Back to search

Article

Biologically-informed machine learning identifies a new clinically-actionable bladder cancer subgroup characterised by NRF2 overactivity

2025-06-05

Abstract excerpt

Muscle-invasive bladder cancer is a diverse disease where subtyping is ambiguous. Gene expression profiling followed by unsupervised machine learning (ML) has broadened our understanding of tumour biology, but has failed to provide high-confidence clinically-actionable subgroups. To focus on tissue-specific urothelial biology, we generated co-expression networks from histologically normal bladder, including multip...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
1b429d5f-8ad8-5799-87e7-3efce0a8ff1e
DOI
10.1101/2025.06.03.657659
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Biologically-informed machine learning identifies a new clinically-actionable bladder cancer subgroup characterised by NRF2 overactivityDOI 10.1101/2025.06.03.657659
Multi-omics consensus ensemble refines the classification of muscle-invasive bladder cancer with stratified prognosis, tumour microenvironment and distinct sensitivity to frontline therapiesSemantic proximity
Gene Expression Monotonicity across Bladder Cancer Stages Informs on the Molecular Pathogenesis and Identifies a Prognostic Eight-Gene SignatureSemantic proximity
EGFR as a potential therapeutic target for a subset of muscle-invasive bladder cancers presenting a basal-like phenotype.Semantic proximity
Machine Learning and Experimental Validation Reveal MYH11 as a Novel Prognostic Biomarker and Therapeutic Target in Bladder CancerSemantic proximity
Personalized <i>in-silico</i> drug response prediction based on the genetic landscape of muscle-invasive bladder cancerSemantic proximity
Multiomics reveals the role of ENO1 in bladder cancer and constructs an epithelial-related prognostic model to predict prognosis and efficacySemantic proximity
Epigenomic mapping identifies an enhancer repertoire that regulates cell identity in bladder cancer through distinct transcription factor networksSemantic proximity
Identification and validation of a signature based on myofibroblastic cancer-associated fibroblast marker genes for predicting prognosis, immune infiltration, and therapeutic response in bladder cancerSemantic proximity
Transcriptome Analysis in Patients With Muscle-invasive Bladder CancerSemantic proximity
Epigenomic mapping identifies a super-enhancer repertoire that regulates cell identity in bladder cancers through distinct transcription factor networksSemantic proximity
Characterization of FGFR Alterations and Activation in Patients with High-Risk Non-Muscle-Invasive Bladder Cancer.Semantic proximity
Machine Learning-based Classification of transcriptome Signatures of non-ulcerative Bladder Pain SyndromeSemantic proximity
Select a neighboring publication to make it the new centre.