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Related Concept Videos

Cancer Survival Analysis01:21

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...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Related Experiment Video

Updated: May 16, 2026

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
06:52

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

A network module-based method for identifying cancer prognostic signatures.

Guanming Wu, Lincoln Stein

    Genome Biology
    |December 12, 2012
    PubMed
    Summary
    This summary is machine-generated.

    Researchers developed a new method to find prognostic gene signatures for cancer survival using gene networks. This approach identified a novel 31-gene signature in breast cancer that outperforms existing biomarkers.

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    Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
    07:41

    Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

    Published on: May 17, 2019

    Area of Science:

    • Genomics
    • Bioinformatics
    • Cancer Research

    Background:

    • Discovering reliable prognostic gene signatures from genomics data is difficult.
    • Gene functional interaction networks offer a robust framework for biomarker discovery.
    • Existing methods may lack efficiency and reproducibility in identifying cancer biomarkers.

    Purpose of the Study:

    • To develop and validate an efficient method for identifying prognostic gene signatures using gene functional interaction networks.
    • To discover novel gene signatures associated with patient survival in breast and ovarian cancers.
    • To provide a user-friendly implementation of the signature discovery method.

    Main Methods:

    • Utilized a highly reliable gene functional interaction network to derive gene modules.
    • Applied a novel algorithm to cancer gene expression datasets (breast and ovarian cancer).
    • Validated the discovered gene signatures across multiple independent gene expression studies.

    Main Results:

    • Identified a novel 31-gene prognostic signature for breast cancer survival, which replicated across 5 independent studies.
    • The breast cancer signature demonstrated superior performance compared to 48 previously published gene signatures.
    • Discovered a 75-gene prognostic signature associated with patient survival in ovarian cancer.

    Conclusions:

    • The developed method provides an efficient and robust approach for prognostic biomarker discovery in cancer genomics.
    • The identified gene signatures hold potential as reliable biomarkers for patient survival in breast and ovarian cancers.
    • A Cytoscape plugin is available, facilitating the application of this method in the broader research community.