe sets were linked to the gene; the first probe set was chosen for further analysis. Current (24.02.12) was selected as the search database and H. sapiens was chosen as the organism filter. The other parameters were set as follows: distance measure, Pearson correlation distance; rank aggregation method, beta MEM method was used to obtain P values for selected ranks; set output limit, 3,000; gene filters, remove unknown genes and ambiguous genes; and dataset filter, 0.9 was set as the StDev threshold for query genes.

SERPINE1-related module screening from the protein-protein interaction (PPI) network and gene ontology (GO) annotation analysis

To investigate the central interactions between SERPINE1 and other genes enriched in overlapping KEGG pathways, a PPI network was constructed using the STRING online tool (https://string-db.org). The resulting network contained a subset of proteins that physically interacted with at least one other list member. Cytoscape was used to visualize this network, and the Molecular Complex Detection (MCODE) algorithm was then applied to this network to identify the SERPINE1-related module. GO enrichment analysis was conducted using R software to reveal the function of SERPINE1-related module genes. To examine the potential prognostic value of the module genes, the UALCAN online tool (http://ualcan.path.uab.edu/analysis.html) was then used to investigate the influence of SERPINE1-related module genes on the OS of GC patients. According to univariate survival analysis, module genes with P<0.05 were considered candidate prognostic module genes and were included in the multivariate Cox proportional hazards regression. To identify independent predictors that significantly contributed to OS or RFS, we used the lowest value of the Akaike information criterion (AIC) with respect to module gene selection and the established MRS (module gene risk score) values. The risk score of each patient was calculated to predict the OS and RFS of GC patients and the regression coefficients of the multivariate Cox regression model were used to weight the expression level of each module gene in the prognostic classifier:

In order to investigate the relationship between risk scores and survival, patients were divided into high-risk and low-risk groups according to the optimum cut-off values obtained from X-tile plots version 3.6.1 (X-TILE, Yale University School of Medicine, New Haven, CT, USA).

Statistical analysis

The mean ± SD was calculated using SPSS to estimate the SERPINE1 expression level in each dataset. SERPINE1 expression was compared between normal gastric tissues and GC by Student’s t-test. A Student’s t-test was also used to evaluate the relationships between SERPINE1 expression and clinicopathological parameters. One-way analysis of variance (ANOVA) was used to compare mean values among subgroups. A ROC curve was generated to evaluate the diagnostic value of SERPINE1 expression using SPSS, and the area under the curve (AUC) was calculated to evaluate the diagnostic value. Patients were divided into two groups (high and low SERPINE1 expression) according to the threshold value identified from the ROC curve. Survival curves were plotted using the Kaplan-Meier method and compared using the log-rank test. A multivariate Cox proportional hazards regression model was used to identify the independent prognostic factors for OS. Univariate and multivariate Cox proportional hazards regression analyses were performed using R software (v.3.6.1). The Kaplan-Meier method was used to compare the survival between high- and low-SERPINE1 expression patients. The hazard ratio (HR) and 95% CI were calculated to identify protective factors (HR <1) or risk factors (HR >1). A correlation matrix was used to evaluate all variables for collinearity and interaction between terms; no significant collinearity or interactions were found. All variables significantly associated with OS were candidates for stepwise multivariate analysis. A nomogram was formulated based on multivariate Cox regression analysis results using the RMS package of R version 3.6.1 (http://www.r-project.org/). Nomogram predictive performance was measured by C statistics and calibration with 1,000 bootstrap samples to decrease the overfit bias (10). The net reclassification improvement (NRI) was calculated to estimate the overall improvement in the reclassification of patients between the two models using the nricens package in R (parameters: t0, 1,095 days; nIter, 1,000). Egger’s test was performed for all datasets to assess publication bias (11-16). In all analyses, P<0.05 was considered statistically significant. Data analysis was conducted from August 1 to October 24, 2019.

Results

SERPINE1 was overexpressed in GC tissues

As shown in , TCGA SERPINE1 expression data analysis revealed that SERPINE1 was significantly overexpressed in GC (11.99±1.52) compared with adjacent, nontumor tissue samples (9.47±1.65, P<0.001). SERPINE1 expression level SERPINE1 in stage T2/T3/T4 GC tissues was significantly higher than that in stage T1 tissues (P<0.001), and the expression level of SERPINE1 in deceased patients was significantly higher than that in surviving patients (P<0.001). These results suggested that SERPINE1 was overexpressed in GC and related to both T stage and survival.

Table 1

Expression of SERPINE1 in GC based on TCGA database

Clinicopathological feature	N	SERPINE1 expression (log2)	T or F value	P value
Tissue type			–8.643	0.000*
Normal	30	9.47±1.65
GC	343	11.99±1.52
Age			0.138	0.089
≤60	110	12.01±1.53
>60	233	11.98±1.52
Sex			0.768	0.443
Female	127	12.07±1.55
Male	216	11.94±1.50
Histologic grade			2.974	0.052
G1	8	11.08±2.03
G2	128	11.82±1.50
G3	200	12.12±1.49
Anatomic location			0.875	0.454
Antrum	123	11.85±1.50
Cardia	45	12.26±1.70
Fundus	122	12.04±1.35
Gastroesophageal junction	36	11.95±1.80
Resection margin			1.733	0.179
R0	274	11.90±1.51
R1	11	12.73±1.91
R2	14	12.19±1.42
T stage			6.267	0.000*
T1	19	10.57±1.99
T2	74	12.02±1.38
T3	157	12.00±1.54
T4	85	12.19±1.36
N stage			0.841	0.472
N0	102	11.83±1.55
N1	90	11.95±1.47
N2	72	12.17±1.62
N3	65	11.99±1.51
M stage			–0.089	0.929
M0	318	11.98±1.52
M1	23	11.96±1.57
TNM stage			1.681	0.171
I	51	11.53±1.74
II	105	12.04±1.51
III	139	12.07±1.44
IV	35	12.03±1.49
Survival status			3.933	0.000*
Dead	134	12.37±1.61
Alive	186	11.71±1.39
Recurrence			1.577	0.116
Yes	60	12.24±1.48
No	205	11.88±1.53

* indicate the clinical variables are related to SERPINE1 expression. SERPINE1 expression values are expressed as the mean ± SD. GC, gastric cancer; TCGA, The Cancer Genome Atlas; N, number; T, Student’s t-test; F, one-way ANOVA; ANOVA, analysis of variance; TNM, tumor-node-metastases; SD, standard deviation.

