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Related Experiment Video

Updated: May 28, 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

Integrative Survival Prediction in Breast Cancer Using Extracellular Matrix Protease Transcript Signatures and

Rami Babas1, Demitrios H Vynios1, Aristotelis Kompothrekas2

  • 1Biochemistry, Biochemical Analysis & Matrix Pathobiochemistry Research Group, Department of Chemistry, University of Patras, 26504 Patras, Greece.

Cancers
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Integrating extracellular matrix (ECM) protease gene expression with clinical data improves breast cancer survival prediction. While promising, these ECM protease markers require further validation for clinical use.

Keywords:
ADAMADAMTSMMPsbreast cancerintegrative survival modelingmachine learningmolecular subtypesprecision oncologyprognostic biomarkers

Related Experiment Videos

Last Updated: May 28, 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

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Traditional breast cancer staging lacks molecular insights, impacting patient outcomes.
  • Extracellular matrix (ECM) remodeling by proteases (MMP, ADAM, ADAMTS) is crucial for tumor progression.
  • Novel prognostic markers are needed for personalized breast cancer risk stratification.

Purpose of the Study:

  • To assess if integrating ECM protease transcript levels with clinical data enhances breast cancer survival prediction.
  • To evaluate the accuracy of multivariable models for personalized risk assessment.
  • To determine the prognostic value of ECM protease genes in breast cancer.

Main Methods:

  • Analysis of The Cancer Genome Atlas (TCGA) breast cancer cohort data (clinical and transcriptomic).
  • Integration of ECM protease transcript abundance (pTPM) with clinical variables (age, stage).
  • Comparison of survival prediction models including CoxPH, CoxNet, RSF, and GBS, with external validation in METABRIC.

Main Results:

  • Specific ECM proteases (ADAM15, MMP15, ADAMTSL1) were identified as risk factors, while others (ADAMTS8, MMP7) were protective.
  • Integrated models significantly outperformed transcript-only and clinical-only models in internal validation (RSF C-index=0.797 vs. Cox C-index=0.742).
  • External validation in METABRIC showed a C-index of 0.581, with significant survival separation across risk groups.

Conclusions:

  • ECM protease transcript profiles offer complementary prognostic information in breast cancer.
  • These markers demonstrate partial transportability across datasets but limited individual discrimination.
  • Further validation is required before clinical implementation of ECM protease-based prognostic markers.