A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer Patients

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dc.authoridYagin, Fatma Hilal/0000-0002-9848-7958
dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridAzzeh, Mohammad/0000-0002-0323-6452
dc.authoridRueda, Luis/0000-0001-7988-2058
dc.authoridAlkhateeb, Abedalrhman/0000-0002-1751-7570
dc.authorwosidYagin, Fatma Hilal/ABI-8066-2020
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.authorwosidAzzeh, Mohammad/G-5472-2017
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorAlkhateeb, Abedalrhman
dc.contributor.authorColak, Cemil
dc.contributor.authorAzzeh, Mohammad
dc.contributor.authorYagin, Burak
dc.contributor.authorRueda, Luis
dc.date.accessioned2024-08-04T20:53:44Z
dc.date.available2024-08-04T20:53:44Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractColorectal cancer (CRC) is one of the most common and lethal diseases among all types of cancer, and metabolites play a significant role in the development of this complex disease. This study aimed to identify potential biomarkers and targets in the diagnosis and treatment of CRC using high-throughput metabolomics. Metabolite data extracted from the feces of CRC patients and healthy volunteers were normalized with the median normalization and Pareto scale for multivariate analysis. Univariate ROC analysis, the t-test, and analysis of fold changes (FCs) were applied to identify biomarker candidate metabolites in CRC patients. Only metabolites that overlapped the two different statistical approaches (false-discovery-rate-corrected p-value < 0.05 and AUC > 0.70) were considered in the further analysis. Multivariate analysis was performed with biomarker candidate metabolites based on linear support vector machines (SVM), partial least squares discrimination analysis (PLS-DA), and random forests (RF). The model identified five biomarker candidate metabolites that were significantly and differently expressed (adjusted p-value < 0.05) in CRC patients compared to healthy controls. The metabolites were succinic acid, aminoisobutyric acid, butyric acid, isoleucine, and leucine. Aminoisobutyric acid was the metabolite with the highest discriminatory potential in CRC, with an AUC equal to 0.806 (95% CI = 0.700-0.897), and was down-regulated in CRC patients. The SVM model showed the most substantial discrimination capacity for the five metabolites selected in the CRC screening, with an AUC of 0.985 (95% CI: 0.94-1).en_US
dc.description.sponsorshipKing Abdullah I School of Graduate Studies and Scientific Research at the Princess Sumaya University for Technology seed fund [2021/202225 (16)]; King Abdullah II for Scientific Research Support Fund from the Ministry of Higher Education [ICT/1/16/2022]en_US
dc.description.sponsorshipThis research was funded by the King Abdullah I School of Graduate Studies and Scientific Research at the Princess Sumaya University for Technology seed fund, grant number 2021/2022-25 (16), and the King Abdullah II for Scientific Research Support Fund from the Ministry of Higher Education, grant number (ICT/1/16/2022). The recipient of these funds was Abedalrhman Alkhateeb.en_US
dc.identifier.doi10.3390/metabo13050589
dc.identifier.issn2218-1989
dc.identifier.issue5en_US
dc.identifier.pmid37233630en_US
dc.identifier.scopus2-s2.0-85160335341en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/metabo13050589
dc.identifier.urihttps://hdl.handle.net/11616/101374
dc.identifier.volume13en_US
dc.identifier.wosWOS:000997039000001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofMetabolitesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectcolorectal canceren_US
dc.subjectmetabolomics profilingen_US
dc.subjectmachine learningen_US
dc.subjectbiomarker discoveryen_US
dc.titleA Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer Patientsen_US
dc.typeArticleen_US

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