In addition to evaluating the diagnostic value of SERPINE1, we generated a ROC curve using TCGA expression data from GC patients and healthy individuals (). The ROC AUC was 0.876, which was indicative of a high diagnostic value. Subgroup analysis showed the diagnostic value of SERPINE1 expression in different GC stages, with AUC values of 0.800, 0.878, 0.891, and 0.897 for stages I, II, III, and IV, respectively ().

Figure 1

Diagnosis value of SERPINE1 expression in GC. (A) ROC curve for SERPINE1 expression in normal gastric tissue and GC; (B,C,D,E) subgroup analysis for stage I, II, III, and IV GC. GC, gastric cancer; ROC, receiver operating characteristic; AUC, area under the curve.

To strengthen the reliability of the results, a meta-analysis of GEO and TCGA database data was performed. The GEO dataset included in the following meta-analysis is summarized in . In total, 631 GC and 314 normal (tumor-adjacent tissues) samples were included. A significant difference was identified in SERPINE1 expression SERPINE1 between GC and normal tissues and the heterogeneity among the individual datasets was high (I2=80.5%, P<0.001; ); thus, a random-effects model was selected. The pooled SMD of the seven studies was 0.95 (95% CI, 0.53–1.36). This result further suggested that SERPINE1 was overexpressed in GC tissues. Publication bias assessment yielded a value of P=0.189. This result suggested that publication bias was absent in the current study.

Table 2

Characteristics of SERPINE1 gene expression profiling datasets obtained from GEO

Accession	Platform	Country	Submission year	Number of normal samples	SERPINE1 expression (log2) of normal samples	Number of tumor samples	SERPINE1 expression (log2) of tumor samples
GSE2685	GPL80	Japan	2005	8	5.59±0.75	12	5.79±0.69
GSE19826	GPL570	China	2010	12	8.17±1.03	12	8.89±0.92
GSE27342	GPL5175	USA	2011	80	6.75±1.96	80	7.56±2.55
GSE29272	GPL96	USA	2011	134	7.10±0.61	134	8.11±1.12
GSE56807	GPL5175	China	2014	5	5.87±0.69	5	7.69±1.33
GSE63089	GPL5175	China	2014	45	6.59±1.07	45	7.71±1.19

SERPINE1 expression values are expressed as the mean ± SD. GEO, Gene Expression Omnibus; SD, standard deviation.

Figure 2

Meta-analysis of SERPINE1 as a GC biomarker based on GEO and TCGA datasets. (A) Forest plot of studies evaluating SMD of SERPINE1 expression between GC and control groups (random-effects model); (B) the SROC curve for the diagnostic accuracy assessment of SERPINE1 in GC; (C) pre- and post-test probability of the included studies; (D) publication bias of the included studies. 1/root (ESS) indicated the inverse root of ESS. Each circle represented an included study. GC, gastric cancer; GEO, Gene Expression Omnibus; TCGA, The Cancer Genome Atlas; SMD, standard mean difference; SROC, summary receiver operating characteristic; ESS, effective sample sizes; CI, confidence interval; SENS, sensitivity; SPEC, specificity; AUC, area under the curve.

SERPINE1 showed a surprising diagnostic value in TCGA dataset. To further identify the prognostic value of SERPINE1, a diagnostic meta-analysis was performed. As shown in , the AUC of the summary ROC (SROC) was 0.80 (0.77–0.84), which indicated that SERPINE1 had a moderate diagnostic value in GC. The pooled sensitivity and specificity of SERPINE1 was 0.69 (0.60–0.77) and 0.78 (0.70–0.84), respectively. In addition, the DLR-positive and DLR-negative values were 3.08 (2.22–4.27) and 0.40 (0.30–0.53), respectively. The diagnostic score and odds ratio were 2.04 (1.51–2.57) and 7.69 (4.52–13.09), respectively. The pretest probability was 20% when the positive and negative pretest probabilities were 44% and 9% (), respectively. Additionally, no significant publication bias was found (P=0.821, ).

Prognostic value of SERPINE1 in GC

We further assessed the relationship between SERPINE1 expression and GC patient survival. Our data suggested that GC patients with high SERPINE1 expression had poorer OS and RFS than those with low SERPINE1 expression ().

Figure 3

Kaplan-Meier curve for SERPINE1 expression in TCGA GC cohort. (A) GC patients with high SERPINE1 expression (n=163) had a poorer OS than those with low SERPINE1 expression (n=157); (B) GC patients with high SERPINE1 expression had a poorer RFS than those with low SERPINE1 expression. TCGA, The Cancer Genome Atlas; GC, gastric cancer; OS, overall survival; RFS, recurrence-free survival.

SERPINE1-related signaling pathways based on GSEA

To identify the signaling pathways engaged in GC, we performed a GSEA to compare the low- and high-SERPINE1 expression data sets. GSEA revealed significant differences (FDR <0.05, nominal P value <0.05) in the enrichment of the Molecular Signature Database (MSigDB) collection (c2.cp.kegg.v7.0 symbols). As shown in , we selected a total of 42 significantly enriched signaling pathways. The top four differentially enriched pathways in the SERPINE1-high expression phenotype group were the focal adhesion, extracellular matrix (ECM) receptor interaction, leukocyte transendothelial migration, and cytokine-cytokine receptor interaction signaling pathways, indicating the potential role of SERPINE1 in GC development ().

