{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:07:34Z","timestamp":1791936454087,"version":"4.3.4"},"reference-count":55,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T00:00:00Z","timestamp":1753228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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, <jats:italic>n<\/jats:italic> = 14) and suboptimal residual disease (R1, <jats:italic>n<\/jats:italic> = 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2025.1562558","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T05:36:45Z","timestamp":1753249005000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Proteomic alterations in ovarian cancer\u2014Predicting residual disease status using artificial intelligence and SHAP-based biomarker interpretation"],"prefix":"10.3389","volume":"12","author":[{"given":"Seyma","family":"Yasar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rauf","family":"Melekoglu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,7,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3322\/caac.21820","article-title":"Cancer statistics, 2024","volume":"74","author":"Siegel","year":"2024","journal-title":"CA Cancer J Clin."},{"key":"B2","first-page":"1993","article-title":"BRCA1- and BRCA2-associated hereditary breast and ovarian cancer","volume-title":"GeneReviews","author":"Petrucelli","year":"1998"},{"key":"B3","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1186\/s12905-022-02016-1","article-title":"Pathways to ovarian cancer diagnosis: a qualitative study","volume":"22","author":"Lawson-Michod","year":"2022","journal-title":"BMC Women's Health."},{"key":"B4","doi-asserted-by":"publisher","first-page":"433","DOI":"10.3390\/cancers10110433","article-title":"Cell origins of high-grade serous ovarian cancer","volume":"10","author":"Kim","year":"2018","journal-title":"Cancers."},{"key":"B5","doi-asserted-by":"publisher","first-page":"952","DOI":"10.3390\/ijms20040952","volume":"20","author":"Lisio","year":"2019","journal-title":"Int J Mol Sci"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11912-020-00933-8","article-title":"First-line management of advanced high-grade serous ovarian cancer","volume":"22","author":"Mahmood","year":"2020","journal-title":"Curr Oncol Rep."},{"key":"B7","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/S0140-6736(14)62223-6","article-title":"Primary chemotherapy versus primary surgery for newly diagnosed advanced ovarian cancer (CHORUS): an open-label, randomised, controlled, non-inferiority trial","volume":"386","author":"Kehoe","year":"2015","journal-title":"Lancet."},{"key":"B8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ygyno.2010.08.011","article-title":"Neoadjuvant chemotherapy in advanced ovarian cancer: what kind of evidence is needed to convince US gynaecological oncologists?","volume":"119","author":"Vergote","year":"2010","journal-title":"Gynecol Oncol."},{"key":"B9","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s12943-022-01526-8","article-title":"Proteomics technologies for cancer liquid biopsies","volume":"21","author":"Ding","year":"2022","journal-title":"Mol Cancer."},{"key":"B10","first-page":"55","article-title":"Proteomics: application of next-generation proteomics in cancer research Proteomics","author":"Mir","year":"2023"},{"key":"B11","doi-asserted-by":"publisher","first-page":"154996","DOI":"10.1016\/j.prp.2023.154996","article-title":"Artificial intelligence in cancer diagnosis: opportunities and challenges","volume":"253","author":"Alshuhri","year":"2024","journal-title":"Pathol.-Res. Pract."},{"key":"B12","doi-asserted-by":"publisher","first-page":"107356","DOI":"10.1016\/j.compbiomed.2023.107356","article-title":"Artificial intelligence in cancer diagnosis and therapy: current status and future perspective","author":"Sufyan","year":"2023","journal-title":"Comput Biol Med."},{"key":"B13","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1186\/s13045-023-01514-5","article-title":"Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment","volume":"16","author":"Zhang","year":"2023","journal-title":"J Hematol Oncol."},{"key":"B14","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s12014-022-09372-y","article-title":"Proteomic alterations associated with residual disease in neoadjuvant chemotherapy treated ovarian cancer tissues","volume":"19","author":"Penick","year":"2022","journal-title":"Clin Proteomics."},{"key":"B15","doi-asserted-by":"crossref","DOI":"10.1145\/3542954.3543024","article-title":"Class imbalance problems in machine learning: a review of methods and future challenges","author":"Niaz","year":"2022","journal-title":"Paper presented at the Proceedings of the 2nd International Conference on Computing Advancements."},{"key":"B16","doi-asserted-by":"publisher","first-page":"8","DOI":"10.32614\/RJ-2014-008","article-title":"ROSE: a package for binary imbalanced learning","volume":"6","author":"Lunardon","year":"2014","journal-title":"R journal"},{"key":"B17","doi-asserted-by":"publisher","first-page":"4543","DOI":"10.1007\/s10489-021-02550-9","article-title":"A comprehensive survey on feature selection in the various fields of machine learning","volume":"52","author":"Dhal","year":"2022","journal-title":"Appl Intellig."