DNA methylation signatures associated with prognosis of gastric cancer

Abstract Background Few studies have examined prognostic outcomes-associated molecular signatures other than overall survival (OS) for gastric cancer (GC). We aimed to identify DNA methylation biomarkers associated with multiple prognostic outcomes of GC in an epigenome-wide association study. Methods Based on the Cancer Genome Atlas (TCGA), DNA methylation loci associated with OS (n = 381), disease-specific survival (DSS, n = 372), and progression-free interval (PFI, n = 383) were discovered in training set subjects (false discovery rates < 0.05) randomly selected for each prognostic outcome and were then validated in remaining subjects (P-values < 0.05). Key CpGs simultaneously validated for OS, DSS, and PFI were further assessed for disease-free interval (DFI, n = 247). Gene set enrichment analyses were conducted to explore the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways simultaneously enriched for multiple GC prognostic outcomes. Methylation correlated blocks (MCBs) were identified for co-methylation patterns associated with GC prognosis. Based on key CpGs, risk score models were established to predict four prognostic outcomes. Spearman correlation analyses were performed between key CpG sites and their host gene mRNA expression. Results We newly identified DNA methylation of seven CpGs significantly associated with OS, DSS, and PFI of GC, including cg10399824 (GRK5), cg05275153 (RGS12), cg24406668 (MMP9), cg14719951(DSC3), and cg25117092 (MED12L), and two in intergenic regions (cg11348188 and cg11671115). Except cg10399824 and cg24406668, five of them were also significantly associated with DFI of GC. Neuroactive ligand-receptor interaction pathway was suggested to play a key role in the effect of DNA methylation on GC prognosis. Consistent with individual CpG-level association, three MCBs involving cg11671115, cg14719951, and cg24406668 were significantly associated with multiple prognostic outcomes of GC. Integrating key CpG loci, two risk score models performed well in predicting GC prognosis. Gene body DNA methylation of cg14719951, cg10399824, and cg25117092 was associated with their host gene expression, whereas no significant associations between their host gene expression and four clinical prognostic outcomes of GC were observed. Conclusions We newly identified seven CpGs associated with OS, DSS, and PFI of GC, with five of them also associated with DFI, which might inform patient stratification in clinical practices.

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DNA Methylation Signatures Associated With Prognosis of Gastric Cancer

10.21203/rs.3.rs-200490/v1 ◽

2021 ◽

Author(s):

Jin Dai ◽

Akihiro Nishi ◽

Zhe-Xuan Li ◽

Yang Zhang ◽

Tong Zhou ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Dna Methylation ◽

Risk Score ◽

Host Gene ◽

The Cancer Genome Atlas ◽

Receptor Interaction ◽

Host Gene Expression ◽

Clinical Practices ◽

Free Interval

Abstract Background: Few studies have examined prognostic outcomes-associated molecular signatures other than overall survival (OS) for gastric cancer (GC). We aimed to identify DNA methylation biomarkers associated with multiple prognostic outcomes of GC in an epigenome-wide association study.Methods: Based on the Cancer Genome Atlas (TCGA), DNA methylation loci associated with OS (n=381), disease-specific survival (DSS, n=372), and progression-free interval (PFI, n=383) were discovered in training set subjects (false discovery rates < 0.05) randomly selected for each prognostic outcome and were then validated in remaining subjects (P-values < 0.05). Key CpGs simultaneously validated for OS, DSS, and PFI were further assessed for disease-free interval (DFI, n=247). Gene set enrichment analyses were conducted to explore the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways simultaneously enriched for multiple GC prognostic outcomes. Methylation correlated blocks (MCBs) were identified for co-methylation patterns associated with GC prognosis. Based on key CpGs, risk score models were established to predict four prognostic outcomes. Spearman correlation analyses were performed between key CpG sites and their host gene mRNA expression.Results: We newly identified DNA methylation of seven CpGs significantly associated with OS, DSS, and PFI of GC, including cg10399824 (GRK5), cg05275153 (RGS12), cg24406668 (MMP9), cg14719951(DSC3), and cg25117092 (MED12L), and two in intergenic regions (cg11348188 and cg11671115). Except cg10399824 and cg24406668, five of them were also significantly associated with DFI of GC. Neuroactive ligand-receptor interaction pathway was suggested to play a key role in the effect of DNA methylation on GC prognosis. Consistent with individual CpG-level association, three MCBs involving cg11671115, cg14719951, and cg24406668 were significantly associated with multiple prognostic outcomes of GC. Integrating key CpG loci, two risk score models performed well in predicting GC prognosis. Gene body DNA methylation of cg14719951, cg10399824, and cg25117092 was associated with their host gene expression, whereas no significant associations between their host gene expression and four clinical prognostic outcomes of GC were observed. Conclusions: We newly identified seven CpGs associated with OS, DSS, and PFI of GC, with five of them also associated with DFI, which might inform patient stratification in clinical practices.

