Identification of potential key genes in gastric cancer using bioinformatics analysis

Identification of gastric cancer biomarkers through in-silico analysis of microarray based datasets

Gastric cancer is among the most prevalent cancers worldwide including in Pakistan. Late diagnosis of gastric cancer leads to reduced survival. The present study aimed to investigate biomarkers for early diagnosis and prognosis of gastric cancer. For this purpose, the ten microarray-based gene expression datasets (GSE54129, GSE79973, GSE161533, GSE103236, GSE33651, GSE19826, GSE118916, GSE112369, GSE13911, and GSE81948) were retrieved from GEO database and analyzed by GEO2R to identify differentially expressed genes. Datasets were arranged in subsets of different dataset combinations to identify common DEGs. The gene ontology and functional pathway enrichment analysis of common DEGs was performed by DAVID tool. Pan-cancer analysis was conducted by UALCAN database. Survival analysis of common DEGs was done by Kaplan-Meier plotter. A total of 71 common DEGs were identified in different combinations of datasets. Among them, only 5 DEGs namely ATP4B, ATP4A, CCKBR, KCNJ15, and KCNJ16 were detected to be common in all the datasets. The GO and pathway analysis represented that the identified DEGs are involved in gastric acid secretion and collecting duct acid secretion pathways. Further expression validation of these five genes using three additional datasets (GSE31811, GSE26899, and GSE26272) confirmed their differential expression in gastric cancer samples. The pan-cancer analysis also revealed aberrant expression of DEGs in various cancers. The survival analysis showed the association of these 5 DEGs with poor survival of gastric cancer patients. To conclude, this study revealed a panel of 5 genes, which can be employed as diagnostic and prognostic biomarkers of gastric cancer patients.

Early diagnostic and prognostic biomarkers for gastric cancer: systems-level molecular basis of subsequent alterations in gastric mucosa from chronic atrophic gastritis to gastric cancer

PURPOSE

It is important to comprehend how the molecular mechanisms shift when gastric cancer in its early stages (GC). We employed integrative bioinformatics approaches to locate various biological signalling pathways and molecular fingerprints to comprehend the pathophysiology of the GC. To facilitate the discovery of their possible biomarkers, a rapid diagnostic may be made, which leads to an improved diagnosis and improves the patient's prognosis.

METHODS

Through protein-protein interaction networks, functional differentially expressed genes (DEGs), and pathway enrichment studies, we examined the gene expression profiles of individuals with chronic atrophic gastritis and GC.

RESULTS

A total of 17 DEGs comprising 8 upregulated and 9 down-regulated genes were identified from the microarray dataset from biopsies with chronic atrophic gastritis and GC. These DEGs were primarily enriched for CDK regulation of DNA replication and mitotic M-M/G1 phase pathways, according to KEGG analysis (p > 0.05). We discovered two hub genes, MCM7 and CDC6, in the protein-protein interaction network we obtained for the 17 DEGs (expanded with increased maximum interaction with 110 nodes and 2103 edges). MCM7 was discovered to be up-regulated in GC tissues following confirmation using the GEPIA and Human Protein Atlas databases.

CONCLUSION

The elevated expression of MCM7 in both chronic atrophic gastritis and GC, as shown by our comprehensive investigation, suggests that this protein may serve as a promising biomarker for the early detection of GC.

Differential expression of KCNQ1 and ATP4A genes according to the sex and age in the stomach

We compared the expression of six genes in stomach tissue samples between healthy men and women in different age groups to study sexually dimorphic gene expression. Real-Time RT-PCR was used to compare gene expression between men and women. Our results showed that the expression of KCNQ1 (p = 0.01) was significantly higher in non-menopausal women compared to post-menopausal women. In addition, the expression level of the ATP4A gene in men under 35 years was significantly higher than in men above 50 (p = 0.026). Sexually and age dimorphic gene expression in some genes throughout life may affect gastric function.

CGB5, INHBA and TRAJ19 Hold Prognostic Potential as Immune Genes for Patients with Gastric Cancer

BACKGROUND

Gastric cancer (GC) seriously threatens people's health and life quality worldwide.

AIM

The current study sought to explore prognostic immune genes and their regulatory network in GC.

