Redox BiologyCancer BiologyPeer-reviewed journal article
Identification of a BACH1 lung cancer signature: A novel tool for understanding BACH1 biology and identifying new inhibitors.
Donika Klenja-Skudrinja, Kevin X. Ali, David Walker, Maureen HigginsiD, Angana AH. Patel, Dorota RajiD, Anna Creelman, Charlotte McDowall, Conor Taylor, Tomasz WentaiD, Erik LarssoniD, Clotilde WieliD, Volkan I. SayiniD, Laureano de la VegaiD
Abstract
The transcription factor BACH1 is a transcriptional repressor with a central role in regulating oxidative stress and anti-inflammatory pathways, emerging as a promising therapeutic target for multiple conditions, including neoplastic malignancies, neurodegenerative disorders, ischemia-reperfusion injuries and sickle cell disease. In the field of cancer BACH1 has gained significant attention, with BACH1 overexpression correlating with poor prognosis and metastasis across various cancer types; however, despite this increasing relevance of BACH1, no universal pro-metastatic mechanism or transcriptional signature for BACH1 has been identified which is a major limitation for this growing field. To address this, we performed RNA-Seq coupled with ChIP-Seq in BACH1-proficient and BACH1-deficient lung cancer cells, identifying a set of common BACH1 directly regulated genes, which we thoroughly validated in a large panel of cancer cells. This novel lung cancer BACH1 transcriptional signature is highly sensitive and specific to BACH1 perturbations (both genetic and pharmacological) and does not respond to NRF2 modulation, underscoring its specificity. This signature not only represents a robust surrogate for BACH1 activity, but we also provide evidence of its potential value as a tool to i) identify novel BACH1 inhibitors, and ii) provide insights into BACH1s pro-metastatic role.
- organism: human
- assay: bulk RNA-seq
- assay: ChIP-seq
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- research focus: cell type annotation
- approach: experimental assay
- approach: statistical model
- Citations
- 8
- FWCI
- 2.39
- Field percentile
- top 11%
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