Predicting Duodenal Cancer Risk in Patients with Familial Adenomatous Polyposis Using Machine Learning Model

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dc.authoridAkbulut, Sami/0000-0002-6864-7711
dc.authorwosidAkbulut, Sami/L-9568-2014
dc.contributor.authorAkbulut, Sami
dc.contributor.authorKucukakcali, Zeynep
dc.contributor.authorColak, Cemil
dc.date.accessioned2024-08-04T20:10:22Z
dc.date.available2024-08-04T20:10:22Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground/Aims: The aim of this study was to both classify data of familial adenomatous polyposis patients with and without duodenal cancer and to identify important genes that may be related to duodenal cancer by XGboost model. Materials and Methods: The current study was performed using expression profile data from a series of duodenal samples from familial adenomatous polyposis patients to explore variations in the familial adenomatous polyposis duodenal adenoma-carcinoma sequence. The expression profiles obtained from cancerous, adenomatous, and normal tissues of 12 familial adenomatous polyposis patients with duodenal cancer and the tissues of 12 familial adenomatous polyposis patients without duodenal cancer were compared. The ElasticNet approach was utilized for the feature selection. Using 5-fold cross-validation, one of the machine learning approaches, XGboost, was utilized to classify duodenal cancer. Accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score performance metrics were assessed for model performance. Results: According to the variable importance obtained from the modeling, ADH1C, DEFA5, CPS1, SPP1, DMBT1, VCAN-AS1, APOB genes (cancer vs. adenoma); LOC399753, APOA4, MIR548X, and ADH1C genes (adenoma vs. adenoma); SNORD123, CEACAM6, SNORD78, ANXA10, SPINK1, and CPS1 (normal vs. adenoma) genes can be used as predictive biomarkers. Conclusions: The proposed model used in this study shows that the aforementioned genes can forecast the risk of duodenal cancer in patients with familial adenomatous polyposis. More comprehensive analyses should be performed in the future to assess the reliability of the genes determined.en_US
dc.identifier.doi10.5152/tjg.2023.22346
dc.identifier.endpage1034en_US
dc.identifier.issn2148-5607
dc.identifier.issue10en_US
dc.identifier.pmid37565794en_US
dc.identifier.scopus2-s2.0-85174080358en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1025en_US
dc.identifier.trdizinid1241564en_US
dc.identifier.urihttps://doi.org/10.5152/tjg.2023.22346
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1241564
dc.identifier.urihttps://hdl.handle.net/11616/92748
dc.identifier.volume34en_US
dc.identifier.wosWOS:001101965800005en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherAvesen_US
dc.relation.ispartofTurkish Journal of Gastroenterologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFamilial adenomatous polyposisen_US
dc.subjectduodenal canceren_US
dc.subjectgene mutationsen_US
dc.subjectgenomicsen_US
dc.subjectmachine learningen_US
dc.subjectXGboosten_US
dc.titlePredicting Duodenal Cancer Risk in Patients with Familial Adenomatous Polyposis Using Machine Learning Modelen_US
dc.typeArticleen_US

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