Artificial intelligence-based prediction of molecular and genetic markers for hepatitis C-related hepatocellular carcinoma

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dc.authoridAkbulut, Sami/0000-0002-6864-7711
dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authorwosidAkbulut, Sami/L-9568-2014
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.contributor.authorColak, Cemil
dc.contributor.authorKucukakcali, Zeynep
dc.contributor.authorAkbulut, Sami
dc.date.accessioned2024-08-04T20:57:11Z
dc.date.available2024-08-04T20:57:11Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground:Hepatocellular carcinoma (HCC) is the main cause of mortality from cancer globally. This paper intends to classify public gene expression data of patients with Hepatitis C virus-related HCC (HCV+HCC) and chronic HCV without HCC (HCV alone) through the XGboost approach and to identify key genes that may be responsible for HCC.Methods:The current research is a retrospective case-control study. Public data from 17 patients with HCV+HCC and 35 patients with HCV-alone samples were used in this study. An XGboost model was established for the classification by 10-fold cross-validation. Accuracy (AC), balanced accuracy (BAC), sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were utilized for performance assessment.Results:AC, BAC, sensitivity, specificity, positive predictive value, negative predictive value, and F1 scores from the XGboost model were 98.1, 97.1, 100, 94.1, 97.2, 100, and 98.6%, respectively. According to the variable importance values from the XGboost, the HAO2, TOMM20, GPC3, and PSMB4 genes can be considered potential biomarkers for HCV-related HCC.Conclusion:A machine learning-based prediction method discovered genes that potentially serve as biomarkers for HCV-related HCC. After clinical confirmation of the acquired genes in the following medical study, their therapeutic use can be established. Additionally, more detailed clinical works are needed to substantiate the significant conclusions in the current study.en_US
dc.identifier.doi10.1097/MS9.0000000000001210
dc.identifier.endpage4682en_US
dc.identifier.issn2049-0801
dc.identifier.issue10en_US
dc.identifier.pmid37811067en_US
dc.identifier.startpage4674en_US
dc.identifier.urihttps://doi.org/10.1097/MS9.0000000000001210
dc.identifier.urihttps://hdl.handle.net/11616/102402
dc.identifier.volume85en_US
dc.identifier.wosWOS:001079998200006en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherLippincott Williams & Wilkinsen_US
dc.relation.ispartofAnnals of Medicine and Surgeryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectartificial Intelligenceen_US
dc.subjectchronic liver diseaseen_US
dc.subjectgenetic markersen_US
dc.subjecthepatitis C infectionen_US
dc.subjecthepatocellular carcinomaen_US
dc.titleArtificial intelligence-based prediction of molecular and genetic markers for hepatitis C-related hepatocellular carcinomaen_US
dc.typeArticleen_US

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