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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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Updated: May 16, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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CancerMA: a web-based tool for automatic meta-analysis of public cancer microarray data.

Julia Feichtinger1, Ramsay J McFarlane, Lee D Larcombe

  • 1North West Cancer Research Fund Institute, Bangor University, Bangor, Gwynedd LL57 2UW, UK. julia.feichtinger@gmail.com

Database : the Journal of Biological Databases and Curation
|December 18, 2012
PubMed
Summary

CancerMA is a new tool that helps researchers find potential cancer markers. It analyzes gene expression data from many cancer types to identify promising targets for new cancer therapies.

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Area of Science:

  • Bioinformatics
  • Cancer Research
  • Genomics

Background:

  • Identifying novel cancer markers is crucial for developing effective cancer therapies.
  • Analyzing large-scale 'omic' data requires accessible and automated computational approaches.
  • Managing vast datasets and narrowing down potential gene targets necessitates advanced data integration and analysis techniques.

Purpose of the Study:

  • To present CancerMA, an integrated bioinformatics pipeline for automated identification of novel cancer markers and targets.
  • To provide researchers with an accessible tool for analyzing gene expression profiles across multiple cancer types.
  • To facilitate the discovery of new therapeutic targets by meta-analyzing publicly available cancer microarray data.

Main Methods:

  • Development of CancerMA, an online, integrated bioinformatics pipeline.
  • Meta-analysis of gene expression profiles from user-defined gene sets.
  • Utilizing a curated database of 80 cancer microarray datasets covering 13 cancer types.
  • Implementation of a user-friendly web interface for analysis initiation and results retrieval.

Main Results:

  • CancerMA enables automated identification of novel candidate cancer markers/targets.
  • The pipeline successfully meta-analyzes gene expression data across diverse cancer types.
  • Demonstrated functionality through two validation datasets, showcasing its utility for cancer research.

Conclusions:

  • CancerMA offers a powerful, automated solution for discovering novel cancer targets.
  • The tool democratizes access to complex data analysis for a wider research community.
  • Facilitates accelerated development of targeted cancer therapies through efficient marker identification.