Machine Learning-based Prediction of HBV-related Hepatocellular Carcinoma and Detection of Key Candidate Biomarkers

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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.authorKucukakcali, Zeynep
dc.contributor.authorAkbulut, Sami
dc.contributor.authorColak, Cemil
dc.date.accessioned2024-08-04T20:10:15Z
dc.date.available2024-08-04T20:10:15Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractObjective: This study aimed to classify open-access gene expression data of patients with hepatitis B virus-related hepatocellular carcinoma (HBV + HCC) and chronic HBV without HCC (HBV alone) using the XGBoost method, one of the machine learning methods, and reveal important genes that may cause HCC.Methods: This case-control study used the open-access gene expression data of patients with HBV + HCC and HBV alone. Data from 17 patients with HBV + HCC and 36 patients with HBV were included. XGBoost was constructed for the classification via 10-fold cross-validation. Accuracy, balanced accuracy, sensitivity, selectivity, positive-predictive value, and negative-predictive value performance metrics were evaluated for model performance. Results: According to the feature-selection method, 18 genes were selected, and modeling was performed with these input variables. Accuracy, balanced accuracy, sensitivity, specificity, positive-predictive value, negative-predictive value, and F1 score obtained from XGBoost model were 98.1%, 98.6%, 100%, 97.2%, 94.4%, 100%, and 97.1%, respectively. Based on the predictor importance findings acquired from XGBoost, the RNF26, FLJ10233, ACBD6, RBM12, PFAS, H3C11, and GKP5 can be employed as potential biomarkers of HBV-related HCC.Conclusions: In this study, genes that may be possible biomarkers of HBV-related HCC were determined using a machine learning-based prediction approach. After the reliability of the obtained genes are clinically verified in subsequent research, therapeutic procedures can be established based on these genes, and their usefulness in clinical practice may be documented.en_US
dc.identifier.doi10.4274/MMJ.galenos.2022.39049
dc.identifier.endpage263en_US
dc.identifier.issn2149-2042
dc.identifier.issn2149-4606
dc.identifier.issue3en_US
dc.identifier.pmid36128800en_US
dc.identifier.scopus2-s2.0-85142669344en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage255en_US
dc.identifier.trdizinid1134332en_US
dc.identifier.urihttps://doi.org/10.4274/MMJ.galenos.2022.39049
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1134332
dc.identifier.urihttps://hdl.handle.net/11616/92681
dc.identifier.volume37en_US
dc.identifier.wosWOS:001109594000004en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherGalenos Publ Houseen_US
dc.relation.ispartofMedeniyet Medical Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHepatocellular carcinomaen_US
dc.subjecthepatitis B infectionen_US
dc.subjectchronic liver diseaseen_US
dc.subjectgene expressionen_US
dc.titleMachine Learning-based Prediction of HBV-related Hepatocellular Carcinoma and Detection of Key Candidate Biomarkersen_US
dc.typeArticleen_US

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