METHODS

First, expression data in GC and normal samples were analyzed based on bioinformatics analysis. Immune-related genes were identified and confirmed with univariate/multivariate Cox analysis and receiver-operating characteristic curve. The upstream transcription factors of immune genes were subsequently predicted, and their regulatory network was constructed. GC and adjacent normal tissues were obtained from 76 patients with GC to determine the expression patterns of immune genes and their correlation with overall prognosis. CD8⁺ T-cell infiltration of patients with high or low risk was detected by means of immunohistochemistry.

RESULTS

Bioinformatics analysis highlighted 3689 differentially expressed genes in GC, including 87 immune genes, 8 of which were significantly associated with patient survival. CGB5 and INHBA were high-risk genes, while TRAJ19 was identified as a low-risk gene, all of which were found to be regulated by 11 different transcription factors. Furthermore, CGB5 and INHBA exhibited negative correlation with the prognosis of GC patients; however, TRAJ19 was positively correlated with GC patient prognosis. The incidence of lymph node metastasis was higher, the pathological stage was advanced and the infiltrated CD8⁺ T cells were fewer in the high-risk GC group.

CONCLUSIONS

Overall, our findings identified the key roles of CGB5, INHBA and TRAJ19 in prognosis GC patients, serving as an important gene set for prognostic prediction.

Identification of gene profiles related to the development of oral cancer using a deep learning technique

BACKGROUND

Oral cancer (OC) is a debilitating disease that can affect the quality of life of these patients adversely. Oral premalignant lesion patients have a high risk of developing OC. Therefore, identifying robust survival subgroups among them may significantly improve patient therapy and care. This study aimed to identify prognostic biomarkers that predict the time-to-development of OC and survival stratification for patients using state-of-the-art machine learning and deep learning.

METHODS

Gene expression profiles (29,096 probes) related to 86 patients from the GSE26549 dataset from the GEO repository were used. An autoencoder deep learning neural network model was used to extract features. We also used a univariate Cox regression model to select significant features obtained from the deep learning method (P < 0.05). High-risk and low-risk groups were then identified using a hierarchical clustering technique based on 100 encoded features (the number of units of the encoding layer, i.e., bottleneck of the network) from autoencoder and selected by Cox proportional hazards model and a supervised random forest (RF) classifier was used to identify gene profiles related to subtypes of OC from the original 29,096 probes.

RESULTS

Among 100 encoded features extracted by autoencoder, seventy features were significantly related to time-to-OC-development, based on the univariate Cox model, which was used as the inputs for the clustering of patients. Two survival risk groups were identified (P value of log-rank test = 0.003) and were used as the labels for supervised classification. The overall accuracy of the RF classifier was 0.916 over the test set, yielded 21 top genes (FUT8-DDR2-ATM-CD247-ETS1-ZEB2-COL5A2-GMAP7-CDH1-COL11A2-COL3A1-AHR-COL2A1-CHORDC1-PTP4A3-COL1A2-CCR2-PDGFRB-COL1A1-FERMT2-PIK3CB) associated with time to developing OC, selected among the original 29,096 probes.

CONCLUSIONS

Using deep learning, our study identified prominent transcriptional biomarkers in determining high-risk patients for developing oral cancer, which may be prognostic as significant targets for OC therapy. The identified genes may serve as potential targets for oral cancer chemoprevention. Additional validation of these biomarkers in experimental prospective and retrospective studies will launch them in OC clinics.

Identification and Verification of Potential Biomarkers in Gastric Cancer By Integrated Bioinformatic Analysis

Background: Gastric cancer (GC) is a common cancer with high mortality. This study aimed to identify its differentially expressed genes (DEGs) using bioinformatics methods.

Methods: DEGs were screened from four GEO (Gene Expression Omnibus) gene expression profiles. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. A protein–protein interaction (PPI) network was constructed. Expression and prognosis were assessed. Meta-analysis was conducted to further validate prognosis. The receiver operating characteristic curve (ROC) was analyzed to identify diagnostic markers, and a nomogram was developed. Exploration of drugs and immune cell infiltration analysis were conducted.

