Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research

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dc.contributor.authorYagin, Burak
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorColak, Cemil
dc.contributor.authorInceoglu, Feyza
dc.contributor.authorKadry, Seifedine
dc.contributor.authorKim, Jungeun
dc.date.accessioned2026-06-19T06:37:52Z
dc.date.available2026-06-19T06:37:52Z
dc.date.issued2023
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractAim: Method: This research presents a model combining machine learning (ML) techniques and eXplainable artificial intelligence (XAI) to predict breast cancer (BC) metastasis and reveal important genomic biomarkers in metastasis patients. Method: A total of 98 primary BC samples was analyzed, comprising 34 samples from patients who developed distant metastases within a 5-year follow-up period and 44 samples from patients who remained disease-free for at least 5 years after diagnosis. Genomic data were then subjected to biostatistical analysis, followed by the application of the elastic net feature selection method. This technique identified a restricted number of genomic biomarkers associated with BC metastasis. A light gradient boosting machine (LightGBM), categorical boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting Trees (GBT), and Ada boosting (AdaBoost) algorithms were utilized for prediction. To assess the models' predictive abilities, the accuracy, F1 score, precision, recall, area under the ROC curve (AUC), and Brier score were calculated as performance evaluation metrics. To promote interpretability and overcome the black box problem of ML models, a SHapley Additive exPlanations (SHAP) method was employed. Results: The LightGBM model outperformed other models, yielding remarkable accuracy of 96% and an AUC of 99.3%. In addition to biostatistical evaluation, in XAI-based SHAP results, increased expression levels of TSPYL5, ATP5E, CA9, NUP210, SLC37A1, ARIH1, PSMD7, UBQLN1, PRAME, and UBE2T (p <= 0.05) were found to be associated with an increased incidence of BC metastasis. Finally, decreased levels of expression of CACTIN, TGFB3, SCUBE2, ARL4D, OR1F1, ALDH4A1, PHF1, and CROCC (p <= 0.05) genes were also determined to increase the risk of metastasis in BC. Conclusion: The findings of this study may prevent disease progression and metastases and potentially improve clinical outcomes by recommending customized treatment approaches for BC patients.
dc.description.sponsorshipTechnology Development Program of MSS [S3033853]; Kongju National University
dc.description.sponsorshipThis research was partly supported by the Technology Development Program of MSS (No. S3033853) and by the research grant of the Kongju National University in 2023.
dc.identifier.doi10.3390/diagnostics13213314
dc.identifier.issn2075-4418
dc.identifier.issue21
dc.identifier.orcid0000-0001-6687-979X
dc.identifier.orcid0000-0002-9848-7958
dc.identifier.orcid0000-0001-5406-098X
dc.identifier.orcid0000-0003-1453-0937
dc.identifier.pmid37958210
dc.identifier.scopus2-s2.0-85176353499
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/diagnostics13213314
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5234
dc.identifier.volume13
dc.identifier.wosWOS:001100253500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofDiagnostics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectBreast Cancer Metastasis
dc.subjectMachine Learning Algorithms
dc.subjectGenomic Biomarkers
dc.subjectExplainable Artificial Intelligence
dc.subjectShap
dc.titleCancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research
dc.typeArticle

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