Vijayachitra Modhukur1, Tatjana Iljasenko1, Tauno Metsalu1, Kaie Lokk2, Triin Laisk-Podar3,4, Jaak Vilo1,5. 1. Institute of Computer Science, University of Tartu, 50409 Tartu, Estonia. 2. United Laboratories of Tartu University Hospital, Tartu University Hospital, 50406 Tartu, Estonia. 3. Competence Centre on Health Technologies, 50410 Tartu, Estonia. 4. Women's Clinic, Institute of Clinical Medicine, University of Tartu, 50406 Tartu, Estonia. 5. Health Data Analytics, Software Technologies & Applications Competence Center STACC, Ülikooli 2, 51003 Tartu, Estonia.
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.
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 humancancers. CONCLUSION: The MethSurv tool is a valuable platform for the researchers without programming skills to perform the initial assessment of methylation-based cancer biomarkers.
Entities:
Keywords:
Cox Proportional-Hazards; DNA methylation; Illumina 450 K; Kaplan–Meier; TCGA; biomarkers; cancer survival analysis; clustering; epigenetics; prognosis