A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer Patients
Colorectal 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-throughp...
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MDPI AG
2023-04-01
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Online Access: | https://www.mdpi.com/2218-1989/13/5/589 |
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author | Fatma Hilal Yagin Abedalrhman Alkhateeb Cemil Colak Mohammad Azzeh Burak Yagin Luis Rueda |
author_facet | Fatma Hilal Yagin Abedalrhman Alkhateeb Cemil Colak Mohammad Azzeh Burak Yagin Luis Rueda |
author_sort | Fatma Hilal Yagin |
collection | DOAJ |
description | Colorectal 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 <i>t</i>-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 <i>p</i>-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 <i>p</i>-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). |
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id | doaj.art-300a0010df58444badc7e762f6b68397 |
institution | Directory Open Access Journal |
issn | 2218-1989 |
language | English |
last_indexed | 2024-03-11T03:30:08Z |
publishDate | 2023-04-01 |
publisher | MDPI AG |
record_format | Article |
series | Metabolites |
spelling | doaj.art-300a0010df58444badc7e762f6b683972023-11-18T02:25:00ZengMDPI AGMetabolites2218-19892023-04-0113558910.3390/metabo13050589A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer PatientsFatma Hilal Yagin0Abedalrhman Alkhateeb1Cemil Colak2Mohammad Azzeh3Burak Yagin4Luis Rueda5Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280 Malatya, TurkeySoftware Engineering Department, King Hussein School of Computing Science, Princess Sumaya University for Technology, Amman P.O. Box 1438, JordanDepartment of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280 Malatya, TurkeyData Science Department, King Hussein School of Computing Science, Princess Sumaya University for Technology, Amman P.O. Box 1438, JordanDepartment of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280 Malatya, TurkeySchool of Computer Science, University of Windsor, Windsor, ON N9B 3P4, CanadaColorectal 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 <i>t</i>-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 <i>p</i>-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 <i>p</i>-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).https://www.mdpi.com/2218-1989/13/5/589colorectal cancermetabolomics profilingmachine learningbiomarker discovery |
spellingShingle | Fatma Hilal Yagin Abedalrhman Alkhateeb Cemil Colak Mohammad Azzeh Burak Yagin Luis Rueda A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer Patients Metabolites colorectal cancer metabolomics profiling machine learning biomarker discovery |
title | A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer Patients |
title_full | A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer Patients |
title_fullStr | A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer Patients |
title_full_unstemmed | A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer Patients |
title_short | A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer Patients |
title_sort | fecal microbial extracellular vesicles based metabolomics machine learning framework and biomarker discovery for predicting colorectal cancer patients |
topic | colorectal cancer metabolomics profiling machine learning biomarker discovery |
url | https://www.mdpi.com/2218-1989/13/5/589 |
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