Table S1

GSEA KEGG pathway enrichment in the SERPINE1-high expression phenotype group

KEGG pathway	Size	NES	NOM P value	FDR q value
Focal adhesion	199	2.50	0.000	0.000
ECM receptor interaction	83	2.43	0.000	0.000
Leukocyte transendothelial migration	115	2.35	0.000	0.000
Cytokine receptor interaction	244	2.19	0.000	0.001
NOD like receptor signaling pathway	62	2.12	0.000	0.001
Regulation of actin cytoskeleton	210	2.10	0.000	0.003
Pathways in cancer	325	2.10	0.000	0.002
Bladder cancer	42	2.09	0.000	0.002
Axon guidance	129	2.09	0.000	0.002
MAPK signaling pathway	266	2.07	0.000	0.003
Prion diseases	35	2.07	0.000	0.002
Leishmania infection	69	2.05	0.000	0.003
Hematopoietic cell lineage	83	2.04	0.002	0.003
Chemokine signaling pathway	185	2.04	0.000	0.003
Cell adhesion molecules cams	130	2.01	0.002	0.004
Glycosaminoglycan biosynthesis chondroitin sulfate	22	1.97	0.000	0.006
Glycosaminoglycan biosynthesis heparan sulfate	26	1.97	0.002	0.006
TGF beta signaling pathway	85	1.97	0.000	0.006
Renal cell carcinoma	70	1.97	0.000	0.005
Complement and coagulation cascades	68	1.96	0.000	0.006
Jak stat signaling pathway	140	1.96	0.000	0.006
Toll like receptor signaling pathway	90	1.89	0.006	0.012
Natural killer cell mediated cytotoxicity	119	1.89	0.008	0.011
Dilated cardiomyopathy	90	1.89	0.008	0.012
Neurotrophin signaling pathway	126	1.85	0.004	0.016
Melanoma	71	1.84	0.000	0.018
Hypertrophic cardiomyopathy (HCM)	83	1.82	0.008	0.020
Pancreatic cancer	70	1.82	0.006	0.020
Small cell lung cancer	84	1.82	0.008	0.020
Glycosaminoglycan biosynthesis keratan sulfate	15	1.81	0.004	0.021
Gap junction	87	1.78	0.002	0.027
Glycosaminoglycan degradation	21	1.78	0.008	0.027
Fc gamma r mediated phagocytosis	95	1.77	0.006	0.028
Epithelial cell signaling in helicobacter pylori infection	68	1.75	0.002	0.032
mTOR signaling pathway	51	1.75	0.014	0.033
Arrhythmogenic right ventricular cardiomyopathy	74	1.74	0.015	0.034
Glycosphingolipid biosynthesis ganglio series	15	1.74	0.010	0.034
Hedgehog signaling pathway	56	1.72	0.013	0.038
Graft versus host disease	37	1.71	0.030	0.042
Endocytosis	180	1.69	0.004	0.047
Acute myeloid leukemia	57	1.68	0.010	0.050
Chronic myeloid leukemia	73	1.67	0.025	0.049

Gene sets with NOM P values <0.05 and FDR q values <0.25 were considered significantly enriched. GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; NES, normalized enrichment score; NOM, nominal; FDR, false discovery rate.

Figure 4

Enrichment plots from GSEA. GSEA results showing the focal adhesion (A), ECM receptor interaction (B), leukocyte transendothelial migration (C), and cytokine-cytokine receptor interaction (D) signaling pathways that were differentially enriched in the SERPINE1 high SERPINE1 expression phenotype group. GSEA, gene set enrichment analysis; ECM, extracellular matrix.

Genes co-expressed with SERPINE1 and bioinformatics analysis

A total of 1,769 genes that were co-expressed with SERPINE1 were extracted from the MEM database. To investigate the pathways of SERPINE1 and its co-expressed genes, 1,769 co-expressed genes were selected and subjected to in silico analysis using the STRING online database. KEGG pathway enrichment analysis revealed a significant enrichment of SERPINE1 co-expressed genes in a total of 200 pathways (). To more accurately identify SERPINE1-involved KEGG pathways, the pathways extracted from the GSEA and SERPINE1 co-expressed genes in KEGG functional annotation were overlapped and 23 pathways were identified for further analysis (). A total of 1,401 genes were identified as GSEA gene set members involved in the 23 overlapping pathways.