},{"key":"B18","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1186\/s12859-023-05300-5","article-title":"A diabetes prediction model based on Boruta feature selection and ensemble learning","volume":"24","author":"Zhou","year":"2023","journal-title":"BMC Bioinformat."},{"key":"B19","doi-asserted-by":"publisher","first-page":"221","DOI":"10.22364\/bjmc.2017.5.2.05","article-title":"Comparison of naive bayes, random forest, decision tree, support vector machines, and logistic regression classifiers for text reviews classification","volume":"5","author":"Pranckevi\u010dius","year":"2017","journal-title":"Baltic J Modern Comp."},{"key":"B20","doi-asserted-by":"publisher","first-page":"3589","DOI":"10.3892\/mmr.2018.9362","article-title":"Establishment of a SVM classifier to predict recurrence of ovarian cancer","volume":"18","author":"Zhou","year":"2018","journal-title":"Mol Med Rep."},{"key":"B21","article-title":"Leveraging random forests for ovarian cancer detection and precision prediction","author":"Inture","year":"2023","journal-title":"Paper presented at the 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA)"},{"key":"B22","doi-asserted-by":"publisher","first-page":"6129817","DOI":"10.1155\/2022\/6129817","article-title":"Role of bioinformatics analysis in early differential diagnosis of ovarian cancer","volume":"2022","author":"Zhang","year":"2022","journal-title":"Contrast Media Mol Imag."},{"key":"B23","doi-asserted-by":"publisher","first-page":"100180","DOI":"10.1016\/j.imu.2019.100180","article-title":"A random forest based predictor for medical data classification using feature ranking","volume":"15","author":"Alam","year":"2019","journal-title":"Inform Med Unlocked."},{"key":"B24","doi-asserted-by":"publisher","first-page":"81","DOI":"10.48161\/qaj.v1n2a50","article-title":"Machine learning applications based on SVM classification a review","volume":"1","author":"Abdullah","year":"2021","journal-title":"Qubahan Acad J."},{"key":"B25","first-page":"128","article-title":"Enhancing cancer detection and classification with ensemble machine learning approaches","volume-title":"Artificial Intelligence Revolutionizing Cancer Care","author":"Swarnkar","year":"2025"},{"key":"B26","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Arrieta","year":"2020","journal-title":"Inform. Fusion."},{"key":"B27","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1002\/aisy.202400304","article-title":"A perspective on explainable artificial intelligence methods: SHAP and LIME","volume":"20","author":"Salih","year":"2024","journal-title":"Adv Intellig Syst."},{"key":"B28","author":"IBM","year":"2019","journal-title":"IBM SPSS Statistics for Windows. V. 26.0"},{"key":"B29","doi-asserted-by":"crossref","DOI":"10.1201\/9781315382548","author":"Gandrud","year":"2018","journal-title":"Reproducible Research with R and R Studio"},{"key":"B30","first-page":"354","article-title":"Python\u2013the fastest growing programming language","volume":"4","author":"Srinath","year":"2017","journal-title":"Int Res J Eng Technol"},{"key":"B31","doi-asserted-by":"publisher","first-page":"11239","DOI":"10.3390\/ijms252011239","article-title":"The role of tumor biomarkers in tailoring the approach to advanced ovarian cancer","volume":"25","author":"Tonti","year":"2024","journal-title":"Int J Mol Sci"},{"key":"B32","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.3802\/jgo.2024.35.e104","article-title":"Targeting BRAF pathway in low-grade serous ovarian cancer","volume":"35","author":"Perrone","year":"2024","journal-title":"J Gynecol Oncol."},{"key":"B33","doi-asserted-by":"publisher","first-page":"1164408","DOI":"10.3389\/fimmu.2023.1164408","article-title":"Machine learning-based integration develops an immune-related risk model for predicting prognosis of high-grade serous ovarian cancer and providing therapeutic strategies","volume":"14","author":"Wu","year":"2023","journal-title":"Front Immunol."},{"key":"B34","doi-asserted-by":"publisher","first-page":"1875","DOI":"10.3390\/cancers13081875","article-title":"Development of machine learning models to predict platinum sensitivity of high-grade serous ovarian carcinoma","volume":"13","author":"Hwangbo","year":"2021","journal-title":"Cancers."},{"key":"B35","doi-asserted-by":"publisher","first-page":"972913","DOI":"10.1155\/2013\/972913","article-title":"Role of glutathione in cancer progression and chemoresistance","volume":"2013","author":"Traverso","year":"2013","journal-title":"Oxid Med Cell Longev."},{"key":"B36","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1038\/bjc.1996.384","article-title":"The relationship between tumour glutathione concentration, glutathione S-transferase isoenzyme expression and response to single agent carboplatin in epithelial ovarian cancer patients","volume":"74","author":"Ghazal-Aswad","year":"1996","journal-title":"Br J Cancer."},{"key":"B37","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.1038\/bjc.2014.386","article-title":"Glutathione S-transferase P1 (GSTP1) directly influences platinum drug chemosensitivity in ovarian tumour cell lines","volume":"111","author":"Sawers","year":"2014","journal-title":"Br J Cancer."},{"key":"B38","doi-asserted-by":"publisher","first-page":"1768","DOI":"10.1039\/C6MB00250A","article-title":"A tyrosine-reactive irreversible inhibitor for glutathione S-transferase Pi (GSTP1)","volume":"12","author":"Crawford","year":"2016","journal-title":"Mol Biosyst."