Results: Nine up-regulated and three down-regulated hub genes were identified, with close relations to gastric functions, extracellular activities, and structures. Overexpressed Collagen Type VIII Alpha 1 Chain (COL8A1), Collagen Type X Alpha 1 Chain (COL10A1), Collagen Triple Helix Repeat Containing 1 (CTHRC1), and Fibroblast Activation Protein (FAP) correlated with poor prognosis. The area under the curve (AUC) of ADAM Metallopeptidase With Thrombospondin Type 1 Motif 2 (ADAMTS2), COL10A1, Collagen Type XI Alpha 1 Chain (COL11A1), and CTHRC1 was >0.9. A nomogram model based on CTHRC1 was developed. Infiltration of macrophages, neutrophils, and dendritic cells positively correlated with COL8A1, COL10A1, CTHRC1, and FAP. Meta-analysis confirmed poor prognosis of overexpressed CTHRC1.

Conclusion: ADAMTS2, COL10A1, COL11A1, and CTHRC1 have diagnostic values in GC. COL8A1, COL10A1, CTHRC1, and FAP correlated with worse prognosis, showing prognostic and therapeutic values. The immune cell infiltration needs further investigations.

Current Insights on the Impact of Proteomics in Respiratory Allergies

Respiratory allergies affect humans worldwide, causing extensive morbidity and mortality. They include allergic rhinitis (AR), asthma, pollen food allergy syndrome (PFAS), aspirin-exacerbated respiratory disease (AERD), and nasal polyps (NPs). The study of respiratory allergic diseases requires new technologies for early and accurate diagnosis and treatment. Omics technologies provide the tools required to investigate DNA, RNA, proteins, and other molecular determinants. These technologies include genomics, transcriptomics, proteomics, and metabolomics. However, proteomics is one of the main approaches to studying allergic disorders' pathophysiology. Proteins are used to indicate normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. In this field, the principal goal of proteomics has been to discover new proteins and use them in precision medicine. Multiple technologies have been applied to proteomics, but that most used for identifying, quantifying, and profiling proteins is mass spectrometry (MS). Over the last few years, proteomics has enabled the establishment of several proteins for diagnosing and treating respiratory allergic diseases.

Identification of Potential Diagnostic and Prognostic Biomarkers for Gastric Cancer Based on Bioinformatic Analysis

Background: Gastric cancer (GC) is one of the most prevalent cancers all over the world. The molecular mechanisms of GC remain unclear and not well understood. GC cases are majorly diagnosed at the late stage, resulting in a poor prognosis. Advances in molecular biology techniques allow us to get a better understanding of precise molecular mechanisms and enable us to identify the key genes in the carcinogenesis and progression of GC.

Methods: The present study used datasets from the GEO database to screen differentially expressed genes (DEGs) between GC and normal gastric tissues. GO and KEGG enrichments were utilized to analyze the function of DEGs. The STRING database and Cytoscape software were applied to generate protein–protein network and find hub genes. The expression levels of hub genes were evaluated using data from the TCGA database. Survival analysis was conducted to evaluate the prognostic value of hub genes. The GEPIA database was involved to correlate key gene expressions with the pathological stage. Also, ROC curves were constructed to assess the diagnostic value of key genes.

Results: A total of 607 DEGs were identified using three GEO datasets. GO analysis showed that the DEGs were mainly enriched in extracellular structure and matrix organization, collagen fibril organization, extracellular matrix (ECM), and integrin binding. KEGG enrichment was mainly enriched in protein digestion and absorption, ECM-receptor interaction, and focal adhesion. Fifteen genes were identified as hub genes, one of which was excluded for no significant expression between tumor and normal tissues. COL1A1, COL5A2, P4HA3, and SPARC showed high values in prognosis and diagnosis of GC.

Conclusion: We suggest COL1A1, COL5A2, P4HA3, and SPARC as biomarkers for the diagnosis and prognosis of GC.

Collagen Family and Other Matrix Remodeling Proteins Identified by Bioinformatics Analysis as Hub Genes Involved in Gastric Cancer Progression and Prognosis

