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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

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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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Related Experiment Video

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Methyl-binding DNA capture Sequencing for Patient Tissues
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Front Cover: PDMSA: A Web-Based Tool for Pan-Cancer Survival Analysis Using DNA Methylation Levels as Biomarkers

Weiwei Guo, Ying Shi, Shanshan Wu

    Advanced Genetics (Hoboken, N.J.)
    |April 8, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces PDMSA, a web tool for Pan-Cancer Survival Analysis. It integrates large-scale genomic data to stratify cancer prognosis and guide precision oncology.

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    Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer
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    Area of Science:

    • Genomics and Bioinformatics
    • Cancer Research
    • Precision Oncology

    Background:

    • Cancer prognosis stratification is crucial for precision oncology.
    • Large-scale genomic data, including methylation patterns, are key to understanding cancer regulation.
    • Existing tools may not fully integrate multi-database information for comprehensive survival analysis.

    Purpose of the Study:

    • To develop and present a novel Pan-Cancer Survival Analysis web tool (PDMSA).
    • To enable data-driven precision oncology through integrated genomic data analysis.
    • To provide prognostic stratification across multiple cancer types.

    Main Methods:

    • Integration of multi-database genomic records (TCGA, GEO) comprising over 16 million data points.
    • Development of a web tool (PDMSA) to analyze methylation-driven cancer regulation.
    • Utilizing Kaplan-Meier survival curve architecture for prognostic stratification.

    Main Results:

    • PDMSA successfully integrates extensive genomic data across 39 cancer types.
    • The tool facilitates the visualization of favorable versus poor survival outcomes.
    • Methylation data is leveraged to understand cancer regulation and its impact on survival.

    Conclusions:

    • PDMSA serves as a valuable resource for data-driven precision oncology.
    • The tool enhances prognostic stratification by integrating large-scale genomic and clinical data.
    • PDMSA supports research in methylation-driven cancer regulation and survival outcomes.