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Updated: May 14, 2026
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Revealing the dynamic modularity of composite biological networks in breast cancer treatment
Konstantina Dimitrakopoulou1, George Dimitrakopoulos, Evangelia I Zacharaki
1Medical School, Patras, 26500 Greece. kondim@upatras.gr
Abstract:
A major challenge in modern breast cancer treatment is to unravel the effect of drug activity through the systematic rewiring of cellular networks over time. Here, we illustrate the efficacy and discriminative power of our integrative approach in detecting modules that represent the regulatory effect of tamoxifen, widely used in anti-estrogen treatment, on transcriptome and proteome and serve as dynamic sub-network signatures. Initially, composite networks, after integrating protein interaction and time series gene expression data between two conditions (estradiol and estradiol plus tamoxifen), were constructed. Further, the Detect Module from Seed Protein (DMSP) algorithm elaborated on the graphs and constructed modules, with specific 'seed' proteins used as starting points. Our findings provide evidence about the way drugs perturb and rewire the high-order organization of interactome in time.
Insights
This study reveals how tamoxifen rewires cellular networks over time in breast cancer. Our integrative approach identifies dynamic sub-network signatures to understand drug effects on transcriptome and proteome.
Area of Science:
- Systems biology
- Cancer research
- Bioinformatics
Background:
- Understanding drug mechanisms in breast cancer requires analyzing cellular network changes over time.
- Tamoxifen is a key anti-estrogen therapy, but its precise effects on cellular networks are complex.
Purpose of the Study:
- To develop and validate an integrative approach for detecting dynamic sub-network signatures.
- To illustrate the regulatory effects of tamoxifen on transcriptome and proteome over time.
Main Methods:
- Constructed composite networks by integrating protein interaction and time-series gene expression data.
- Utilized the Detect Module from Seed Protein (DMSP) algorithm to identify regulatory modules.
- Analyzed data from two conditions: estradiol and estradiol plus tamoxifen.
Main Results:
- Demonstrated the efficacy and discriminative power of the integrative approach.
- Identified modules representing tamoxifen's regulatory effects.
- Provided insights into how drugs perturb and rewire the interactome over time.
Conclusions:
- The integrative approach effectively detects dynamic sub-network signatures.
- Tamoxifen significantly rewires cellular networks, impacting transcriptome and proteome.
- Findings advance the understanding of drug-induced network perturbations in cancer therapy.
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