Table S2

KEGG pathways enriched by genes MEM co-expressed with SERPINE1

KEGG pathways	Description	Count	Gene set count	FDR
hsa04010	MAPK signaling pathway	274	293	4.62E–170
hsa05200	Pathways in cancer	325	515	2.12E–167
hsa04060	Cytokine-cytokine receptor interaction	236	263	7.22E–143
hsa04810	Regulatiin cytoskeleton	198	205	5.42E–123
hsa04151	PI3K-Akt signaling pathway	226	348	1.23E–115
hsa04510	Focal adhesion	187	197	3.71E–115
hsa04144	Endocytosis	181	242	3.53E–99
hsa04062	Chemokine signaling pathway	155	181	1.75E–90
hsa04014	Ras signaling pathway	167	228	2.51E–90
hsa04015	Rap1 signaling pathway	149	203	2.01E–80
hsa04630	Jak-STAT signaling pathway	133	160	2.52E–76
hsa04514	Cell adhesion molecules (CAMs)	127	139	3.33E–76
hsa04722	Neurotrophin signaling pathway	112	116	8.87E–69
hsa04670	Leukocyte transendothelial migration	109	112	3.56E–67
hsa05165	Human papillomavirus infection	157	317	6.93E–67
hsa04650	Natural killer cell mediated cytotoxicity	109	124	3.65E–64
hsa05167	Kaposi's sarcoma-associated herpesvirus infection	123	183	3.72E–63
hsa05205	Proteoglycans in cancer	125	195	1.67E–62
hsa05166	HTLV-I infection	134	250	4.58E–60
hsa05145	Toxoplasmosis	97	109	1.93E–57
hsa04921	Oxytocin signaling pathway	104	149	1.76E–54
hsa05414	Dilated cardiomyopathy (DCM)	87	88	4.92E–54
hsa04666	Fc gamma R-mediated phagocytosis	85	89	5.30E–52
hsa05410	Hypertrophic cardiomyopathy (HCM)	81	81	1.40E–50
hsa05418	Fluid shear stress and atherosclerosis	95	133	1.99E–50
hsa04660	T cell receptor signaling pathway	86	99	2.05E–50
hsa04659	Th17 cell differentiation	86	102	1.03E–49
hsa05222	Small cell lung cancer	83	92	1.48E–49
hsa04380	Osteoclast differentiation	91	124	4.61E–49
hsa05161	Hepatitis B	95	142	1.04E–48
hsa04611	Platelet activation	90	123	1.79E–48
hsa04350	TGF-beta signaling pathway	79	83	2.18E–48
hsa05169	Epstein-Barr virus infection	105	194	2.12E–47
hsa05152	Tuberculosis	100	172	3.01E–47
hsa04218	Cellular senescence	96	156	5.86E–47
hsa04933	AGE-RAGE signaling pathway in diabetic complications	81	98	1.74E–46
hsa05220	Chronic myeloid leukemia	73	76	5.82E–45
hsa05226	Gastric cancer (GC)	91	147	1.07E–44
hsa04512	ECM-receptor interaction	74	81	1.41E–44
hsa04668	TNF signaling pathway	81	108	2.66E–44
hsa04072	Phospholipase D signaling pathway	89	145	1.58E–43
hsa05164	Influenza A	94	168	1.97E–43
hsa05212	Pancreatic cancer	70	74	6.80E–43
hsa04610	Complement and coagulation cascades	71	78	9.24E–43
hsa05206	MicroRNAs in cancer	88	149	4.35E–42
hsa05218	Melanoma	68	72	1.12E–41
hsa04926	Relaxin signaling pathway	83	130	1.31E–41
hsa04261	Adrenergic signaling in cardiomyocytes	85	139	1.50E–41
hsa05160	Hepatitis C	83	131	1.92E–41
hsa01522	Endocrine resistance	73	95	1.52E–40
hsa05140	Leishmaniasis	66	70	1.78E–40
hsa01521	EGFR tyrosine kinase inhibitor resistance	68	78	3.17E–40
hsa05211	Renal cell carcinoma	65	68	3.95E–40
hsa05142	Chagas disease (American trypanosomiasis)	74	101	4.00E–40
hsa04012	ErbB signaling pathway	69	83	6.43E–40
hsa04912	GnRH signaling pathway	70	88	1.25E–39
hsa05215	Prostate cancer	72	97	2.43E–39
hsa05203	Viral carcinogenesis	91	183	5.06E–39
hsa04024	cAMP signaling pathway	935	195	9.80E–39
hsa04068	FoxO signaling pathway	79	130	1.39E–38
hsa05214	Glioma	63	68	1.98E–38
hsa05162	Measles	79	133	4.51E–38
hsa04530	Tight junction	86	167	8.16E–38
hsa05223	Non-small cell lung cancer	61	66	3.30E–37
hsa04658	Th1 and Th2 cell differentiation	67	88	3.50E–37
hsa04640	Hematopoietic cell lineage	68	94	9.61E–37
hsa05412	Arrhythmogenic right ventricular cardiomyopathy (ARVC)	62	72	1.30E–36
hsa05224	Breast cancer	797	147	8.59E–36
hsa05210	Colorectal cancer	64	85	2.29E–35
hsa04664	Fc epsilon RI signaling pathway	59	67	2.94E–35
hsa05146	Amoebiasis	66	94	3.80E–35
hsa05133	Pertussis	60	74	1.80E–34
hsa04370	VEGF signaling pathway	55	59	8.52E–34
hsa05168	Herpes simplex infection	83	181	9.79E–34
hsa04620	Toll-like receptor signaling pathway	66	102	1.30E–33
hsa05132	Salmonella infection	61	84	3.77E–33
hsa05231	Choline metabolism in cancer	64	98	8.48E–33
hsa04210	Apoptosis	72	135	1.45E–32
hsa04657	IL-17 signaling pathway	62	92	2.28E–32
hsa04621	NOD-like receptor signaling pathway	78	166	2.50E–32
hsa04064	NF-kappa B signaling pathway	61	93	2.16E–31
hsa04910	Insulin signaling pathway	70	134	2.85E–31
hsa04662	B cell receptor signaling pathway	55	71	5.20E–31
hsa04750	Inflammatory mediator regulation of TRP channels	60	92	8.32E–31
hsa05100	Bacterial invasion of epithelial cells	55	72	8.39E–31
hsa04270	Vascular smooth muscle contraction	66	119	1.05E–30
hsa04917	Prolactin signaling pathway	54	69	1.23E–30
hsa05321	Inflammatory bowel disease (IBD)	52	62	1.50E–30
hsa04066	HIF-1 signaling pathway	61	98	1.66E–30
hsa05416	Viral myocarditis	50	56	2.75E–30
hsa04371	Apelin signaling pathway	68	133	5.17E–30
hsa05131	Shigellosis	51	63	1.72E–29
hsa04071	Sphingolipid signaling pathway	63	116	5.45E–29
hsa05225	Hepatocellular carcinoma	72	163	1.20E–28
hsa04550	Signaling pathways regulating pluripotency of stem cells	67	138	1.40E–28
hsa05213	Endometrial cancer	48	58	4.00E–28
hsa04360	Axon guidance	73	173	4.60E–28
hsa04022	cGMP-PKG signaling pathway	70	160	1.08E–27
