Proteomic alterations in ovarian cancer-Predicting residual disease status using artificial intelligence and SHAP-based biomarker interpretation

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dc.contributor.authorYasar, Seyma
dc.contributor.authorMelekoglu, Rauf
dc.date.accessioned2026-04-04T13:31:18Z
dc.date.available2026-04-04T13:31:18Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractIntroduction High-grade serous ovarian cancer (HGSOC) is the most aggressive and prevalent subtype of ovarian Treatment outcomes are significantly influenced by residual disease status following neoadjuvant chemotherapy (NACT). Predicting residual disease before surgery can improve patient stratification and personalized treatment strategies.Methods This study analyzed pre-NACT proteomic data from 20 HGSOC patients treated with NACT. Patients were categorized into two groups based on surgical outcomes: no residual disease (R0, n = 14) and suboptimal residual disease (R1, n = 6). From an initial set of 97 differentially expressed proteins, 18 significant proteins were selected using the BORUTA feature selection method. Three machine learning models-Random Forest (RF), Support Vector Machine (SVM), and Bootstrap Aggregation with Classification and Regression Trees (BaggedCART)-were developed and evaluated.Results The Random Forest model achieved the best performance with an AUC of 0.955, accuracy of 0.830, sensitivity of 0.904, specificity of 0.763, and F1-score of 0.839. SHapley Additive exPlanations (SHAP) analysis identified five proteins (P48637, O43491, O95302, Q96CX2, and P49189) as the most influential predictors of residual disease. These proteins, including glutathione synthetase and peptidyl-prolyl cis-trans isomerase FKBP9, are associated with chemotherapy resistance mechanisms.Discussion The findings demonstrate the potential of integrating proteomic data with machine learning techniques for predicting surgical outcomes in HGSOC. Identified protein signatures may serve as valuable biomarkers for anticipating NACT response and informing clinical decision-making, ultimately contributing to personalized patient care.
dc.description.sponsorshipThe author(s) declare that no financial support was received for the research and/or publication of this article.
dc.identifier.doi10.3389/fmed.2025.1562558
dc.identifier.issn2296-858X
dc.identifier.orcid0000-0001-7113-6691
dc.identifier.pmid40771481
dc.identifier.scopus2-s2.0-105012625727
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3389/fmed.2025.1562558
dc.identifier.urihttps://hdl.handle.net/11616/108705
dc.identifier.volume12
dc.identifier.wosWOS:001544462200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherFrontiers Media Sa
dc.relation.ispartofFrontiers in Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjecthigh-grade serous ovarian cancer (HGSOC)
dc.subjectneoadjuvant chemotherapy (NACT)
dc.subjectmachine learning
dc.subjectproteomic biomarkers
dc.subjectSHAP analysis
dc.titleProteomic alterations in ovarian cancer-Predicting residual disease status using artificial intelligence and SHAP-based biomarker interpretation
dc.typeArticle

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