Integrating proteomics and explainable artificial intelligence: a comprehensive analysis of protein biomarkers for endometrial cancer diagnosis and prognosis

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dc.authoridYagin, Fatma Hilal/0000-0002-9848-7958
dc.authoridMELEKOGLU, RAUF/0000-0001-7113-6691
dc.authorwosidYagin, Fatma Hilal/ABI-8066-2020
dc.contributor.authorYasar, Seyma
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorMelekoglu, Rauf
dc.contributor.authorArdigo, Luca Paolo
dc.date.accessioned2024-08-04T20:56:06Z
dc.date.available2024-08-04T20:56:06Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractEndometrial cancer, which is the most common gynaecological cancer in women after breast, colorectal and lung cancer, can be diagnosed at an early stage. The first aim of this study is to classify age, tumor grade, myometrial invasion and tumor size, which play an important role in the diagnosis and prognosis of endometrial cancer, with machine learning methods combined with explainable artificial intelligence. 20 endometrial cancer patients proteomic data obtained from tumor biopsies taken from different regions of EC tissue were used. The data obtained were then classified according to age, tumor size, tumor grade and myometrial invasion. Then, by using three different machine learning methods, explainable artificial intelligence was applied to the model that best classifies these groups and possible protein biomarkers that can be used in endometrial prognosis were evaluated. The optimal model for age classification was XGBoost with AUC (98.8%), for tumor grade classification was XGBoost with AUC (98.6%), for myometrial invasion classification was LightGBM with AUC (95.1%), and finally for tumor size classification was XGBoost with AUC (94.8%). By combining the optimal models and the SHAP approach, possible protein biomarkers and their expressions were obtained for classification. Finally, EWRS1 protein was found to be common in three groups (age, myometrial invasion, tumor size). This article's findings indicate that models have been developed that can accurately classify factors including age, tumor grade, and myometrial invasion all of which are critical for determining the prognosis of endometrial cancer as well as potential protein biomarkers associated with these factors. Furthermore, we were able to provide an analysis of how the quantities of the proteins suggested as biomarkers varied throughout the classes by combining the SHAP values with these ideal models.en_US
dc.identifier.doi10.3389/fmolb.2024.1389325
dc.identifier.issn2296-889X
dc.identifier.pmid38894711en_US
dc.identifier.scopus2-s2.0-85196078765en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3389/fmolb.2024.1389325
dc.identifier.urihttps://hdl.handle.net/11616/102060
dc.identifier.volume11en_US
dc.identifier.wosWOS:001247224700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherFrontiers Media Saen_US
dc.relation.ispartofFrontiers in Molecular Biosciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectmachine learningen_US
dc.subjectexplainable artificial intelligenceen_US
dc.subjectendometrium canceren_US
dc.subjectproteomicen_US
dc.subjectbiomarkeren_US
dc.titleIntegrating proteomics and explainable artificial intelligence: a comprehensive analysis of protein biomarkers for endometrial cancer diagnosis and prognosisen_US
dc.typeArticleen_US

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