hsa04217	Necroptosis	69	155	1.15E–27
hsa04915	Estrogen signaling pathway	64	133	3.43E–27
hsa05323	Rheumatoid arthritis	53	84	7.06E–27
hsa04520	Adherens junction	49	71	3.42E–26
hsa04390	Hippo signaling pathway	66	152	4.97E–26
hsa04213	Longevity regulating pathway—multiple species	46	61	8.08E–26
hsa05150	Staphylococcus aureus infection	43	51	1.49E–25
hsa04725	Cholinergic synapse	56	111	1.05E–24
hsa05219	Bladder cancer	39	41	1.42E–24
hsa04720	Long-term potentiation	45	64	2.13E–24
hsa05221	Acute myeloid leukemia	45	66	5.28E–24
hsa04672	Intestinal immune network for IgA production	39	44	7.89E–24
hsa05332	Graft-versus-host disease	36	36	2.90E–23
hsa04020	Calcium signaling pathway	669	179	6.70E–23
hsa04919	Thyroid hormone signaling pathway	54	115	9.56E–23
hsa04934	Cushing’s syndrome	60	153	5.42E–22
hsa04114	Oocyte meiosis	53	116	6.34E–22
hsa05330	Allograft rejection	34	35	8.83E–22
hsa04914	Progesterone-mediated oocyte maturation	48	94	1.58E–21
hsa04211	Longevity regulating pathway	46	88	5.39E–21
hsa04931	Insulin resistance	49	107	2.15E–20
hsa04145	Phagosome	54	145	4.24E–19
hsa04110	Cell cycle	50	123	4.78E–19
hsa04540	Gap junction	43	87	5.30E–19
hsa04940	Type I diabetes mellitus	32	40	7.16E–19
hsa05120	Epithelial cell signaling in Helicobacter pylori infection	38	66	1.26E–18
hsa05320	Autoimmune thyroid disease	34	49	1.28E–18
hsa05144	Malaria	33	47	3.22E–18
hsa04140	Autophagy—animal	49	125	3.46E–18
hsa04920	Adipocytokine signaling pathway	38	69	3.81E–18
hsa05202	Transcriptional misregulation in cancer	56	169	6.67E–18
hsa04612	Antigen processing and presentation	37	66	6.76E–18
hsa04932	Non-alcoholic fatty liver disease (NAFLD)	52	149	1.74E–17
hsa04728	Dopaminergic synapse	48	128	3.11E–17
hsa05134	Legionellosis	33	54	6.37E–17
hsa05322	Systemic lupus erythematosus	41	94	1.05E–16
hsa05230	Central carbon metabolism in cancer	35	65	1.36E–16
hsa04923	Regulatiolysis in adipocytes	32	53	2.43E–16
hsa05020	Prion diseases	27	33	2.61E–16
hsa04730	Long-term depression	33	60	6.32E–16
hsa04310	Wnt signaling pathway	48	143	1.03E–15
hsa04916	Melanogenesis	40	98	1.45E–15
hsa05014	Amyotrophic lateral sclerosis (ALS)	29	50	1.35E–14
hsa04930	Type II diabetes mellitus	28	46	1.58E–14
hsa01524	Platinum drug resistance	33	70	1.88E–14
hsa04260	Cardiac muscle contraction	34	76	2.47E–14
hsa04713	Circadian entrainment	37	93	3.18E–14
hsa04971	Gastric acid secretion	33	72	3.46E–14
hsa04150	mTOR signaling pathway	46	148	4.10E–14
hsa04724	Glutamatergic synapse	40	112	4.95E–14
hsa05031	Amphetamine addiction	31	65	9.03E–14
hsa04925	Aldosterone synthesis and secretion	36	93	1.34E–13
hsa05216	Thyroid cancer	24	37	4.37E–13
hsa05130	Pathogenic Escherichia coli infection	27	53	1.11E–12
hsa04726	Serotonergic synapse	37	112	2.99E–12
hsa04972	Pancreatic secretion	34	95	3.81E–12
hsa04115	p53 signaling pathway	29	68	5.02E–12
hsa04622	RIG-I-like receptor signaling pathway	29	70	8.79E–12
hsa05310	Asthma	20	28	1.14E–11
hsa04961	Endocrine and other factor-regulated calcium reabsorption	24	47	1.95E–11
hsa04913	Ovarian steroidogenesis	24	49	3.79E–11
hsa04924	Renin secretion	26	63	1.13E–10
hsa00592	Alpha-linolenic acid metabolism	18	25	1.14E–10
hsa04922	Glucagon signaling pathway	32	100	1.72E–10
hsa04152	AMPK signaling pathway	35	120	1.94E–10
hsa04911	Insulin secretion	29	84	2.90E–10
hsa00565	Ether lipid metabolism	22	46	3.50E–10
hsa04927	Cortisol synthesis and secretion	25	63	4.88E–10
hsa00591	Linoleic acid metabolism	18	29	6.37E–10
hsa05034	Alcoholism	37	142	8.22E–10
hsa05032	Morphine addiction	29	91	1.31E–09
hsa04714	Thermogenesis	47	228	3.40E–09
hsa04215	Apoptosis—multiple species	17	31	7.73E–09
hsa04723	Retrograde endocannabinoid signaling	35	148	1.95E–08
hsa05143	African trypanosomiasis	17	34	2.20E–08
hsa04727	GABAergic synapse	26	88	3.38E–08
hsa04970	Salivary secretion	25	86	8.09E–08
hsa04960	Aldosterone-regulated sodium reabsorption	16	37	2.73E–07
hsa04137	Mitophagy—animal	20	63	5.19E–07
hsa05340	Primary immunodeficiency	15	37	1.24E–06
hsa00590	Arachidonic acid metabolism	19	61	1.27E–06
hsa04070	Phosphatidylinositol signaling system	24	97	1.71E–06
hsa04918	Thyroid hormone synthesis	20	73	3.47E–06
hsa04120	Ubiquitin mediated proteolysis	28	134	4.13E–06
hsa04975	Fat digestion and absorption	14	39	8.69E–06
hsa05010	Alzheimer’s disease	31	168	1.15E–05
hsa00564	Glycerophospholipid metabolism	22	96	1.29E–05
hsa04340	Hedgehog signaling pathway	14	46	3.97E–05
hsa05030	Cocaine addiction	14	49	7.05E–05
hsa04976	Bile secretion	17	71	7.89E–05
hsa04962	Vasopressin-regulated water reabsorption	13	44	9.59E–05
hsa04974	Protein digestion and absorption	19	90	1.30E–04
hsa04710	Circadian rhythm	10	30	2.80E–04
hsa04721	Synaptic vesicle cycle	14	61	4.90E–04
hsa04973	Carbohydrate digestion and absorption	11	42	7.80E–04
hsa00562	Inositol phosphate metabolism	15	73	8.30E–04
hsa05110	Vibrio cholerae infection	11	48	0.0020
hsa01523	Antifolate resistance	8	31	0.0045
hsa05217	Basal cell carcinoma	12	63	0.0047
hsa04141	Protein processing in endoplasmic reticulum	22	161	0.0065
hsa04744	Phototransduction	6	26	0.0211
hsa05016	Huntington’s disease	22	193	0.0362

