Gene ontology functional enrichment analysis
To investigate the encoding gene ontology for the mRNAs identified with association P values < 5 × 10−3 in the discovery stage (see Table S1, Supplemental Digital Content 1, http://links.lww.com/CTG/A1), GO analysis was performed using online biological tool DAVID. Twelve GO terms were obtained (Figure 1a and see Table S4, Supplemental Digital Content 1, http://links.lww.com/CTG/A1). Most genes were enriched in the plasma membrane cellular component.
Pathway enrichment analysis
A total of 4 databases, including “KEGG pathway,” “Reactome,” “BioCyc,” and “PANTHER,” were applied in pathway enrichment analysis. Top 20 pathways were shown in Figure 1b and Table S5 (Supplemental Digital Content 1, http://links.lww.com/CTG/A1). The result indicated that encoding genes of mRNAs significantly associated with GC survival in TCGA may be mainly involved in signaling transduction pathways.
Construction of PPI network and subnetwork analysis
To achieve a better understanding of the biological processes in GC survival, we adopted online database STRING (https://string-db.org/) and visualization software Cytoscape (version: 3.6.1, http://www.cytoscape.org/) to construct PPI network. Based on the criterion (minimum required interaction score = 0.4), a total of 66 genes associated with GC survival were filtered into the PPI network. It comprised 66 nodes and 58 edges (Figure 2a). Furthermore, a key subnetwork model, which could represent the overlapping protein complexes in the PPI network, was identified through the plugin software ClusterONE, containing VMF, SELP, CD36, CD44, and CD109 (Figure 2b).
Selection of key candidate mRNAs for the validation study
A total of 54 mRNAs (see Table S3, Supplemental Digital Content 1, http://links.lww.com/CTG/A1) were examined in the validation study based on GSE84437 datasets, with 13 of them significantly associated with GC survival at P < 0.05 in the validation. Among them, high mRNA expression of 12 genes, including RIMS1 (regulating synaptic membrane exocytosis 1), PRICKLE1 (prickle planar cell polarity protein 1), MCC (mutated in colorectal cancers), DCLK1 (doublecortin-like kinase 1), FLRT2 (fibronectin leucine rich transmembrane protein 2), SLCO2A1 (solute carrier organic anion transporter family member 2A1), CDO1 (cysteine dioxygenase type 1), GHR (growth hormone receptor), CD109 (CD109 molecule), SELP (selectin P), UPK1B (uroplakin 1B), and CD36 (CD36 molecule), were associated with poor survival, while high mRNA expression of SLC25A15 (solute carrier family 25 member 15) was associated with improved survival. The associations remained significant in the Cox regression analysis, which adjusted additionally for clinical characteristics (Table 2). Among these 13 mRNAs, SELP, CD36, and CD109 were also highlighted in the key subnetwork, which could represent the overlapping protein complexes in the PPI network, as described above (Figure 2b).
Construction and validation of risk score model
Based on the mRNA expression levels of these 13 genes, we established the risk score as follows:
Risk score = 0.618 × DCLK1 + 0.400 × FLRT2 + 1.395 × MCC + 0.458 × PRICKLE1 + 0.881 × RIMS1 + (−0.768) × SLC25A15 + 0.404 × SLCO2A1 + 0.655 × CDO1 + 0.379 GHR + 0.969 × CD109 + 0.441 × SELP + 0.382 × UPK1B + 0.289 × CD36, with a higher score indicating worse survival potential. The risk score was independently associated with GC survival, with a hazard ratio (95% confidence interval) of 1.34 (1.19–1.51) per one score increase in TCGA-STAD and of 1.22 (1.12–1.32) per one score increase in GSE84437. Kaplan–Meier survival curves performed well on distinguishing patients with GC with differential survival status in TCGA-STAD (Figure 3). In GSE84437, the risk scores performed well in distinguishing patients by survival probability, particularly for those with the survival time of around 45 months or longer (Figure 3).
Molecular signatures associated with GC prognosis have been sparse. In the present study, we comprehensively examined the mRNA signature associated with GC survival in the discovery stage (TCGA-STAD) based on RNA-Seq data and the validation stage (GSE84437) based on microarray data. A total of 13 mRNAs were identified significantly associated with GC survival. Among them, except for SLC25A15, 12 mRNAs were inversely associated with GC survival in both the discovery and validation dataset, including RIMS1, PRICKLE1, MCC, DCLK1, FLRT2, SLCO2A1, CDO1, GHR, CD109, SELP, UPK1B, and CD36. A 13 mRNA-based risk score model was established, which achieved good performance on predicting GC survival in both TCGA and GSE84437 datasets.
