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Updated: Jun 17, 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

Gene expression-based diagnosis of efficacy of chemotherapy for breast cancer

Yoshio Miki1

  • 1Department of Molecular Genetics, Medical Research Institute, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan. miki@jfcr.or.jp

Breast Cancer (Tokyo, Japan)
|December 17, 2009
PubMed

Insights

Accurate prediction of drug sensitivity is key for effective cancer treatment and personalized medicine. Molecular information from gene expression and polymorphisms guides tailored therapies for maximum patient benefit.

Area of Science:

  • Genomics
  • Molecular Biology
  • Oncology

Background:

  • Advancements in genome science, particularly microarrays, enable molecular-level understanding of diseases like cancer.
  • Molecular information, including gene expression profiles and patient-specific gene polymorphisms, influences cancer development, progression, and treatment outcomes.
  • Understanding these molecular factors is crucial for optimizing drug efficacy and minimizing adverse effects.

Purpose of the Study:

  • To develop a clear index for selecting drugs based on accurate prediction of drug sensitivity.
  • To advance personalized medicine by tailoring treatments to individual patient and cancer characteristics.
  • To maximize therapeutic effects and improve patient outcomes through molecularly guided drug selection.

Main Methods:

  • Utilizing gene expression profiles from cancer cells.
  • Analyzing individual patient gene polymorphisms.
  • Correlating molecular data with drug sensitivity and therapeutic outcomes.

Main Results:

  • Identification of key molecular markers predictive of drug sensitivity.
  • Demonstration of the impact of gene expression and polymorphisms on treatment response.
  • Establishment of a framework for predicting drug efficacy based on molecular profiles.

Conclusions:

  • Accurate prediction of drug sensitivity is essential for maximizing therapeutic effects.
  • Personalized medicine approaches, informed by molecular data, are advancing cancer treatment.
  • Clinical application of molecular information enables tailored therapies for improved patient care.

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