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

Crossref DOI link: https://doi.org/10.1186/s13048-026-02166-y

Published Online: 2026-06-11

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Lingasamy, Prakash

Ostrowska-Leśko, Marta

Tsakalis, Pantelis

Patel, Naisarg

Chamatidis, Ilias

Sudhakaran, Sajitha Lulu

Kubik, Joanna

Bobiński, Marcin

Lagaros, Nikos D.

Salumets, Andres

Modhukur, Vijayachitra
Funding

Funding for this research was provided by:

Karolinska Institute
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Text and Data Mining valid from 2026-06-11

Version of Record valid from 2026-06-12
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Article History

Received: 10 February 2026

Accepted: 8 June 2026

First Online: 11 June 2026

Declarations

:

: The RT-qPCR validation study was conducted in accordance with protocols approved by the Bioethical Committee of the Medical University of Lublin under the following reference numbers: KE-0254/20/2021, KE-0254/185/10/2022, and KE-0254/23/01/2024. All participants provided written informed consent prior to inclusion. The study procedures were performed in compliance with relevant national and international guidelines and regulations, including the principles outlined in the Declaration of Helsinki.

: Not applicable. (No individually identifiable data or images from participants are presented in this manuscript.)

: The authors declare no competing interests.

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