KEGG, Kyoto Encyclopedia of Genes and Genomes; MEM, multi experiment matrix; FDR, false discovery rate.

Table 3

GSEA and MEM overlapped KEGG pathway

KEGG pathways	Description	Count	Gene set count	FDR
hsa04510	Focal adhesion	69	197	2.46E–16
hsa04810	Regulatiin cytoskeleton	54	205	4.40E–09
hsa04512	ECM-receptor interaction	30	81	8.47E–08
hsa04010	MAPK signaling pathway	60	293	6.53E–07
hsa04144	Endocytosis	52	242	1.25E–06
hsa04621	NOD-like receptor signaling pathway	37	166	3.09E–05
hsa05222	Small cell lung cancer	25	92	6.03E–05
hsa05212	Pancreatic cancer	22	74	6.39E–05
hsa05220	Chronic myeloid leukemia	21	76	2.10E–04
hsa04140	Autophagy - animal	27	125	0.0006
hsa04060	Cytokine-cytokine receptor interaction	44	263	9.10E–04
hsa05410	Hypertrophic cardiomyopathy (HCM)	19	81	0.0023
hsa05211	Renal cell carcinoma	17	68	0.0024
hsa05219	Bladder cancer	12	41	0.0046
hsa04630	Jak-STAT signaling pathway	28	160	0.0057
hsa04350	TGF-beta signaling pathway	18	83	0.0057
hsa04610	Complement and coagulation cascades	17	78	0.0069
hsa04722	Neurotrophin signaling pathway	22	116	0.0070
hsa04666	Fc gamma R-mediated phagocytosis	18	89	0.0095
hsa04670	Leukocyte transendothelial migration	21	112	0.0095
hsa05414	Dilated cardiomyopathy (DCM)	17	88	0.0153
hsa04514	Cell adhesion molecules (CAMs)	23	139	0.0191
hsa04650	Natural killer cell mediated cytotoxicity	20	124	0.0351

GSEA, gene set enrichment analysis; MEM, multi experiment matrix; KEGG, Kyoto Encyclopedia of Genes and Genomes; FDR, false discovery rate.

Utilizing the MCODE algorithm, 60 genes involved in the SERPINE1-related module were identified (). According to GO enrichment analysis, these 60 genes were mainly enriched in ‘platelet degranulation’, ‘ECM organization’, and ‘extracellular structure organization’ in the biological process (BP) category; ‘platelet alpha granule lumen’, ‘platelet alpha granule lumen’, and ‘secretory granule lumen’ in the cellular component (CC) category; and ‘ECM structural constituent’, ‘cell adhesion molecule binding’, and ‘integrin binding’ in the molecular function (MF) category. The PI3K-Akt, Ras, and MAPK signaling pathways were the most enriched KEGG terms. GO functional annotations of the KEGG pathway enrichment results are shown in and the top 10 significantly enriched terms for SERPINE1-related module genes are provided for each category.

Figure 5

The PPI network of the SERPINE1-related module genes. The PPI network was constructed online via STRING and those genes were chosen for further analysis. Network nodes represent proteins and edges represent protein-protein associations. PPI, protein-protein interaction.

Figure 6

Function analysis of SERPINE1-related module genes. (A) The top 10 significantly enriched GO categories of SERPINE1-related module genes; (B) the top 10 significantly enriched KEGG signaling pathways of SERPINE1-related module genes. GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Identification of the prognostic module genes and construction of the SERPINE1-related module genes prognostic risk model

Investigation of the influence of module genes on the OS of GC patients using the UALCAN online tool showed that 15 SERPINE1-related module genes (LAMA4, PROS1, LEFTY2, A2M, THBS1, FN1, SERPING1, PAK3, LAMA2, TGFB1, VWF, F8, F5, ARHGEF6, and ACTN2) affected the OS of GC patients. Kaplan-Meier analysis showed that eight SERPINE1-related module genes (F13A1, PROS1, LEFTY2, SERPING1, PAK3, TGFB1, VEGFB, and VEGFC) were associated with GC RFS. These genes were subsequently entered into a multivariate Cox regression analysis. To identify the best predictors that significantly contributed to patient OS and RFS, we used the lowest AIC value for variable selection to build prognostic classifiers that consisted of five genes (LAMA4, PAK3, TGFB1, ARHGEF6, and SERPING1) for OS and two genes (VEGFB and LEFTY2) for RFS. We developed risk score formulas to predict patient survival:

We then calculated the risk scores for all GC patients using these two formulas. Additionally, by using Pearson’s correlation analysis in the GEPIA online database, SERPINE1 expression was found to be correlated with the expression of SERPINE1-related module genes included in the Cox regression model with the following findings: TGFB1 (r=0.37; P<0.0001), LAMA4 (r=0.22; P<0.0001), PAK3 (r=0.13; P<0.01), ARHGEF6 (r=0.29; P<0.05), SERPING1 (r=0.28; P<0.0001), VEGFB (r=0.14; P<0.0001), and LEFTY2 (r=0.2; P<0.0001) ().

Figure S1

Correlation analysis between SERPINE1 and SERPINE1-related module genes included in the Cox regression model using Pearson’s correlation based on TCGA database. (A) LAMA4, (B) ARHGEF6, (C) TGFB1, (D) PAK3, (E) SERPING1, (F) LEFTY2, and (G) VEGFB. TCGA, The Cancer Genome Atlas.

X-tile plots were used to obtain the optimum cutoff values for OS (3.5) and RFS (7.5) risk scores. Patients with a higher risk score generally had poorer survival than those with a lower risk score. Kaplan-Meier survival analysis demonstrated that patients with high-risk scores had a shorter OS and RFS than those with low-risk scores ().

Figure 7

Kaplan-Meier curves demonstrating patient survival after resection for GC according to risk score based on SERPINE1-related module genes prognostic classifiers. (A) GC patients with high risk score had a poorer OS than those with low risk score; (B) GC patients with high risk score had a poorer RFS than those with low risk score. GC, gastric cancer; OS, overall survival; RFS, recurrence-free survival.

Using a univariate and multivariate Cox proportional hazards regression model to identify OS and RFS predictors

All variables listed in were used for univariate and multivariate Cox proportional hazards regression analysis. A Cox proportional hazards regression model with backward stepwise selection using the AIC from the Cox proportional hazards regression model showed the following five OS-associated variables: age, resection margins, lymph node-positive proportion, patient tumor status, and risk score (). In multivariable analysis, age ≥60 years (HR, 2.14; 95% CI, 1.45–3.16; P<0.01), R2 margins (HR, 2.70; 95% CI, 1.41–5.14; P<0.05), lymph node-positive proportion (HR, 3.38; 95% CI, 2.03–5.63; P<0.001), patient tumor status (HR, 3.33; 95% CI, 2.28–4.87; P<0.001), and OS risk score (HR, 2.72; 95% CI, 1.82–4.05; P<0.05) were independently associated with OS. Male sex (HR, 2.55; 95% CI, 1.46–4.45; P<0.01), R2 margins (HR, 13.08; 95% CI, 4.26–40.15; P<0.001), lymph node-positive proportion (HR, 2.55; 95% CI, 1.20–5.45; P<0.05), and RFS risk score (HR, 2.70; 95% CI, 1.82–4.06; P<0.001) were independently associated with RFS ().