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A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽

10.3390/genes12060854 ◽

2021 ◽

Vol 12 (6) ◽

pp. 854

Author(s):

Yishu Wang ◽

Lingyun Xu ◽

Dongmei Ai

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Dna Methylation ◽

Regression Model ◽

The Cancer Genome Atlas ◽

Low Rank ◽

Expression Levels ◽

Rank Regression ◽

Prediction Of Prognosis ◽

Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

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Epigenetic Variation Analysis Leads to Biomarker Discovery in Gastric Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2020.551787 ◽

2020 ◽

Vol 11 ◽

Author(s):

Yan Zhang ◽

Dianjing Guo

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Dna Methylation ◽

Gastric Adenocarcinoma ◽

Histone Modifications ◽

Biomarker Discovery ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Integrated Analysis ◽

Epigenetic Variation

As one of the most common malignant tumors worldwide, gastric adenocarcinoma (GC) and its prognosis are still poorly understood. Various genetic and epigenetic factors have been indicated in GC carcinogenesis. However, a comprehensive and in-depth investigation of epigenetic alteration in gastric cancer is still missing. In this study, we systematically investigated some key epigenetic features in GC, including DNA methylation and five core histone modifications. Data from The Cancer Genome Atlas Program and other studies (Gene Expression Omnibus) were collected, analyzed, and validated with multivariate statistical analysis methods. The landscape of epi-modifications in gastric cancer was described. Chromatin state transition analysis showed a histone marker shift in gastric cancer genome by employing a Hidden-Markov-Model based approach, indicated that histone marks tend to label different sets of genes in GC compared to control. An additive effect of these epigenetic marks was observed by integrated analysis with gene expression data, suggesting epigenetic modifications may cooperatively regulate gene expression. However, the effect of DNA methylation was found more significant without the presence of the five histone modifications in our study. By constructing a PPI network, key genes to distinguish GC from normal samples were identified, and distinct patterns of oncogenic pathways in GC were revealed. Some of these genes can also serve as potential biomarkers to classify various GC molecular subtypes. Our results provide important insights into the epigenetic regulation in gastric cancer and other cancers in general. This study describes the aberrant epigenetic variation pattern in GC and provides potential direction for epigenetic biomarker discovery.

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Progressive and Prognostic Performance of an Extracellular Matrix-Receptor Interaction Signature in Gastric Cancer

Disease Markers ◽

10.1155/2020/8816070 ◽

2020 ◽

Vol 2020 ◽

pp. 1-23

Author(s):

Xiangchou Yang ◽

Liping Chen ◽

Yuting Mao ◽

Zijing Hu ◽

Muqing He

Keyword(s):

Gastric Cancer ◽

Extracellular Matrix ◽

Risk Score ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Receptor Interaction ◽

Clinicopathologic Features ◽

Normal Tissues ◽

Prognostic Application ◽

Score Model

The role of an extracellular matrix- (ECM-) receptor interaction signature has not been fully clarified in gastric cancer. This study performed comprehensive analyses on the differentially expressed ECM-related genes, clinicopathologic features, and prognostic application in gastric cancer. The differentially expressed genes between tumorous and matched normal tissues in The Cancer Genome Atlas (TCGA) and validation cohorts were identified by a paired t -test. Consensus clusters were built to find the correlation between clinicopathologic features and subclusters. Then, the least absolute shrinkage and selection operator (lasso) method was used to construct a risk score model. Correlation analyses were made to reveal the relation between risk score-stratified subgroups and clinicopathologic features or significant signatures. In TCGA (26 pairs) and validation cohort (134 pairs), 25 ECM-related genes were significantly highly expressed and 11 genes were downexpressed in gastric cancer. ECM-based subclusters were slightly related to clinicopathologic features. We constructed a risk score model = 0.081 ∗ log 2 CD 36 + 0.043 ∗ log 2 COL 5 A 2 + 0.001 ∗ log 2 ITGB 5 + 0.039 ∗ log 2 SDC 2 + 0.135 ∗ log 2 SV 2 B + 0.012 ∗ log 2 THBS 1 + 0.068 ∗ log 2 VTN + 0.023 ∗ log 2 VWF . The risk score model could well predict the outcome of patients with gastric cancer in both training ( n = 351 , HR: 1.807, 95% CI: 1.292-2.528, P = 0.00046 ) and validation ( n = 300 , HR: 1.866, 95% CI: 1.347-2.584, P = 0.00014 ) cohorts. Besides, risk score-based subgroups were associated with angiogenesis, cell adhesion molecules, complement and coagulation cascades, TGF-beta signaling, and mismatch repair-relevant signatures ( P < 0.0001 ). By univariate (1.845, 95% CI: 1.382-2.462, P < 0.001 ) and multivariate (1.756, 95% CI: 1.284-2.402, P < 0.001 ) analyses, we regarded the risk score as an independent risk factor in gastric cancer. Our findings revealed that ECM compositions became accomplices in the tumorigenesis, progression, and poor survival of gastric cancer.