Potential function of the mRNA encoding genes was annotated based on the gene ontology functional enrichment analysis. Among the encoding genes for 13 mRNAs significantly associated with GC survival in the replication analysis, SLC25A15 was not enriched in the plasma membrane cellular component. Interestingly, also, SLC25A15 was the only mRNA inversely associated with risk of GC death in our analysis. A previous study suggested the association between plasma membrane and cancer cell migration and invasion (17). Several biomarkers based on plasma membrane proteins for GC development and progression have also been identified (18,19). The association of plasma membrane proteins with GC prognosis has been investigated as well (20,21). Thus, we assume that plasma membrane might play a key role in GC survival through regulating these identified mRNA expressions.
We also conducted pathway enrichment analysis to identify potential biological pathways associated with GC survival. Among the 13 mRNAs, we found once again that SLC25A15 was not involved in signal transduction pathway. A previous study showed that the cancer development was encoded by altering the patterns of signal transduction networks (21). Aberrant activation of several signal transduction pathways, including Hedgehog, Notch, and Wnt pathways, has already been associated with multicancer development (22). Several cancer therapeutics have been developed targeting signal transduction pathways during past few years (23). Thus, the signal transduction networks may be involved in the mechanisms underlying the associations between identified mRNAs and GC survival.
The contrasting associations with GC death that we found for SLC25A15 and other mRNAs are interesting, but the underlying mechanism is still unclear. Previous literature has not linked SLC25A15 to cancer development or prognosis. Current knowledge regarding the functional differences between SLC25A15 and other mRNAs has been very sparse. In our study, further integrating the results for the gene ontology functional enrichment analysis and pathway enrichment analysis, SLC25A15, which encodes the only mRNA inversely associated with GC death, was neither enriched in the plasma membrane cellular component nor involved in the signal transduction pathway. It is therefore reasonable to speculate that the plasma membrane cellular component and signal transduction pathway might play important roles in risk of GC death, which may partly explain the associations with GC death in the opposite directions that were found for SLC25A15 and other mRNAs. However, our study based on association analyses and function and pathway annotations cannot directly respond to the mechanisms underlying the associations. Efforts from basic laboratory are required to elucidate the gene functions, which could clarify the contrasting associations for SLC25A15 and other mRNAs with GC death.
Among the highlighted genes in our study, mRNA expression of CD36 and protein expression of DCLK1 and SLCO2A1 have been previously associated with GC survival. CD36 mRNA overexpression was associated with poor GC overall survival based on cDNA microarray data of 18 patients with GC in the discovery set and 30 patients with GC in the validation set (7). DCLK1 protein overexpression has been associated with poor GC overall survival using expression data of GC tumor specimens (n = 122) examined by immunohistochemistry (25). Our findings are consistent with these studies on the association of DCLK1 and CD36 with GC survival, though they either lacked suitable validation sets or had limited sample size. In addition, a DCLK1-based mRNA signature has also been established previously to predict GC survival (26). However, negative SLCO2A1 (also known as PGT) protein expression in GC tumor specimens (n = 96), as examined by immunohistochemistry, was associated with poor survival (27), in contrast to our findings on the inverse association between SLCO2A1 mRNA expression and GC survival. As that study was conducted based on a limited sample size, and also lacked a validation stage, the robustness of that finding may be of concern.
In previous studies, 4 mRNAs have been associated with prognosis of other cancers, but not with GC prognosis. CD109 mRNA overexpression has been associated with poor overall survival of lung adenocarcinoma (RNA-Seq) (28). SELP mRNA overexpression has been associated with the poor overall survival of gastrointestinal stromal tumors (microarray) (29). PRICKLE1 mRNA overexpression from cancer cell lines has been associated with poor metastasis-free survival of basal breast cancer (30). In addition, UPK1B mRNA overexpression has been associated with laryngeal cancer recurrence (microarray) (31). Our study is the first to report the association of these 4 mRNAs with GC survival in the same direction with the previously reported association of these mRNAs with survival of other cancers.