Table 4

Cox proportional hazards regression model showing the association of variables with OS

Variables	Univariate analysis			Multivariate analysis
	HR (95% CI)	P value		HR (95% CI)	P value
Factors selected
Age, y
<60	1 (Reference)	NA		1 (Reference)	NA
≥60	1.61 (1.21–2.23)	0.0183*		2.14 (1.45–3.16)	0.0013*
Resection margin
R0	1 (Reference)	NA		1 (Reference)	NA
R1	2.25 (1.17–4.31)	0.0407*		1.20 (0.59–2.44)	0.6734
R2	7.39 (4.31–12.69)	<0.0001*		2.70 (1.41–5.14)	0.0115*
Lymph node positive proportion	4.31 (2.77–6.71)	<0.0001*		3.38 (2.03–5.63)	<0.0001*
Patient tumor status
Tumor free	1 (Reference)	NA		1 (Reference)	NA
With tumor	4.92 (3.47–6.98)	<0.0001*		3.33 (2.28–4.87)	<0.0001*
Risk score	1.74 (1.32–2.30)	<0.0010*		2.72 (1.82–4.05)	<0.0001*
Factors not selected
Sex
Female	1 (Reference)	NA		NA	NA
Male	1.26 (0.93–1.71)	0.0207*		NA	NA
Histologic grade
G1	1 (Reference)	NA		NA	NA
G2	1.22 (0.37–4.01)	0.781		NA	NA
G3	1.54 (0.47–4.99)	0.549		NA	NA
Tumor anatomic site
Antrum	1 (Reference)	NA		NA	NA
Cardia	1.04 (0.68–1.58)	0.8790		NA	NA
Fundus	0.81 (0.58–1.14)	0.316		NA	NA
Gastroesophageal junction	0.73 (0.42–1.26)	0.346		NA	NA
TNM stage
I/II	1 (Reference)	NA
III/IV	2.01 (1.48–2.74)	<0.0002*
T stage
T1/T2	1 (Reference)	NA
T3/T4	1.64 (1.15–2.35)	0.0224*
N stage
N0/N1	1 (Reference)	NA
N2/N3	1.56 (1.17–2.09)	0.0109*
M stage
M0	1 (Reference)	NA
M1	2.12 (1.31–3.44)	0.0103*
SERPINE1 expression	1.26 (1.14–1.38)	0.0001*

* indicate P<0.05. OS, overall survival; HR, hazard ratio; CI, confidence interval; NA, not applicable; TNM, tumor-node-metastases.

Table 5

Cox proportional hazards regression model showing the association of variables with RFS

Variables	Univariate analysis			Multivariate analysis
	HR (95% CI)	P value		HR (95% CI)	P value
Factors selected
Sex
Female	1 (Reference)	NA		NA	NA
Male	1.98 (1.21–3.24)	0.0220*		2.55 (1.46–4.45)	0.0060*
Resection margin
R0	1 (Reference)	NA		1 (Reference)	NA
R1	1.24 (0.38–4.08)	0.7680		0.67 (0.20–2.28)	0.5953
R2	8.21 (3.03–22.25)	0.0005*		13.08 (4.26–40.15)	0.0002*
Lymph node positive proportion	3.94 (1.98–7.82)	0.0010*		2.55 (1.20–5.45)	<0.0417*
Risk score, RFS	2.67 (1.90–3.75)	<0.0001*		2.70 (1.82–4.06)	<0.0001*
Factors not selected
Age, y
<60	1 (Reference)	NA		NA	NA
≥60	0.69 (0.45–1.07)	0.1617		NA	NA
Histologic grade
G1/G2	1 (Reference)	NA		NA	NA
G3	2.02 (1.25–3.27)	0.0158*		NA	NA
Tumor anatomic site
Antrum	1 (Reference)	NA		NA	NA
Cardia	1.42 (0.79–2.56)	0.3300		NA	NA
Fundus	0.63 (0.37–1.08)	0.1603		NA	NA
Gastroesophageal junction	0.91 (0.44–1.86)	0.8194		NA	NA
TNM stage
I/II	1 (Reference)	NA
III/IV	0.96 (0.63–1.47)	0.8686
T stage
T1/T2	1 (Reference)	NA
T3/T4	0.75 (0.48–1.16)	0.2783
N stage
N0/N1	1 (Reference)	NA
N2/N3	1.39 (0.91–2.13)	0.2041
M stage
M0	1 (Reference)	NA
M1	1.43 (0.61–3.36)	0.4910
SERPINE1 expression	1.20 (1.04–1.38)	0.0384*

* indicate P<0.05; RFS, recurrence-free survival; HR, hazard ratio; CI, confidence interval; NA, not applicable; TNM, tumor-node-metastases.

Nomograms and model performance

Nomograms to predict GC patient OS and RFS are shown in . The nomogram to predict OS was created based on the following five independent prognostic factors: age (<60 or ≥60 years), resection margins (R0, R1, or R2), patient tumor status (tumor-free or with tumor), lymph node-positive proportion, and risk score. The nomogram to predict RFS was created based on the following four independent prognostic factors: sex (female or male), resection margins (R0, R1, or R2), lymph node-positive proportion, and RFS risk score. A higher total number of points based on the sum of the number of points assigned to each factor in the nomograms was associated with a poorer prognosis. The discriminative ability of the final model for OS and RFS was assessed using C statistics (0.755 for OS and 0.745 for RFS). Model accuracy and potential overfit were assessed by bootstrap validation with 1,000 re-samplings. The 60-sample bootstrapped calibration plots for the prediction of 3-year OS and RFS are presented in . Predictive accuracy for OS was compared between the proposed nomogram and the nomogram based on the conventional staging system constructed using the prognostic factors of age (<60 or ≥60 years) and TNM stage (T1/T2, T3/T4). The C statistics of the proposed nomogram were greater than those of the TNM stage nomogram (0.755 vs. 0.617). The calculated NRI was 0.48 (95% CI, 0.23–0.96), which indicated that the performance of the new model was better than that of the TNM stage model for predicting OS.

