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Metabolic gene expression-based stratification and prognostic risk predictive model of head and neck squamous cell carcinoma
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  • Published: 25 March 2026

Metabolic gene expression-based stratification and prognostic risk predictive model of head and neck squamous cell carcinoma

  • Soumya Sau1,2,
  • Anjali Gupta1,2,
  • Swastik Sinha1,
  • S. M. Azeem Mohiyuddin3 &
  • …
  • Arvind M. Korwar  ORCID: orcid.org/0000-0001-5806-35391,2 

npj Systems Biology and Applications , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Biomarkers
  • Cancer
  • Computational biology and bioinformatics
  • Oncology

Abstract

Head and neck squamous cell carcinoma (HNSCC) is a complex multivariable disease posing a significant challenge in therapeutics. OXPHOS and PPP are centrally implicated in metabolic heterogeneity influencing tumor behavior, treatment response, and patient outcomes. This study aims to stratify HNSCC based on OXPHOS and PPP gene expression to construct a clinical outcome risk predictive model. HNSCC patients were stratified into four metabolic subtypes including mixed, OXPHOS-leaning, PPP-leaning, and quiescent. The quiescent metabolic subtype showed longest survival with lower proliferation scores, whereas mixed subtype showed the worst survival with higher proliferation scores. Metabolic proteins of the CPTAC and ICPC cohorts affirmed the existence of metabolic heterogeneity among tumor samples. A prognostic risk predictive model was developed based on thirteen genes, which performed better than the OXPHOS-PPP-glycolysis model and other predictive forty-seven metabolic gene’s model. Existence of metabolic heterogeneity was successfully determined in HNSCC. OXPHOS and PPP genes enriched in mixed metabolic subtype having the worst clinical outcome are suggestive of higher metastatic potential, which might offer advisement in personalized therapeutics.

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Data availability

The proteomics dataset generated in this study has been deposited at the ProteomeXchange Consortium via the PRIDE83 partner repository with the dataset identifier PXD073947. The data analysis workflow and code is available at https://github.com/AK2-lab/Metabolic-Heterogeneity-Code/tree/main.

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Acknowledgements

We thank TCGA project and PDC portal for the availability of the transcriptome and proteome data of HNSCC cohort. AG thanks CSIR India for fellowship. The authors are grateful to Prof. Saumitra Das (Department of Microbiology and Cell Biology, Indian Institute of Science, Bengaluru) for connecting us with clinicians. This research work was supported by BRIC-National Institute of Biomedical Genomic intramural funding (NIBMG-60116).

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Authors and Affiliations

  1. Biotechnology Research and Innovation Council, National Institute of Biomedical Genomics (BRIC-NIBMG), Kalyani, India

    Soumya Sau, Anjali Gupta, Swastik Sinha & Arvind M. Korwar

  2. Regional Centre for Biotechnology, NCR Biotech Science Cluster, Faridabad, India

    Soumya Sau, Anjali Gupta & Arvind M. Korwar

  3. Department of Otorhinolaryngology, Sri Devaraj Urs Medical College, Sri Devaraj URS Academy of Higher Education and Research, Kolar, India

    S. M. Azeem Mohiyuddin

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  1. Soumya Sau
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  2. Anjali Gupta
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Contributions

Writing—Original draft preparation, S. Sau, A.G., S. Sinha, A.M.K.; Writing–Review and Editing, S.M.A.M., A.M.K.; Software and Methodology, S. Sau, A.M.K.; Investigation and conceptualization, S. Sau, A.M.K.; Supervision, A.M.K. All authors read and approved the final manuscript.

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Correspondence to Arvind M. Korwar.

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Sau, S., Gupta, A., Sinha, S. et al. Metabolic gene expression-based stratification and prognostic risk predictive model of head and neck squamous cell carcinoma. npj Syst Biol Appl (2026). https://doi.org/10.1038/s41540-026-00689-0

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  • Received: 04 November 2025

  • Accepted: 07 March 2026

  • Published: 25 March 2026

  • DOI: https://doi.org/10.1038/s41540-026-00689-0

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