Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma

Abstract Background High-grade serous ovarian cancer (HGSOC) is the most lethal ovarian cancer subtype, responsible for ~ 70% of ovarian cancer–related deaths and marked by late-stage diagnosis and frequent platinum resistance. Although transcriptomic profiling enables molecular stratification and p...

Descripció completa

Dades bibliogràfiques

Autors principals:	Prakash Lingasamy, Marta Ostrowska-Leśko, Pantelis Tsakalis, Naisarg Patel, Ilias Chamatidis, Sajitha Lulu Sudhakaran, Joanna Kubik, Marcin Bobiński, Nikos D. Lagaros, Andres Salumets, Vijayachitra Modhukur
Format:	Artigo
Idioma:	Inglês
Publicat:	BMC 2026-06-01
Col·lecció:	Journal of Ovarian Research
Matèries:	High-grade serous ovarian cancer Computational pathology Self-supervised learning Gene expression prediction Platinum response Digital pathology
Accés en línia:	https://doi.org/10.1186/s13048-026-02166-y
Etiquetes:	Afegir etiqueta Sense etiquetes, Sigues el primer a etiquetar aquest registre!