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Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome
Published on: June 15, 2016
Identification of Post-Transcriptional Modulators of Breast Cancer Transcription Factor Activity Using MINDy
Thomas M Campbell1, Mauro A A Castro2, Bruce A J Ponder1
1Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, United Kingdom.
Abstract:
We have recently identified transcription factors (TFs) that are key drivers of breast cancer risk. To better understand the pathways or sub-networks in which these TFs mediate their function we sought to identify upstream modulators of their activity. We applied the MINDy (Modulator Inference by Network Dynamics) algorithm to four TFs (ESR1, FOXA1, GATA3 and SPDEF) that are key drivers of estrogen receptor-positive (ER+) breast cancer risk, as well as cancer progression. Our computational analysis identified over 500 potential modulators. We assayed 189 of these and identified 55 genes with functional characteristics that were consistent with a role as TF modulators. In the future, the identified modulators may be tested as potential therapeutic targets, able to alter the activity of TFs that are critical in the development of breast cancer.
Insights
Researchers identified key gene modulators for breast cancer risk. These modulators target transcription factors (TFs) involved in estrogen receptor-positive (ER+) breast cancer, offering potential new therapeutic avenues.
Area of Science:
- Oncology
- Genetics
- Bioinformatics
Background:
- Transcription factors (TFs) are critical drivers of breast cancer risk and progression.
- Understanding the regulatory networks of these TFs is essential for developing targeted therapies.
Purpose of the Study:
- To identify upstream modulators of key transcription factors (TFs) involved in estrogen receptor-positive (ER+) breast cancer.
- To uncover novel genes that regulate TF activity in breast cancer development.
Main Methods:
- Application of the Modulator Inference by Network Dynamics (MINDy) algorithm for computational analysis.
- Assay of 189 potential modulators identified through computational predictions.
- Functional characterization of candidate genes to confirm their role as TF modulators.
Main Results:
- Over 500 potential TF modulators were computationally identified.
- Experimental validation confirmed 55 genes possessing functional characteristics of TF modulators.
- Identified modulators are associated with key TFs (ESR1, FOXA1, GATA3, SPDEF) in ER+ breast cancer.
Conclusions:
- The study successfully identified novel gene modulators of critical transcription factors in breast cancer.
- These identified modulators represent potential therapeutic targets for altering TF activity in ER+ breast cancer.
- Further research into these modulators may lead to new strategies for breast cancer treatment.
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