Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma
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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 prediction of therapeutic response; routine clinical use of this approach is limited by cost and logistical constraints. Computational pathology analysis offers a scalable alternative by inferring transcriptional states directly from routine hematoxylin and eosin (H&E) whole-slide images (WSIs). Methods Paired H&E WSIs and RNA-sequencing data from the TCGA-OV cohort, including 1,371 diagnostic H&E WSIs retrieved for preprocessing and quality control, were used to develop a self-supervised virtual-transcriptomics framework based on Momentum Contrast v2 (MoCo v2) and multi-output Random Forest regression. Model performance was assessed using patient-level five-fold cross-validation. Candidate genes were evaluated by reverse transcription quantitative polymerase chain reaction (RT-qPCR) in an independent cohort of 10 HGSOC tumors, including 4 platinum responders and 6 non-responders. Results The model predicted expression of approximately 6,400 protein-coding genes, achieving a genome-wide mean Pearson correlation of r = 0.36, with more than 300 genes showing stronger image–expression coupling ( r > 0.44). RT-qPCR analysis of 18 candidate genes revealed substantial inter-patient heterogeneity. NR5A1 exhibited the highest expression variability (coefficient of variation [CV] = 1.486) and significantly higher expression in platinum-responsive tumors than in non-responders (mean 2 ⁻ΔCt = 0.263 vs. 0.013; p < 0.05). Exploratory in silico docking and molecular dynamics (MD) analyses suggested structurally stable binding interactions between Steroidogenic Factor-1 ( SF-1 / NR5A1 ) and the natural plant compound cubebin. Conclusion This study demonstrates that histological architecture contains measurable transcriptomic information that can support scalable biomarker prioritization from routine diagnostic histology in HGSOC. NR5A1 represents a hypothesis-generating candidate biomarker and structurally tractable target for future experimental studies. Future validation in larger, multi-center cohorts will be essential to confirm model robustness, biological relevance, and potential clinical utility.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma
- Date Crossref
- 11/06/2026
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Competence Centre on Health Technologies (Estonia) pays non établi dans la noticeEntreprise
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University of Tartu Laboratory of Precision and Nanomedicine pays non établi dans la noticeUniversité ou école supérieure
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Medical University of Lublin Independent Laboratory of Translational Medicine pays non établi dans la noticeUniversité ou école supérieure
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National Technical University of Athens Inferesence(INFS) pays non établi dans la noticeUniversité ou école supérieure
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Vellore Institute of Technology University pays non établi dans la noticeUniversité ou école supérieure
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Karolinska University Hospital Department of Clinical Science pays non établi dans la noticeÉtablissement de santé
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Karolinska Institutet pays non établi dans la noticeUniversité ou école supérieure
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Celvia CC AS pays non établi dans la noticeInstitution
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School of Bio Sciences and Technology Integrative Multiomics Lab pays non établi dans la noticeUniversité ou école supérieure
Competence Centre on Health Technologies (Estonia), Laboratory of Precision and Nanomedicine — University of Tartu et Independent Laboratory of Translational Medicine — Medical University of Lublin, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.