What question did this study set out to answer?

The research aims to predict gene expression from histopathological images to identify potential biomarkers for high-grade serous ovarian carcinoma (HGSOC).

June 13, 2026Open Access

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

Key Points

The research aims to predict gene expression from histopathological images to identify potential biomarkers for high-grade serous ovarian carcinoma (HGSOC).
Developed a self-supervised virtual-transcriptomics framework using Momentum Contrast v2 and Random Forest regression.
Utilized paired hematoxylin and eosin whole-slide images (WSIs) and RNA sequencing data from the TCGA-OV cohort for model development.
Conducted reverse transcription quantitative polymerase chain reaction (RT-qPCR) on candidate genes in an independent cohort of 10 HGSOC tumors.
The prediction model achieved a genome-wide mean Pearson correlation of r = 0.36, with over 300 genes demonstrating a stronger image-expression coupling (r > 0.44).
NR5A1 showed the most variability in expression (CV = 1.486), being significantly higher in platinum-responsive tumors (mean 2⁻ΔCt = 0.263) compared to non-responders (mean 2⁻ΔCt = 0.013; p < 0.05).
Molecular docking analyses indicated stable interactions between NR5A1 and cubebin, a natural plant compound.

Abstract

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 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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