ABSTRACT
AIM: To develop a web tool for survival analysis based on CpG methylation patterns. MATERIALS & METHODS: We utilized methylome data from 'The Cancer Genome Atlas' and used the Cox proportional-hazards model to develop an interactive web interface for survival analysis. RESULTS: MethSurv enables survival analysis for a CpG located in or around the proximity of a query gene. For further mining, cluster analysis for a query gene to associate methylation patterns with clinical characteristics and browsing of top biomarkers for each cancer type are provided. MethSurv includes 7358 methylomes from 25 different human cancers. CONCLUSION: The MethSurv tool is a valuable platform for the researchers without programming skills to perform the initial assessment of methylation-based cancer biomarkers.
Subject(s)
Computational Biology/methods , DNA Methylation , DNA, Neoplasm/genetics , Epigenesis, Genetic , Neoplasms/genetics , Software , Atlases as Topic , Cluster Analysis , CpG Islands , DNA, Neoplasm/metabolism , Data Mining , Genome, Human , Humans , Kaplan-Meier Estimate , Molecular Sequence Annotation , Neoplasms/diagnosis , Neoplasms/metabolism , Neoplasms/mortality , Proportional Hazards ModelsABSTRACT
Polygenic risk scores are gaining more and more attention for estimating genetic risks for liabilities, especially for noncommunicable diseases. They are now calculated using thousands of DNA markers. In this paper, we compare the score distributions of two previously published very large risk score models within different populations. We show that the risk score model together with its risk stratification thresholds, built upon the data of one population, cannot be applied to another population without taking into account the target population's structure. We also show that if an individual is classified to the wrong population, his/her disease risk can be systematically incorrectly estimated.
