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Cancer Survival Analysis01:21

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Comparing the Survival Analysis of Two or More Groups01:20

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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MethSurv: a web tool to perform multivariable survival analysis using DNA methylation data.

Vijayachitra Modhukur1, Tatjana Iljasenko1, Tauno Metsalu1

  • 1Institute of Computer Science, University of Tartu, 50409 Tartu, Estonia.

Epigenomics
|December 22, 2017
PubMed
Summary

MethSurv is a web tool for cancer survival analysis using CpG methylation data. It helps researchers identify potential methylation-based biomarkers across 25 cancer types without coding skills.

Keywords:
Cox Proportional-HazardsDNA methylationIllumina 450 KKaplan–MeierTCGAbiomarkerscancer survival analysisclusteringepigeneticsprognosis

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • CpG methylation patterns are increasingly recognized as crucial regulators of gene expression and have emerged as significant biomarkers in cancer.
  • Understanding the relationship between methylation and patient survival is vital for developing targeted therapies and improving clinical outcomes.

Purpose of the Study:

  • To develop an accessible web-based tool, MethSurv, for performing survival analysis utilizing CpG methylation data.
  • To provide researchers, including those without programming expertise, with a platform for exploring methylation-based cancer biomarkers.

Main Methods:

  • Utilized The Cancer Genome Atlas (TCGA) methylome data comprising 7358 samples from 25 human cancer types.
  • Developed an interactive web interface employing the Cox proportional-hazards model for survival analysis.
  • Integrated functionalities for CpG-based survival analysis, cluster analysis of methylation patterns, and identification of top cancer-specific biomarkers.

Main Results:

  • MethSurv facilitates survival analysis for CpGs in proximity to query genes across diverse cancer types.
  • The tool enables cluster analysis to associate methylation patterns with clinical characteristics.
  • Users can browse top methylation-based biomarkers identified for each of the 25 cancer types.

Conclusions:

  • MethSurv serves as a valuable, user-friendly platform for researchers to conduct preliminary assessments of methylation-based cancer biomarkers.
  • The tool democratizes the analysis of methylome data for survival prediction, aiding in biomarker discovery and cancer research.