Yonsei Med J.  2019 Nov;60(11):1013-1020. 10.3349/ymj.2019.60.11.1013.

A Two-DNA Methylation Signature to Improve Prognosis Prediction of Clear Cell Renal Cell Carcinoma

Affiliations
  • 1Diabetes Center, Zhejiang Provincial Key Laboratory of Pathophysiology, Institute of Biochemistry and Molecular Biology, Medical School of Ningbo University, Ningbo, Zhejiang, China. xiyang@nbu.edu.cn
  • 2Department of Preventative Medicine, Medical School of Ningbo University, Ningbo, Zhejiang, China.

Abstract

PURPOSE
Effective biomarkers and models are needed to improve the prognostic prospects of clear cell renal cell carcinoma (ccRCC). The purpose of this work was to identify DNA methylation biomarkers and to evaluate the utility of DNA methylation analysis for ccRCC prognosis.
MATERIALS AND METHODS
An overview of genome-wide methylation of ccRCC tissues derived from The Cancer Genome Atlas (TCGA) database was download for analysis. DNA methylation signatures were identified using Cox regression methods. The potential clinical significance of methylation biomarkers acting as a novel prognostic markers was analyzed using the Kaplan-Meier method and receiver operating characteristic (ROC) curves.
RESULTS
This study analyzed data for 215 patients with information on 23171 DNA methylation sites and identified a two-DNA methylation signature (cg18034859, cg24199834) with the help of a step-wise multivariable Cox regression model. The area under the curve of ROCs for the two-DNA methylation signature was 0.819. The study samples were stratified into low- and high-risk classifications based on an optimal threshold, and the two groups showed markedly different survival rates. Moreover, the two-DNA methylation marker was suitable for patients of varying ages, sex, stages (I and IV), and histologic grade (G2).
CONCLUSION
The two-DNA methylation signature was deemed to be a potential novel prognostic biomarker of use in increasing the accuracy of predicting overall survival of ccRCC patients.

Keyword

Carcinoma; renal cell; biomarkers; DNA methylation; prognosis

MeSH Terms

Biomarkers
Carcinoma, Renal Cell*
Classification
DNA Methylation
Genome
Humans
Methods
Methylation*
Prognosis*
ROC Curve
Survival Rate
Biomarkers

Figure

  • Fig. 1 Flow-chart of the study. The order of analyses to develop the risk score model and to validate the efficiency of the signature to predict prognostic outcomes. TCGA, The Cancer Genome Atlas; ccRCC, clear cell renal cell carcinoma.

  • Fig. 2 Evaluation of the predictive performance of the two-DNA methylation signature. (A) Receiver operating characteristic analysis of the sensitivity and specificity for survival time by the two-DNA methylation signature in the training dataset. The black dot represents the optimal validation dataset using the two-DNA methylation signature. (B) The Kaplan-Meier analysis was used to visualize the survival probability for the low-risk versus high-risk groups of patients based on the optimal cut-off point. Rows represent survival time (days), and columns represent survival rate. TPR, true positive rate; FPR, false positive rate; AUC, area under the curve of receiver operating characteristics.

  • Fig. 3 Risk score analysis of the training set. The distribution of two-DNA methylation based risk core, patient survival, and methylation levels of two CpGs were analyzed in the training set (n=129). (A) Two-DNA methylation signature risk score distribution. ‘Time’ means ‘survival time’. (B) Heat-map of the DNA methylation profiles. Rows represent CpG sites, and columns represent patients.

  • Fig. 4 Stratified analysis. Kaplan-Meier and receiver operating characteristic (ROC) analyses of patients with clear cell renal cell carcinoma of different age. Grouping was based on age at initial diagnosis: ≤60 (n=106, 49.53%), >60 (n=108, 50.47%). (A) Kaplan-Meier analysis with two-sided log-rank test was performed to estimate the differences in survival time between the low-risk and high-risk patients. (B) ROC curves of the two-DNA methylation signature were used to demonstrate the sensitivity and specificity in predicting the survival time of patients. TPR, true positive rate; FPR, false positive rate; AUC, area under the curve of receiver operating characteristics.


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