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Updated: May 10, 2026
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
An ERRα-ZEB1 transcriptional signature predicts survival in triple-negative breast cancers
Jing-Ru Shi1,2,3, Coralie Poulard3, Catherine Cerutti2
1The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute for Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China.
Background:
Transcription factors (TFs) act together with co-regulators to modulate the expression of their target genes, which eventually dictates their pathophysiological effects. Depending on the co-regulator, TFs can exert different activities. The Estrogen Related Receptor α (ERRα) acts as a transcription factor that regulates several pathophysiological phenomena. In particular, interactions with PGC-1 co-activators are responsible for the metabolic activities of ERRα. In breast cancers however, ERRα exerts several tumor-promoting, metabolism-unrelated activities that do not depend on PGC-1, questioning the identity of the co-activators involved in these cancer-related effects.
Methods:
Bio-computing methods were used to identify a potential ERRα coactivating factor in aggressive breast cancers. Bench experiments (qPCR, ChIP, proximity ligation assays) were used to validate the effect of the co-factor on ERRα transcriptional activity specifically in triple negative breast cancer (TNBC) cells. Predictive value of ERRα-co-factor target genes on the survival of TNBC patients was studied.
Results:
ZEB1 appears as a major ERRα co-factor, involved in increased expression of direct ERRα targets that induce cell migration in TNBCs. Experimental validations establish that ERRα and ZEB1 interact together and are bound to the promoters of the target genes that they transcriptionally coregulate. Further analyses show that the ERRα-ZEB1 downstream transcriptional signature can predict the survival of TNBC patients.
Conclusions:
Our approach combining bio-computing as well as experimental validation allows to propose a gene signature, the high expression of which predicts TNBC patient survival. Down modulation of these genes could be promising against TNBCs.