Methylation has been associated with the risk and (or) prognosis of GC and other cancers (32). Among the highlighted genes in our study, CDO1 methylation has been correlated with prognosis of other cancers previously (33,34), while our study is the first to report the association between CDO1 hypermethylation and mRNA overexpression and the risk of GC death (see Figure S1 and Table S3, Supplemental Digital Content 1, http://links.lww.com/CTG/A1). Several studies have identified FLRT2 methylation as potential biomarker for screening of prostate cancer (35) and breast cancer (36). However, although our study showed the association between FLRT2 mRNA overexpression and poor GC survival, we did not find the association of FLRT2 methylation with GC prognosis. In addition, our study is also the first to report an inverse association between GHR mRNA expression and methylation (see Table S2 and Figure S1, Supplemental Digital Content 1, http://links.lww.com/CTG/A1) and also the first to report GHR mRNA overexpression and hypomethylation as risky factors for GC death (see Table S3, Supplemental Digital Content 1, http://links.lww.com/CTG/A1). GHR encodes the transmembrane receptor for growth hormone, while the elevated serum level of growth hormone has been related to increased risk of GC and several other cancers (37). For MCC, RIMS1, and SLC25A15, previous evidence has been sparse regarding their associations with cancer development or prognosis.
Our study was conducted based on publicly accessible databases with a relatively big sample size of both discovery and validation datasets. A comprehensive approach was conducted by integrating prognostic mRNAs with methylation profiles and subnetwork analysis for conspicuously thorough selection of key mRNAs for validation, which yielded 13 mRNA signatures associated with GC survival. Except CD36, DCLK1, and SLCO2A1, other mRNAs are newly reported to be associated with GC survival. The 13 mRNA-based risk score model performed well in distinguishing the risk of GC prognosis, which might be useful in clinical practice regarding patient stratification in the recent future. We were able to control for the major clinical characteristics in a secondary analysis, which did not change the results materially.
We acknowledge several limitations. First, TCGA and GEO databases were derived based on different platforms for mRNA expression (RNA-Seq vs microarray). Although we deliberately performed data processing, the interpretation of the results, particularly the effect magnitudes across the 2 platforms, should be cautious. Second, our study was based on association studies and bioinformatics analysis. Further studies are warranted to clarify the mechanisms linking these mRNAs to GC poor survival. Third, our analyses were restricted by the available clinical characteristics and GC outcome variables. Gene expression may be varied by different race/ethnicity but our validation dataset (GSE84437) does not have the information on race/ethnicity. In the discovery stage, a secondary analysis additionally adjusting for race did not materially change the findings though. Further studies are warranted to examine the potential predictive value of the 13 mRNA signatures associated with other GC prognosis-related variables, such as progression-free survival, and also to examine the mRNAs associated with GC survival in certain race/ethnicity group.
In conclusion, findings based on 2 well-established cohorts suggest the 13 mRNA signatures might play an important role in predicting GC survival ahead of time. Our study may have implications for clinical practices regarding patient stratification. Exploration in the laboratory setting may contribute to the understanding of underlying molecular mechanisms and may inspire the development of novel targeted therapeutic strategies.
CONFLICTS OF INTEREST
Guarantor of the article: Wen-Qing Li and Kai-Feng Pan.
Specific author contributions: J.D.: study concept and design, data analyses, and draft of the manuscript. Z.-X.L., Y.Z., J.-L.M., T.Z., and W.-C.Y.: interpretation of data. W.-Q.L., and K.-F.P.: study concept and design, draft of the manuscript, critical revision of the manuscript for important intellectual content. All authors corrected and approved the manuscript.
Financial support: Beijing Municipal Administration of Hospitals' Ascent Plan (DFL20181102) (to K.-F.P.).
Potential competing interests: None.
WHAT IS KNOWN
- ✓ The prognosis of patients with GC vary significantly even among those with similar clinical features.
- ✓ Molecular biomarkers which predict GC prognosis are still limited.
WHAT IS NEW HERE
- ✓ This study was conducted based on publicly accessible databases with a relatively big sample size of both discovery and validation datasets.
- ✓ The study yielded 13 mRNA signatures significantly associated with GC survival.
- ✓ Except CD36, DCLK1, and SLCO2A1, other mRNAs are newly reported to be associated with GC survival.
- ✓ The 13 mRNA-based risk score model performed well in distinguishing the risk of GC prognosis, which might be useful in clinical practice regarding patient stratification in the recent future.
We thank all individuals who participated in this study and donated samples.
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Supplemental Digital Content
© 2019 by Lippincott Williams & Wilkins, Inc.