Gastric cancer has remained in the top five cancers for over ten years, both in terms of incidence and mortality due to the shortage of biomarkers for disease follow-up and effective therapies. Aiming to fill this gap, we performed a bioinformatics assessment on our data and two additional GEO microarray profiles, followed by a deep analysis of the 40 differentially expressed genes identified. PPI network analysis and MCODE plug-in pointed out nine upregulated hub genes coding for proteins from the collagen family (COL12A1, COL5A2, and COL10A1) or involved in the assembly (BGN) or degradation of collagens (CTHRC1), and also associated with cell adhesion (THBS2 and SPP1) and extracellular matrix degradation (FAP, SULF1). Those genes were highly upregulated at the mRNA and protein level, the increase being correlated with pathological T stages. The high expression of BGN (p = 8 × 10⁻¹²), THBS2 (p = 1.2 × 10⁻⁶), CTHRC1 (p = 1.1 × 10⁻⁴), SULF1 (p = 3.8 × 10⁻⁴), COL5A1 (p = 1.3 × 10⁻⁴), COL10A1 (p = 5.7 × 10⁻⁴), COL12A1 (p = 2 × 10⁻³) correlated with poor overall survival and an immune infiltrate based especially on immunosuppressive M2 macrophages (p-value range 4.82 × 10⁻⁷–1.63 × 10⁻¹³). Our results emphasize that these genes could be candidate biomarkers for GC progression and prognosis and new therapeutic targets.

DNA Hypomethylation Is Associated with the Overexpression of INHBA in Upper Tract Urothelial Carcinoma

Urothelial carcinoma includes upper urinary tract cancer (UTUC) and bladder cancer. Although nephroureterectomy is the standard treatment for UTUC, the recurrence rate is approximately half and the tumor is associated with poor prognoses. Metastases are the most devastating and lethal clinical situation in urothelial carcinoma. Despite its clinical importance, few potential diagnostic biomarkers are suitable for early UC detection. We compared high-stage/high-grade urothelial carcinoma tissues to adjacent normal urothelial tissues using methyl-CpG binding domain protein capture for genome-wide DNA methylation analysis. Based on our findings, inhibin βA (INHBA) might be associated with carcinogenesis and metastasis. Further, clinical UC specimens had significant INHBA hypomethylation based on pyrosequencing. INHBA was detected by real-time PCR and immunohistochemistry staining, and was found to be highly expressed in clinical tissues and cell lines of urothelial carcinoma. Further, INHBA depletion was found to significantly reduce BFTC-909 cell growth and migration by INHBA-specific small interfering RNA. Interestingly, a positive correlation was found between SMAD binding and extracellular structure organization with INHBA using gene set enrichment analysis and gene ontology analysis. Together, these results are the first evidence of INHBA promoter hypomethylation and INHBA overexpression in UTUC. INHBA may affect urothelial carcinoma migration by reorganizing the extracellular matrix through the SMAD pathway.

Identification of Differentially Expressed Genes Reveals BGN Predicting Overall Survival and Tumor Immune Infiltration of Gastric Cancer

Gastric cancer (GC) is one of the most widely occurring malignancies worldwide. Although the diagnosis and treatment strategies of GC have been greatly improved in the past few decades, the morbidity and lethality rates of GC are still rising due to lacking early diagnosis strategies and powerful treatments. In this study, a total of 37 differentially expressed genes were identified in GC by analyzing TCGA, GSE118897, GSE19826, and GSE54129. Using the PPI database, we identified 17 hub genes in GC. By analyzing the expression of hub genes and OS, MFAP2, BGN, and TREM1 were related to the prognosis of GC. In addition, our results showed that higher levels of BGN exhibited a significant correlation with shorter OS time in GC. Nomogram analysis showed that the dysregulation of BGN could predict the prognosis of GC. Moreover, we revealed that BGN had a markedly negative correlation with B cells but had positive correlations with CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells in GC samples. The pan-cancer analysis demonstrated that BGN was differentially expressed and related to tumor-infiltrating immune cells across human cancers. This study for the first time comprehensively revealed that BGN was a potential biomarker for the prediction of GC prognosis and tumor immune infiltration.

Evaluation of a five-gene signature associated with stromal infiltration for diffuse large B-cell lymphoma

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is a common non-Hodgkin lymphoma. The development of immunotherapy greatly improves the patient prognosis but there are some exceptions. Thus, screening for better biomarkers for prognostic evaluation could contribute to the treatment of DLBCL patients.

AIM

To screen the novel mediators involved in the development of DLBCL.

