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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
bc-GenExMiner 3.0: new mining module computes breast cancer gene expression correlation analyses
Pascal Jézéquel1, Jean-Sébastien Frénel, Loïc Campion
1Unité Mixte de Génomique du Cancer, Hôpital Laënnec/Institut de Cancérologie de l'Ouest - site René Gauducheau, Bd J. Monod, 44805 Nantes - Saint Herblain Cedex, France. pascal.jezequel@ico.unicancer.fr
Database : the Journal of Biological Databases and Curation
|January 18, 2013
Summary
Researchers can now explore breast cancer gene expression using the new bc-GenExMiner correlation module. This tool aids in understanding gene correlations and biological pathways, advancing breast cancer research.
Area of Science:
- * Bioinformatics
- * Computational Biology
- * Molecular Biology
Background:
- * The bc-GenExMiner web application facilitates the evaluation of gene prognostic informativity in breast cancer.
- * Existing tools primarily focus on prognostic value, necessitating enhanced capabilities for exploring gene relationships.
Purpose of the Study:
- * To introduce a novel 'correlation module' within bc-GenExMiner for comprehensive gene expression correlation analyses in breast cancer.
- * To enable researchers to investigate pairwise gene correlations, identify highly correlated gene sets, and analyze gene expression patterns in relation to genomic location.
- * To facilitate the exploration of biological pathways and functions associated with correlated genes using Gene Ontology (GO) enrichment analysis.
Main Methods:
- * Development of a 'correlation module' with three distinct analysis types: pairwise gene correlation, identification of top correlated genes (positive/negative), and analysis of gene expression correlation with neighboring genomic regions (telomeric/centromeric).
- * Integration of a Gene Ontology (GO) mining function to analyze biological process, molecular function, and cellular component enrichments for identified gene lists.
- * Capability to perform analyses across different patient cohorts: all patients, specific molecular subtypes (basal-like, HER2+, luminal A, luminal B), and by oestrogen receptor status.
Main Results:
- * The 'correlation module' provides three distinct methods for analyzing gene expression correlations, enhancing the utility of bc-GenExMiner.
- * Gene Ontology enrichment analysis offers insights into the biological significance of correlated gene sets.
- * Validation against published data confirms the consistency and reliability of the automated analysis results.
Conclusions:
- * The new 'correlation module' significantly expands the analytical capabilities of bc-GenExMiner for breast cancer research.
- * This tool empowers basic researchers to investigate complex molecular mechanisms and gene interactions in breast cancer.
- * The enhanced functionality supports deeper exploration of gene expression patterns and their biological relevance.
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