},{"key":"B39","doi-asserted-by":"publisher","first-page":"2750","DOI":"10.3390\/ijms20112750","article-title":"Gene expression indicates altered immune modulation and signaling pathway activation in ovarian cancer patients resistant to topotecan","volume":"20","author":"Menyh\u00e1rt","year":"2019","journal-title":"Int J Mol Sci."},{"key":"B40","doi-asserted-by":"publisher","first-page":"eaan2507","DOI":"10.1126\/science.aan2507","article-title":"A pathology atlas of the human cancer transcriptome","volume":"357","author":"Uhlen","year":"2017","journal-title":"Science"},{"key":"B41","doi-asserted-by":"publisher","first-page":"72","DOI":"10.3390\/biom7040072","article-title":"Peptidylprolyl isomerases as in vivo carriers for drugs that target various intracellular entities","volume":"7","author":"Galat","year":"2017","journal-title":"Biomolecules."},{"key":"B42","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1038\/sj.bjc.6606014","article-title":"The role of FKBP5 in cancer aetiology and chemoresistance","volume":"104","author":"Li","year":"2011","journal-title":"Br J Cancer."},{"key":"B43","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1016\/j.coph.2011.03.008","article-title":"FKBP51 regulation of AKT\/protein kinase B phosphorylation","volume":"11","author":"Wang","year":"2011","journal-title":"Curr Opin Pharmacol."},{"key":"B44","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1128\/MCB.01891-08","article-title":"FKBP51 promotes assembly of the Hsp90 chaperone complex and regulates androgen receptor signaling in prostate cancer cells","volume":"30","author":"Ni","year":"2010","journal-title":"Mol Cell Biol."},{"key":"B45","doi-asserted-by":"publisher","first-page":"11939","DOI":"10.18632\/oncotarget.2654","article-title":"Transcriptomic profiling of taxol-resistant ovarian cancer cells identifies FKBP5 and the androgen receptor as critical markers of chemotherapeutic response","volume":"5","author":"Sun","year":"2014","journal-title":"Oncotarget."},{"key":"B46","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1038\/nature08964","article-title":"Native GABAB receptors are heteromultimers with a family of auxiliary subunits","volume":"465","author":"Schwenk","year":"2010","journal-title":"Nature."},{"key":"B47","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1002\/cne.22610","article-title":"Distribution of the auxiliary GABAB receptor subunits KCTD8, 12, 12b, and 16 in the mouse brain","volume":"519","author":"Metz","year":"2011","journal-title":"J Comparat Neurol."},{"key":"B48","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1093\/jjco\/hyr121","article-title":"Pfetin as a prognostic biomarker for gastrointestinal stromal tumor: validation study in multiple clinical facilities","volume":"41","author":"Kubota","year":"2011","journal-title":"Jpn J Clin Oncol."},{"key":"B49","doi-asserted-by":"publisher","first-page":"1707","DOI":"10.1158\/1078-0432.CCR-07-1478","article-title":"Pfetin as a prognostic biomarker of gastrointestinal stromal tumors revealed by proteomics","volume":"14","author":"Suehara","year":"2008","journal-title":"Clini Cancer Res."},{"key":"B50","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1038\/nrc2499","article-title":"Cancer stem cells in solid tumours: accumulating evidence and unresolved questions","volume":"8","author":"Visvader","year":"2008","journal-title":"Nat Rev Cancer."},{"key":"B51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1479-7364-5-4-283","article-title":"Update on the aldehyde dehydrogenase gene (ALDH) superfamily","volume":"5","author":"Jackson","year":"2011","journal-title":"Hum Genomics."},{"key":"B52","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1002\/stem.563","article-title":"Aldehyde dehydrogenase activity of breast cancer stem cells is primarily due to isoform ALDH1A3 and its expression is predictive of metastasis","volume":"29","author":"Marcato","year":"2011","journal-title":"Stem Cells."},{"key":"B53","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.ygyno.2018.05.006","article-title":"The prognostic impact of cancer stem-like cell biomarker aldehyde dehydrogenase-1 (ALDH1) in ovarian cancer: a meta-analysis","volume":"150","author":"Ruscito","year":"2018","journal-title":"Gynecol Oncol."},{"key":"B54","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1080\/15384047.2017.1295175","article-title":"Decreased expression of ALDH5A1 predicts prognosis in patients with ovarian cancer","volume":"18","author":"Tian","year":"2017","journal-title":"Cancer Biol Ther."},{"key":"B55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2407-12-329","article-title":"Characterization of aldehyde dehydrogenase isozymes in ovarian cancer tissues and sphere cultures","volume":"12","author":"Saw","year":"2012","journal-title":"BMC Cancer."}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2025.1562558\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T05:36:46Z","timestamp":1753249006000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2025.1562558\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,23]]},"references-count":55,"alternative-id":["10.3389\/fmed.2025.1562558"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2025.1562558","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,23]]},"article-number":"1562558"}}