Dataset Information

A four-DNA methylation signature as a novel prognostic biomarker for survival of patients with gastric cancer.

ABSTRACT:

Background

Gastric cancer (GC) is the fifth most frequently diagnosed cancer and the third leading cause of cancer-related mortality. Lack of prognostic indicators for patient survival hinders GC treatment and survival.

Methods and results

Methylation profile data of patients with GC obtained from The Cancer Genome Atlas (TCGA) database were analyzed to identify methylation sites as biomarkers for GC prognosis. The cohort was divided into training and validation sets. Univariate Cox, LASSO regression,and multivariate Cox analyses revealed a close correlation of a four-DNA methylation signature as a risk score model with the overall survival of patients with GC. The survival between high-risk and low-risk score patients with GC was significantly different. Analyses of receiver operating characteristics revealed a high prognostic accuracy of the four-DNA methylation signature in patients with GC. The subgroup analysis indicated that the accuracy included that for anatomical region, histologic grade, TNM stage, pathological stage, and sex. The GC prognosis based on the four-DNA methylation signature was more precise than that based on known biomarkers.

Conclusions

The four-DNA methylation signature could serve as a novel independent prognostic factor that could be an important tool to predict the prognostic outcome of GC patients. This potential must be verified in a large-scale population cohort study and through basic research studies.

SUBMITTER: Li C

PROVIDER: S-EPMC7085204 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

A four-DNA methylation signature as a novel prognostic biomarker for survival of patients with gastric cancer.

Li Chunmei C Zheng Ya Y Pu Ke K Zhao Da D Wang Yuping Y Guan Quanlin Q Zhou Yongning Y

Cancer cell international 20200320

<h4>Background</h4>Gastric cancer (GC) is the fifth most frequently diagnosed cancer and the third leading cause of cancer-related mortality. Lack of prognostic indicators for patient survival hinders GC treatment and survival.<h4>Methods and results</h4>Methylation profile data of patients with GC obtained from The Cancer Genome Atlas (TCGA) database were analyzed to identify methylation sites as biomarkers for GC prognosis. The cohort was divided into training and validation sets. Univariate C ...[more]

PMID: 32206039

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Project description:BackgroundThe current Union International Committee on Cancer or the American Joint Committee on Cancer TNM stage system has shown valuable but insufficient estimation for subsets of gastric cancer and prediction for prognosis patients. Thus, there is an urgent need to identify diagnostic, prognostic, and predictive biomarkers to improve patients' outcomes. Our aim was to perform an integrative analysis on publicly available datasets to identify epigenetic changes that may play key role in the initiation and progression of gastric cancer, based on which we set to develop a DNA methylation signature to improve survival prediction of gastric cancer.ResultsA total of 340 methylation-related differentially expression genes (mrDEGs) were screened in gastric cancer patients from The Cancer Genome Atlas (TCGA) project. Pathway enrichment analysis revealed that they were involved in the biological process related to initiation and progression of gastric cancer. Based on the mrDEGs identified, we developed a DNA methylation signature consisting of ten gene members (SCNN1B, NFE2L3, CLDN2, RBPMS2, JPH2, GBP6, COL4A5, SMKR1, PPP1R14A, and ARL4D) according to their methylation β value. This innovative DNA methylation signature was associated with cancer recurrence, while it showed independence of cancer recurrence and TNM stage for survival prediction. Combination of this DNA methylation signature and TNM stage improved overall survival prediction in the receiver operating characteristic analysis. We also verified that two individual genes (PPP1R14A and SCNN1B) of the identified prognostic signature were regulated by promoter region methylation in a panel of gastric cell lines.ConclusionsThis study presents a powerful DNA methylation signature by performing analyses integrating multi-source data including transcriptome, methylome, and clinical outcome of gastric cancer patients from TCGA. The identified DNA methylation signature may be used to refine the current prognostic model and facilitate further stratification of patients in the future clinical trials. Further experimental studies are warranted to unveil the regulatory mechanism and functional role of all the individual genes of the DNA methylation signature. Also, clinical investigations in large GC patient cohorts are greatly needed to validate our findings.

Project description:BackgroundThis study aimed to explore the prognostic role of DNA methylation pattern in endometrial cancer (EC) patients.MethodsDifferentially methylated genes (DMGs) of EC patients with distinct survival from The Cancer Genome Atlas (TCGA) database were analyzed to identify methylated genes as biomarkers for EC prognosis. The Least Absolute Shrinkage and Selection Operator (LASSO) analysis was used to construct a risk score model. A nomogram was built based on analysis combining the risk score model with clinicopathological signatures together, and then verified in the validation cohort and patients in our own center.ResultsIn total, 157 DMGs were identified between different prognostic groups. Based on the LASSO analysis, five genes (GBP4, OR8K3, GABRA2, RIPPLY2, and TRBV5-7) were screened for the establishment of risk score model. The model outperformed in prognostic accuracy at varying follow-up times (AUC for 3 years: 0.824, 5 years: 0.926, and 7 years: 0.853). Multivariate analysis identified four independent risk factors including menopausal status (HR = 3.006, 95%CI: 1.062-8.511, p = 0.038), recurrence (HR = 2.116, 95%CI: 1.061-4.379, p = 0.046), lymph node metastasis (LNM, HR = 3.465, 95%CI: 1.225-9.807, p = 0.019), and five-DNA methylation risk model (HR = 3.654, 95%CI: 1.458-9.161, p = 0.006) in training cohort. The performance of the nomogram was good in the training (AUC = 0.828), validation (AUC = 0.866) and the whole cohorts (AUC = 0.843). Furthermore, we verified the nomogram with 24 patients in our center and the Kaplan-Meier survival curve also proved to be significantly different (p < 0.01). The subgroup analysis in different stratifications indicated that the accuracy was high in different subgroups for age, histological type, tumor grade, and clinical stage (all p < 0.01).ConclusionsBriefly, our work established and verified a five-DNA methylation risk model, and a nomogram merging the model with clinicopathological characteristics to facilitate individual prediction of EC patients for clinicians.

Dataset Information

A four-DNA methylation signature as a novel prognostic biomarker for survival of patients with gastric cancer.

Background

Methods and results

Conclusions

Publications

A four-DNA methylation signature as a novel prognostic biomarker for survival of patients with gastric cancer.

Similar Datasets

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