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Immunological and prognostic impact of NRF2 in high grade serous ovarian cancer

Genes & Immunity (2026) Cite this article

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Abstract

NRF2 modulates tumor immune microenvironment in several cancers. NRF2 is activated in about 50% of high-grade serous ovarian cancer (HGSOC), the most aggressive type of ovarian cancer. Through analyzing data from scRNA-seq (n = 7), bulk RNA-seq (n = 365), and tumor microarray (TMA) of human HGSOC (n = 240) samples, we demonstrated that NRF2 expression correlated with tumor immune microenvironment in HGSOC. Functional pathway enrichment analysis and transcription factors (TFs) prediction showed the functional relevance of NRF2 expression in shaping the immune phenotype of HGSOC. Pathways such as hedgehog and ROS signaling, and TFs including EGR1, ESRRA, SMAD proteins, and SP-family proteins, are implicated in the immune suppressive microenvironment of NRF2High tumors. Immune differentiation analysis showed patients with NRF2High tumors enriched with CD68 have lower survival (p = 0.038) than those with CD68Low tumors, whereas NRF2Low tumors enriched with immune-activated markers such as CD3E and CD80 exhibit a better prognosis. This study is the first that shows classification of HGSOC based on NRF2 levels, highlights new biomarkers, and suggests IHC-labeling and genomic evaluation of NRF2 and immune markers for better prognosis.

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Fig. 1: Single-cell (sc)RNA-seq data analysis of 7 human HGSOC tumor samples reveals canonical activation of NRF2 in epithelial cells of HGSOC.
The alternative text for this image may have been generated using AI.
Fig. 2: Classification of single cell (sc)RNA-seq data analysis of 7 human HGSOC tumor samples with immune cell distribution and infiltration in high (n = 4) versus low (n = 3) NRF2 HGSOC.
The alternative text for this image may have been generated using AI.
Fig. 3: Hallmark enrichment analysis and transcription factors (TFs) enrichment analysis of scRNA-seq data of NRF2High (n = 4) and NRF2Low (n = 3) High Grade Serous Ovarian Cancer.
The alternative text for this image may have been generated using AI.
Fig. 4: RNA-seq data analysis of n = 365 HGSOC tumor samples from TCGA with immune phenotype and pathway analysis in high (n = 194) versus low (n = 171) NRF2 HGSOC.
The alternative text for this image may have been generated using AI.
Fig. 5: RNA-seq data analysis of n = 365 HGSOC tumor samples from TCGA for transcription factors (TFs) enrichment analysis in high (n = 194) versus low (n = 171) NRF2 HGSOC and survival of patients with different levels of immune markers in high and low NRF2 HGSOC.
The alternative text for this image may have been generated using AI.
Fig. 6: Two representative HGSOC tumor samples from n = 240 TMA human tumors used in this study.
The alternative text for this image may have been generated using AI.

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Data availability

Data supporting the results presented in this study are available in the Supplementary Figures and Tables. The normalized ovarian cancer transcriptomic data used in this study can be obtained from UCSC Xena (https://xena.ucsc.edu/cite-us) [32]. Sample-specific information was obtained from Verhaak et al. [8] and the TCGA Biolinks database [99]. Organizational scripts are available from the corresponding author (SHH) upon reasonable request.

Code availability

We extensively used publicly available algorithms/methods. Additional code used in this study is available upon request.

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Acknowledgements

The authors thank the staff of the MAPCore at the University of British Columbia, the BASIC lab of the BC Cancer Research Institute, and the Anatomical Pathology department of BC Cancer Agency for performing immunohistochemistry staining. This study was supported in part by personal funds (SHH), the Canadian Institutes of Health Research (PJT-178179; to YW), Ovarian Cancer Research Alliance (ECIG-2025-3-2014, to YW), and the Michael Smith Health Research BC postdoctoral fellowship to LF. The authors also appreciate the generous support from the VGH/UBC Hospital Foundation to the Ovarian Cancer Research Centre.

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  1. These authors contributed equally: Chelsea Katz, Helen Toma.

Authors and Affiliations

  1. Department of Surgery, Cooper Medical School of Rowan University, Camden, NJ, USA

    Samera H. Hamad

  2. MD Anderson Cancer Center at Cooper, Camden, NJ, USA

    Samera H. Hamad, Gord Zhu, Hadi Shojaei, Lauren Krill, Christina Chu & David P. Warshal

  3. Cooper University Health Care, Camden, NJ, USA

    Samera H. Hamad, Chelsea Katz, Helen Toma, Gord Zhu, Hadi Shojaei, Lauren Krill, Christina Chu & David P. Warshal

  4. Department of Obstetrics and Gynecology, Kindai University Faculty of Medicine, Osaka, Japan

    Kosuke Murakami

  5. The Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University, Baltimore, MD, USA

    Kosuke Murakami

  6. College of Science and Mathematics, Rowan University, Glassboro, NJ, USA

    Nasrine Bendjilali

  7. Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada

    Lanlan Fang, Samuel Leung & Yemin Wang

  8. Ovarian Cancer Research Centre, Vancouver Coastal Health Research Institute, Vancouver, BC, Canada

    Lanlan Fang, Samuel Leung & Yemin Wang

  9. Department of Pathology, University of Calgary, Calgary, AB, Canada

    Martin Koebel

  10. North Carolina State University, Raleigh, NC, USA

    Huseyin Karaduman

  11. Division of Biostatistics and Bioinformatics, Department of Pharmacology, Physiology, and Cancer Biology, Thomas Jefferson University, Philadelphia, PA, USA

    Oliver Abinader & Ramkrishna Mitra

Authors
  1. Samera H. Hamad
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  2. Chelsea Katz
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  3. Helen Toma
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  4. Kosuke Murakami
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  5. Nasrine Bendjilali
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  6. Gord Zhu
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  7. Hadi Shojaei
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  8. Lanlan Fang
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  9. Samuel Leung
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  10. Martin Koebel
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  11. Huseyin Karaduman
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  12. Oliver Abinader
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  13. Ramkrishna Mitra
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  14. Lauren Krill
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  15. Christina Chu
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  16. David P. Warshal
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  17. Yemin Wang
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Contributions

SHH: conception, study supervision, funding support, experimental design, methodology, data analysis, interpretation of data, and writing, review, and revision of the manuscript; CK and HT: data analysis, and help in writing, review and revision of the manuscript; KM, NB, GZ, HS, LF, SL, MK, HK, OA, RM: data analysis and interpretation, and help in review and revision of the manuscript; LK, CC, DW: interpretation of data and help in review and revision of the manuscript; YW: funding support, methodology, data analysis, interpretation of data and help in writing, review and revision of the manuscript.

Corresponding author

Correspondence to Samera H. Hamad.

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Competing interests

The authors declare no competing interests.

Ethics statement

As this study used only publicly available, anonymized data and did not involve human participants or confidential data, formal ethics approval was not required. All methods were performed in accordance with the relevant guidelines and regulations.

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Hamad, S.H., Katz, C., Toma, H. et al. Immunological and prognostic impact of NRF2 in high grade serous ovarian cancer. Genes Immun (2026). https://doi.org/10.1038/s41435-026-00400-7

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