eTRex Reveals Oncogenic Transcriptional Regulatory Programs Across Human Cancers
Lu, Z.; Yang, Y.; Zheng, Q.; Gao, F.; Xu, L.; Wang, X.
Show abstract
Transcriptional regulators (TRs) are essential proteins that regulate gene transcription, and their dysregulation defines the oncogenic transcriptional regulatory programs that drive tumor-specific gene expression. Existing pan-cancer resources summarize these programs by aggregating signals across individual datasets, neglecting the context-specific features that capture the diversity of transcriptional regulation underlying cancer heterogeneity. By developing a variational Bayesian hierarchical model named eTRex (epigenomics-based Transcriptional Regulator explorer) and applying it to 4,819 cancer-related ATAC-seq datasets, we provide a comprehensive pan-cancer atlas of functional TR profiles that preserve the context-specific features of each dataset. This study reveals both common regulators across diverse malignancies and those with highly specific roles. We extensively validated these findings using independent CRISPR/Cas9 screening, mutation, and transcriptomic datasets. Collectively, this pan-cancer atlas of functional TR profiles represents a comprehensive, biologically interpretable resource for uncovering transcriptional regulatory programs, identifying biomarkers, prioritizing therapeutic targets in oncology, and is freely accessible through an interactive web portal.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning. 96%
- SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks 96%
- NEST: Spatially-mapped cell-cell communication patterns using a deep learning-based attention mechanism 96%
Similar papers in this journal
- An interpretable bimodal neural network characterizes the sequence and preexisting chromatin predictors of induced TF binding 97%
- Enhancer regulatory networks globally connect non-coding breast cancer loci to cancer genes 97%
- CREaTor: zero-shot cis-regulatory pattern modeling with attention mechanisms 97%
Similar papers in this journal
- Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers 96%
- Enhancing Disease Risk Gene Discovery by Integrating Transcription Factor-Linked Trans-located Variants into Transcriptome-Wide Association Analyses 96%
- Recruitment of Homodimeric Proneural Factors by Conserved CAT-CAT E-Boxes Drives Major Epigenetic Reconfiguration in Cortical Neurogenesis 96%
Similar papers in this journal
- Normal and cancer tissues are accurately characterised by intergenic transcription at RNA polymerase 2 binding sites 96%
- Interpretable deep learning reveals the sequence rules of Hippo signaling 96%
- Gene regulatory network inference from CRISPR perturbations in primary CD4+ T cells elucidates the genomic basis of immune disease 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.