Identification of a Novel Lipidomic Biomarker for Hepatocyte Carcinoma Diagnosis: Advanced Boosting Machine Learning Techniques Integrated with Explainable Artificial Intelligence

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dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorColak, Cemil
dc.contributor.authorAl-Hashem, Fahaid
dc.contributor.authorAlzakari, Sarah A.
dc.contributor.authorAlhussan, Amel Ali
dc.contributor.authorAghaei, Mohammadreza
dc.date.accessioned2026-04-04T13:30:59Z
dc.date.available2026-04-04T13:30:59Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractBackground: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, often diagnosed at late stages due to the limited sensitivity of current screening tools. This study explores whether blood-based lipidomic profiling, combined with explainable artificial intelligence (XAI), can improve early and interpretable detection of HCC. Methods: We analyzed lipidomic data from 219 HCC patients and 219 matched healthy controls using liquid chromatography-mass spectrometry. An Explainable Boosting Machine (EBM) was employed to identify discriminatory lipid biomarkers and was compared against several standard machine learning algorithms. Results: The EBM model achieved superior performance with 87.0% accuracy, 87.7% sensitivity, 86.3% specificity, and an AUC of 91.8%, outperforming other models. Key lipid biomarkers identified included specific phosphatidylcholines (PC 38:2, PC 40:4), sphingomyelins (SM d40:2 B), and lysophosphatidylcholines (LPC 18:2), which exhibited significant alterations in HCC patients and highlighted disruptions in sphingolipid metabolism. Conclusions: Integration of lipidomics with explainable machine learning offers a powerful, transparent approach for HCC biomarker discovery, achieving high diagnostic accuracy while providing biological insights. This strategy holds promise for developing non-invasive, clinically interpretable screening tools to improve early detection of liver cancer.
dc.description.sponsorshipPrincess Nourah bint Abdulrahman University Researchers Supporting Project [PNURSP2025R716]
dc.description.sponsorshipPrincess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R716), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
dc.identifier.doi10.3390/metabo15110716
dc.identifier.issn2218-1989
dc.identifier.issue11
dc.identifier.orcid0000-0002-9848-7958
dc.identifier.orcid0000-0001-5735-3825
dc.identifier.orcid0000-0001-7530-7961
dc.identifier.orcid0000-0001-5406-098X
dc.identifier.pmid41295302
dc.identifier.scopus2-s2.0-105022904473
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/metabo15110716
dc.identifier.urihttps://hdl.handle.net/11616/108498
dc.identifier.volume15
dc.identifier.wosWOS:001624176700001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofMetabolites
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjecthepatocellular carcinoma
dc.subjectlipidomics
dc.subjectbiomarkers
dc.subjectmachine learning
dc.subjectexplainable boosting machine
dc.titleIdentification of a Novel Lipidomic Biomarker for Hepatocyte Carcinoma Diagnosis: Advanced Boosting Machine Learning Techniques Integrated with Explainable Artificial Intelligence
dc.typeArticle

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