METHODS

The GSE60 dataset was applied to identify the differentially expressed genes (DEGs) in DLBCL, and the principal components analysis plot was used to determine the quality of the included samples. The protein-protein interactions were analyzed by the STRING tool. The key hub genes were entered into to the GEPIA database to determine their expressions in DLBCL. Furthermore, these hub gene alterations were analyzed in cBioportal. The UALCAN portal was employed to analyze the expression of the hub genes in different stages of DLBCL. The Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data Score was conducted to evaluate the correlation between the gene expression and tumor purity. The gene-gene correlation analysis was conducted in the GEPIA. The stromal score analysis was conducted in TIMER to confirm the correlation between the gene expression and infiltrated stromal cells. The correlation between the indicated genes and infiltration level of cancer-associated fibroblasts (CAFs) was also completed in TIMER with two methods, MCP-Counter and Tumor immune dysfunction and exclusion. The correlation between fibronectin (FN1) protein level and secreted protein acidic and cysteine-rich (SPARC) messenger ribonucleic acid expression was confirmed in the cBioportal.

RESULTS

The top 20 DEGs in DLBCL were identified, and the principal components analysis plot confirmed the quality of the significant DEGs. The pairwise correlation coefficient analysis among all samples showed that these DEGs have a certain co-expression pattern. The DEGs were subjected to STRING to identify the hub genes, alpha-2-macroglobulin (A2M), cathepsin B (CTSB), FN1, matrix metallopeptidase 9 (MMP9), and SPARC. The five hub genes were confirmed to be overexpressed in DLBCL. The cBioportal portal detected these five hub genes that had gene alteration, including messenger ribonucleic acid high amplification and missense mutation, and the gene alteration percentages of A2M, FN1, CTSB, MMP9, and SPARC were 5%, 8%, 5%, 2.7%, and 5%, respectively. Furthermore, the five hub genes had a potential positive correlation with tumor stage. The correlation analysis between the five genes and tumor purity confirmed that the five genes were overexpressed in DLBCL and had a positive correlation with the development of DLBCL. More interestingly, the five genes had a significant correlation with the stromal infiltration scores. The correlation analysis between the fives genes and CAFs also showed a significant value, among which the top two genes, FN1 and SPARC, had a remarkable co-expression pattern.

CONCLUSION

The top DEGs were identified, and the five hub genes were overexpressed in DLBCL. Furthermore, the gene alterations were confirmed and the positive correlation with tumor purity revealed the overexpression of the five genes and close association with the development of DLBCL. More interestingly, the five genes were positively correlated with stromal infiltration, especially in CAFs. The top two genes, FN1 and SPARC, showed a co-expression pattern, which indicates their potential as novel therapeutic targets for DLBCL.

Identification and Validation of SNP-Containing Genes With Prognostic Value in Gastric Cancer via Integrated Bioinformatics Analysis

Background

Gastric cancer is one of the most common malignancies worldwide. Although the diagnosis and treatment of this disease have substantially improved in recent years, the five-year survival rate of gastric cancer is still low due to local recurrence and distant metastasis. An in-depth study of the molecular pathogenesis of gastric cancer and related prognostic markers will help improve the quality of life and prognosis of patients with this disease. The purpose of this study was to identify and verify key SNPs in genes with prognostic value for gastric cancer.

Methods

SNP-related data from gastric cancer patients were obtained from The Cancer Genome Atlas (TCGA) database, and the functions and pathways of the mutated genes were analyzed using DAVID software. A protein-protein interaction (PPI) network was constructed using the STRING database and visualized by Cytoscape software, and molecular complex detection (MCODE) was used to screen the PPI network to extract important mutated genes. Ten hub genes were identified using cytoHubba, and the expression levels and the prognostic value of the central genes were determined by UALCAN and Kaplan-Meier Plotter. Finally, quantitative PCR and Western blotting were used to verify the expression of the hub genes in gastric cancer cells.

Results

From the database, 945 genes with mutations in more than 25 samples were identified. The PPI network had 360 nodes and 1616 edges. Finally, cytoHubba identified six key genes (TP53, HRAS, BRCA1, PIK3CA, AKT1, and SMARCA4), and their expression levels were closely related to the survival rate of gastric cancer patients.

Conclusion

Our results indicate that TP53, HRAS, BRCA1, PIK3CA, AKT1, and SMARCA4 may be key genes for the development and prognosis of gastric cancer. Our research provides an important bioinformatics foundation and related theoretical foundation for further exploring the molecular pathogenesis of gastric cancer and evaluating the prognosis of patients.

