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Cancer Survival Analysis

Cancer Survival Analysis

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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Related Experiment Video

Updated: Apr 23, 2026

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

PDMSA: A Web-Based Tool for Pan-Cancer Survival Analysis Using DNA Methylation Levels as Biomarkers.

Weiwei Guo^1,2, Ying Shi¹, Shanshan Wu^1,2

¹Department of Oncology Zhujiang Hospital The First School of Clinical Medicine Southern Medical University Guangzhou China.

Advanced Genetics (Hoboken, N.J.)

|April 22, 2026

Summary

This summary is machine-generated.

Pan-cancer DNA Methylation Survival Analysis (PDMSA) is a new web tool that analyzes DNA methylation and survival data for 39 cancer types. It helps researchers understand cancer development and improve patient outcomes.

Keywords:

DNA methylation R Shiny biomarker cancer survival analysis

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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

Area of Science:

Oncology
Genomics
Bioinformatics

Background:

DNA methylation is crucial in cancer development, progression, and treatment response.
Analyzing methylation-prognosis links aids in understanding cancer mechanisms and improving patient survival.
Existing web tools for methylation and survival analysis have limitations.

Purpose of the Study:

To develop a user-friendly web-based tool for analyzing pan-cancer DNA methylation and survival data.
To integrate large-scale DNA methylation and clinical data from public repositories.
To facilitate research into the relationship between gene methylation and cancer prognosis.

Main Methods:

Developed the Pan-cancer DNA Methylation Survival Analysis (PDMSA) tool using the Shiny web application framework.
Integrated DNA methylation and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases.
Implemented prognostic Kaplan-Meier survival and Cox regression analyses with customizable visualization options.

Main Results:

PDMSA incorporates data from 30 TCGA and 15 GEO datasets, covering 39 cancer types, 19,909 genes, and 8,369 samples.
The tool provides two distinct cutoff value grouping methods for survival analysis.
Offers customizable visualization of survival analysis results.

Conclusions:

PDMSA is a comprehensive and user-friendly platform for exploring DNA methylation and cancer survival relationships.
The tool supports biomedical researchers in advancing precision medicine in oncology.
Facilitates investigation of gene-specific methylation patterns and their impact on patient survival.