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Updated: Jul 15, 2026

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

Classification of sarcomas using bioinformatics and molecular profiling.

T A Dang1, T K Man

  • 1Ciphgergen Biosystems, Inc.

Current Pharmaceutical Biotechnology
|April 14, 2007
PubMed
Summary

Genomic and proteomic technologies enable molecular classifiers for cancer diagnosis. These tools improve the classification of challenging tumors like sarcomas, aiding in diagnosis and understanding disease mechanisms.

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Area of Science:

  • Genomics and Proteomics
  • Bioinformatics
  • Cancer Biology

Background:

  • Cancer classification traditionally relies on histology, which can be ambiguous for heterogeneous tumors like sarcomas.
  • Advances in high-throughput genomic and proteomic technologies offer new avenues for precise cancer subtyping.

Purpose of the Study:

  • To review current genomic and proteomic profiling methods for cancer classification.
  • To outline the development of molecular classifiers using bioinformatic approaches.
  • To illustrate the application of molecular classification in distinguishing sarcoma subtypes and understanding their biology.

Main Methods:

  • Utilizing high-throughput genomic and proteomic data.
  • Applying multivariate bioinformatic approaches to develop molecular classifiers.

Related Experiment Videos

Last Updated: Jul 15, 2026

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

  • Reviewing existing profiling techniques and bioinformatic strategies.

    • Main Results:

    • Molecular classification effectively distinguishes histologically similar but molecularly distinct sarcoma subtypes.
    • Identification of discriminatory genes provides insights into sarcoma-specific molecular pathways and mechanisms.
    • Improved tumor diagnosis and stratification are achieved through molecular profiling.

    Conclusions:

    • Molecular classification represents a significant advancement in cancer diagnosis, particularly for heterogeneous tumors.
    • This approach enhances our understanding of sarcoma biology and facilitates personalized treatment strategies.
    • The integration of genomic, proteomic, and bioinformatic techniques is crucial for future cancer research.