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ORIGINAL RESEARCH ARTICLE

Determination of specific salivary biochemical profiles in oral and ovarian cancer using machine learning and SHAP analysis

Elena I. Dyachenko1 ,  Lyudmila V. Bel’skaya1*
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1 Biochemistry Research Laboratory, Omsk State Pedagogical University, Omsk, Omsk Oblast , Russia
EJMO, 026310388 https://doi.org/10.36922/EJMO026310388
Received: 27 July 2026 | Revised: 5 September 2026 | Accepted: 15 September 2026 | Published online: 8 October 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Introduction: Saliva contains local and systemic molecular signals that may support non-invasive cancer detection, but the diagnostic value of integrated salivary profiles across anatomically distinct malignancies remains unclear.

Objective: This study evaluates whether multivariable salivary signatures could distinguish controls, ovarian cancer, and oral cavity cancer.

Methods: Saliva samples from 75 controls, 89 patients with ovarian cancer, and 49 patients with oral cavity cancer were analyzed for amino acids and nitrogen metabolites, inorganic ions, cytokines, growth factors, tumor-associated markers, lipid peroxidation products, and Fourier-transform infrared (FTIR) features. Results from regularized multinomial logistic regression, random forest, and XGBoost models were compared using repeated stratified five-fold cross-validation within the training set. Final model performance was then evaluated on a held-out test set using accuracy, Cohen’s κ, balanced accuracy, macro-F1, multiclass receiver operating characteristic–area under the curve, and log loss. Permutation testing, bootstrap uncertainty estimation, SHapley Additive exPlanations (SHAP) analysis, and decision curve analysis were additionally performed.

Results: XGBoost showed the strongest performance and correctly classified all ovarian and oral cavity cancer cases in the held-out test set. Permutation testing confirmed that performance exceeded that of randomly assigned class labels. SHAP analysis indicated that mainly cytokine and tumor-associated markers drove ovarian cancer predictions, whereas lipid-related FTIR features dominated oral cavity cancer predictions. Decision curve analysis demonstrated positive net benefit across threshold ranges.

Conclusion: Integrated salivary profiling enabled accurate internal differentiation of local and distant malignancies. The findings support biologically distinct salivary signatures, but external validation is required.

Keywords
Machine learning
Saliva
Cancer
Biochemical profile
Cytokines
Amino acids
Funding
This research was funded by the Russian Science Foundation (grant number 23-15-00188-П).
Conflict of interest
The authors declare they have no competing interests.
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Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing
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