Proteomic alterations in ovarian cancer—Predicting residual disease status using artificial intelligence and SHAP-based biomarker interpretation
IntroductionHigh-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 stratifica...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1562558/full |
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| author | Seyma Yasar Rauf Melekoglu |
| author_facet | Seyma Yasar Rauf Melekoglu |
| author_sort | Seyma Yasar |
| collection | DOAJ |
| description | IntroductionHigh-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.MethodsThis 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.ResultsThe 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.DiscussionThe 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. |
| format | Article |
| id | doaj-art-5b146047fbdb4aeaa3f803333fce5e9c |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Medicine |
| spelling | doaj-art-5b146047fbdb4aeaa3f803333fce5e9c2025-08-20T03:55:59ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-07-011210.3389/fmed.2025.15625581562558Proteomic alterations in ovarian cancer—Predicting residual disease status using artificial intelligence and SHAP-based biomarker interpretationSeyma Yasar0Rauf Melekoglu1Department of Biostatistics, and Medical Informatics, Medicine Faculty, Inonu University, Malatya, TürkiyeDepartment of Obstetrics and Gynecology, Faculty of Medicine, Inonu University, Malatya, TürkiyeIntroductionHigh-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.MethodsThis 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.ResultsThe 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.DiscussionThe 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.https://www.frontiersin.org/articles/10.3389/fmed.2025.1562558/fullhigh-grade serous ovarian cancer (HGSOC)neoadjuvant chemotherapy (NACT)machine learningproteomic biomarkersSHAP analysis |
| spellingShingle | Seyma Yasar Rauf Melekoglu Proteomic alterations in ovarian cancer—Predicting residual disease status using artificial intelligence and SHAP-based biomarker interpretation Frontiers in Medicine high-grade serous ovarian cancer (HGSOC) neoadjuvant chemotherapy (NACT) machine learning proteomic biomarkers SHAP analysis |
| title | Proteomic alterations in ovarian cancer—Predicting residual disease status using artificial intelligence and SHAP-based biomarker interpretation |
| title_full | Proteomic alterations in ovarian cancer—Predicting residual disease status using artificial intelligence and SHAP-based biomarker interpretation |
| title_fullStr | Proteomic alterations in ovarian cancer—Predicting residual disease status using artificial intelligence and SHAP-based biomarker interpretation |
| title_full_unstemmed | Proteomic alterations in ovarian cancer—Predicting residual disease status using artificial intelligence and SHAP-based biomarker interpretation |
| title_short | Proteomic alterations in ovarian cancer—Predicting residual disease status using artificial intelligence and SHAP-based biomarker interpretation |
| title_sort | proteomic alterations in ovarian cancer predicting residual disease status using artificial intelligence and shap based biomarker interpretation |
| topic | high-grade serous ovarian cancer (HGSOC) neoadjuvant chemotherapy (NACT) machine learning proteomic biomarkers SHAP analysis |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1562558/full |
| work_keys_str_mv | AT seymayasar proteomicalterationsinovariancancerpredictingresidualdiseasestatususingartificialintelligenceandshapbasedbiomarkerinterpretation AT raufmelekoglu proteomicalterationsinovariancancerpredictingresidualdiseasestatususingartificialintelligenceandshapbasedbiomarkerinterpretation |