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Effect of LAMA4 on Prognosis and Its Correlation with Immune Infiltration in Gastric Cancer

BioMed Research International ◽

10.1155/2021/6428873 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Mingming Wang ◽

Changzheng Li ◽

Ying Liu ◽

Zuomin Wang

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Immune Cell ◽

Expression Patterns ◽

Disease Free Survival ◽

Genetic Alterations ◽

Clinicopathological Characteristics ◽

Basement Membranes ◽

The Cancer Genome Atlas ◽

Receptor Interaction

Background. Laminin alpha 4 (LAMA4) is widely distributed in the basement membranes of various tissues. It can regulate cancer cell proliferation and migration. We investigated the effects of LAMA4 in gastric cancer (GC). Methods. LAMA4 expression patterns were analyzed in GC using the Gene Expression Omnibus (GEO), Gene Expression Profiling Interactive Analysis (GEPIA), and UALCAN. Correlations between LAMA4 expression and clinicopathological characteristics were evaluated using data from The Cancer Genome Atlas (TCGA). The survival analysis was examined using the Kaplan-Meier plotter and GEPIA and ascertained by multivariate Cox analysis. Genetic alterations and DNA methylation of LAMA4 were analyzed using cBioPortal and MethSurv. LinkedOmics was applied to identify coexpressed genes of LAMA4. The association between LAMA4 and infiltration of immune cells was explored using Tumor Immune Estimation Resource (TIMER) and GEPIA. Results. LAMA4 was highly expressed in GC, and its upregulation significantly correlated with T classification ( P = 0.040 ). LAMA4 expression was an independent risk factor for overall survival (OS, P = 0.033 ). Patients with genetic alterations of LAMA4 showed a significantly better disease-free survival (DFS, P = 0.022 ). Ten CpG sites of LAMA4 were significantly associated with prognosis in GC. The functions of LAMA4 and coexpression genes were mainly involved in extracellular matrix (ECM) receptor interaction. LAMA4 expression significantly correlated with infiltration of macrophages ( P < 0.001 ), CD4+ T cells ( P < 0.001 ), and dendritic cells ( P < 0.001 ). Furthermore, LAMA4 expression was significantly associated with markers of M2 and tumor-associated macrophages (TAMs). Conclusion. LAMA4 expression was linked to GC prognosis and immune cell infiltration, indicating its potential use as a prognostic biomarker and therapeutic target.

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A signature of seven immune‐related genes predicts overall survival in male gastric cancer patients

Cancer Cell International ◽

10.1186/s12935-021-01823-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xin Xu ◽

Yida Lu ◽

Youliang Wu ◽

Mingliang Wang ◽

Xiaodong Wang ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Overall Survival ◽

Risk Score ◽

Cox Regression ◽

External Validation ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Survival Prediction ◽

Immune Related Genes

Abstract Background Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC. Methods RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT. Results A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment. Conclusions Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

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Identification and validation of a novel ferroptosis-related gene model for predicting the prognosis of gastric cancer patients

PLoS ONE ◽

10.1371/journal.pone.0254368 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254368

Author(s):

Gang Liu ◽

Jian-ying Ma ◽

Gang Hu ◽

Huan Jin

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Risk Score ◽

Cox Regression ◽

Predictive Accuracy ◽

Expression Profiles ◽

Critical Role ◽

Clinicopathological Features ◽

The Cancer Genome Atlas ◽

Risk Scores

Background Ferroptosis is a novel form of regulated cell death that plays a critical role in tumorigenesis. The purpose of this study was to establish a ferroptosis-associated gene (FRG) signature and assess its clinical outcome in gastric cancer (GC). Methods Differentially expressed FRGs were identified using gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were performed to construct a prognostic signature. The model was validated using an independent GEO dataset, and a genomic-clinicopathologic nomogram integrating risk scores and clinicopathological features was established. Results An 8-FRG signature was constructed to calculate the risk score and classify GC patients into two risk groups (high- and low-risk) according to the median value of the risk score. The signature showed a robust predictive capacity in the stratification analysis. A high-risk score was associated with advanced clinicopathological features and an unfavorable prognosis. The predictive accuracy of the signature was confirmed using an independent GSE84437 dataset. Patients in the two groups showed different enrichment of immune cells and immune-related pathways. Finally, we established a genomic-clinicopathologic nomogram (based on risk score, age, and tumor stage) to predict the overall survival (OS) of GC patients. Conclusions The novel FRG signature may be a reliable tool for assisting clinicians in predicting the OS of GC patients and may facilitate personalized treatment.

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A signature of seven immune-related genes predicts overall survival in male gastric cancer patients

10.21203/rs.3.rs-52209/v2 ◽

2021 ◽

Author(s):

Xin Xu ◽

Yida Lu ◽

Youliang Wu ◽

Mingliang Wang ◽

Xiaodong Wang ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Overall Survival ◽

Risk Score ◽

Cox Regression ◽

External Validation ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Survival Prediction ◽

Immune Related Genes

Abstract Background: Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC.Method: RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT.Results: A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment.Conclusions: Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

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