Identification of key methylation differentially expressed genes in posterior fossa ependymoma based on epigenomic and transcriptome analysis

Background

Posterior fossa ependymoma (EPN-PF) can be classified into Group A posterior fossa ependymoma (EPN-PFA) and Group B posterior fossa ependymoma (EPN-PFB) according to DNA CpG island methylation profile status and gene expression. EPN-PFA usually occurs in children younger than 5 years and has a poor prognosis.

Methods

Using epigenome and transcriptome microarray data, a multi-component weighted gene co-expression network analysis (WGCNA) was used to systematically identify the hub genes of EPN-PF. We downloaded two microarray datasets (GSE66354 and GSE114523) from the Gene Expression Omnibus (GEO) database. The Limma R package was used to identify differentially expressed genes (DEGs), and ChAMP R was used to analyze the differential methylation genes (DMGs) between EPN-PFA and EPN-PFB. GO and KEGG enrichment analyses were performed using the Metascape database.

Results

GO analysis showed that enriched genes were significantly enriched in the extracellular matrix organization, adaptive immune response, membrane raft, focal adhesion, NF-kappa B pathway, and axon guidance, as suggested by KEGG analysis. Through WGCNA, we found that MEblue had a significant correlation with EPN-PF (R = 0.69, P = 1 × 10^–08) and selected the 180 hub genes in the blue module. By comparing the DEGs, DMGs, and hub genes in the co-expression network, we identified five hypermethylated, lower expressed genes in EPN-PFA (ATP4B, CCDC151, DMKN, SCN4B, and TUBA4B), and three of them were confirmed by IHC.

Conclusion

ssGSEA and GSVA analysis indicated that these five hub genes could lead to poor prognosis by inducing hypoxia, PI3K-Akt-mTOR, and TNFα-NFKB pathways. Further study of these dysmethylated hub genes in EPN-PF and the pathways they participate in may provides new ideas for EPN-PF treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02834-1.

α-Enolase Lies Downstream of mTOR/HIF1α and Promotes Thyroid Carcinoma Progression by Regulating CST1

Novel therapy strategies are crucial for thyroid carcinoma treatment. It is increasingly important to clarify the mechanism of thyroid carcinoma progression. Several studies demonstrate that α-Enolase (ENO1) participates in cancer development; nevertheless, the role of ENO1 in thyroid carcinoma progression remains unclear. In the present study, we found that the expression of ENO1 was upregulated in thyroid carcinoma samples. Proliferation and migration of thyroid carcinoma cells were suppressed by depletion of ENO1; conversely, ENO1 overexpression promoted thyroid carcinoma cell growth and invasion. To elucidate the mechanisms, we found that the hypoxia-related mTOR/HIF1 pathway regulated ENO1 expression. ENO1 regulated the expression of CST1; knockdown of CST1 reversed the tumorigenicity enhanced by ENO1 overexpression. Taken together, our findings provide a theoretical foundation for thyroid carcinoma treatment.

The Reprimo-Like Gene Is an Epigenetic-Mediated Tumor Suppressor and a Candidate Biomarker for the Non-Invasive Detection of Gastric Cancer

Reprimo-like (RPRML) is an uncharacterized member of the Reprimo gene family. Here, we evaluated the role of RPRML and whether its regulation by DNA methylation is a potential non-invasive biomarker of gastric cancer. RPRML expression was evaluated by immunohistochemistry in 90 patients with gastric cancer and associated with clinicopathologic characteristics and outcomes. The role of RPRML in cancer biology was investigated in vitro, through RPRML ectopic overexpression. Functional experiments included colony formation, soft agar, MTS, and Ki67 immunofluorescence assays. DNA methylation-mediated silencing was evaluated by the 5-azacytidine assay and direct bisulfite sequencing. Non-invasive detection of circulating methylated RPRML DNA was assessed in 25 gastric cancer cases and 25 age- and sex-balanced cancer-free controls by the MethyLight assay. Downregulation of RPRML protein expression was associated with poor overall survival in advanced gastric cancer. RPRML overexpression significantly inhibited clonogenic capacity, anchorage-independent growth, and proliferation in vitro. Circulating methylated RPRML DNA distinguished patients with gastric cancer from controls with an area under the curve of 0.726. The in vitro overexpression results and the poor patient survival associated with lower RPRML levels suggest that RPRML plays a tumor-suppressive role in the stomach. Circulating methylated RPRML DNA may serve as a biomarker for the non-invasive detection of gastric cancer.