Figure 8

Nomogram for predicting OS in GC patients after surgery. OS, overall survival; GC, gastric cancer.

Figure 9

Nomogram for predicting RFS in GC patients after surgery. RFS, recurrence-free survival; GC, gastric cancer.

Figure 10

Calibration plot comparing predicted and actual survival probabilities at the 3-year follow-up. The 60-sample bootstrapped calibration plot for 3-year OS (A) and RFS (B) prediction is shown. The 45-degree line represents the ideal fit; rhombuses represent nomogram-predicted probabilities; crosses represent the bootstrap-corrected estimates; and error bars represent the 95% CIs of these estimates. OS, overall survival; RFS, recurrence-free survival; CI, confidence interval.

Discussion

In the current study, we found that SERPINE1 was significantly upregulated in GC tissues compared to normal or adjacent normal tissues based on the meta-analysis of TCGA and GEO datasets. Moreover, high SERPINE1 expression was associated with GC T stage and survival status. Univariate Cox regression analyses indicated that SERPINE1 expression was associated with prognosis and may therefore be a potentially useful biomarker for GC prognosis and diagnosis and a potential therapeutic target. Meta-analysis confirmed the diagnostic value of SERPINE1 in GC. Similarly, Sakakibara et al. found that SERPINE1 overexpression is significantly associated with malignancy in GC (17). A meta-analysis of 22 studies that included 1,966 patients revealed that high SERPINE1 expression is associated with a short OS (18). Furthermore, Nishioka et al. reported that SERPINE1 RNA interference (RNAi) suppresses GC metastasis in vivo (19). These conclusions are consistent with those of our study and demonstrate the prognostic value and potential therapeutic roles of SERPINE1.

Interestingly, SERPINE1 showed surprising diagnostic value in TCGA data; for healthy individuals the AUC was 0.876 and the AUC values were 0.800, 0.878, 0.891, and 0.897 for stages I, II, III, and IV GC patients, respectively. In the diagnostic meta-analysis, 631 GC and 314 controls were included from the GEO and TCGA databases. The meta-analysis was performed to evaluate the accuracy of SERPINE1 for GC detection. The combined AUC was 0.80, which was indicative of moderate diagnostic accuracy. The combined values of the sensitivity (0.69) and specificity (0.78) showed the accuracy of SERPINE1 for GC detection. However, there were some limitations to our meta-analysis. Heterogeneity (I2=80.5%) was unavoidable, partly because of the different platforms that were used. Furthermore, different races also contributed to heterogeneity. Because SERPINE1 is not the only factor with diagnostic value for GC, combining SERPINE1 with other specific markers for GC diagnosis might further improve diagnostic accuracy.

The molecular mechanisms underlying the differential expression of SERPINE1 and its potential prognostic impact on GC are still poorly understood. The current study improved our understanding of the relationship between SERPINE1 and GC. In the current study, functional annotation based on GSEA and MEM SERPINE1 co-expression analysis showed that SERPINE1 the three most significant pathways associated with the high SERPINE1 expression phenotype were the PI3K-Akt, Ras, and MAPK signaling pathways; this indicated that SERPINE1 and related module genes might promote GC cell growth and metastasis, and result in poorer survival via the PI3K-Akt, Ras, and MAPK pathways. Accumulating evidence shows that the activation of these pathways plays a critical role in promoting GC progression and metastasis (20-22).

The creation of a reliable and practicable nomogram for predicting GC OS and recurrence is both clinically valuable and challenging to create. GC is a highly malignant tumor, with up to 18.4% of patients with R0 resections for node-negative GC experiencing recurrence after surgical resection (23). The results from a large sample and multicenter cohort of Chinese patients indicated that 60.8% of patients experienced recurrence after curative resection for GC from 1986 to 2013 (24). Accurate prognostication for GC after surgery is vital, not only for informing patients about their risk of recurrence and prognosis, but also for selecting patients for further adjuvant treatment. Recent studies on clinical measurement models of GC have shown that a nomogram with the TNM staging system combined with other variables is better than that of the TNM staging system alone (25,26). Consistently, our results showed that the proposed nomogram provided more accurate OS prediction for GC patients than the AJCC TNM-based nomogram Although the accuracy and discrimination of a model with one biomarker may be limited, a model established on the basis of module genes could likely provide more accurate and reliable prognostic predictions for GC patients. Therefore, we proposed a signature comprising these SERPINE1-related module genes that could be independent factors affecting OS and RFS in GC patients. Studies have shown that resection margins and lymph node-positive proportions are independent prognostic factors for GC and that patients with positive margins and higher lymph node-positive proportions have a poor prognosis (27,28). Accordingly, our results showed that these two factors were independent prognostic factors for OS and RFS in GC.

Limitations to the current study included the following: First, our study is a retrospective study and therefore has inherent defects such as selection bias. Second, GC development is a complex process and all kinds of clinical factors, such as treatment details, should be considered to clarify the key role of SERPINE1 in GC development; however, this kind of information is lacking or inconsistently available in public databases. Third, our nomograms were internally validated using bootstrap validation and lack external validation. Future studies are urgently needed to externally validate the proposed nomograms and other essential factors based on treatment strategies should be incorporated. Finally, the current study was based on TCGA data mining; therefore, the protein level of SERPINE1 expression could not be directly evaluated, and the SERPINE1 mechanisms involved in GC development could not be clearly illustrated. The signaling pathways involved in SERPINE1 upregulation SERPINE1 in GC patients need to be verified by in vivo and in vitro experiments.

Conclusions

This study comprehensively analyzed the expression of SERPINE1 in patients with GC and evaluated the potential clinical value of SERPINE1 expression by performing a meta-analysis of data from GEO and TCGA databases. Bioinformatics analysis identified the possible functional mechanisms of SERPINE1 expression that facilitate GC onset and development as being regulated through the PI3K-Akt, Ras, and MAPK pathways. Finally, a nomogram based on SERPINE1-related module genes provided a more accurate OS prediction for GC patients than the AJCC TNM-based nomogram. These findings must be validated in multicenter clinical trials.