Biomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explainable artificial intelligence
BackgroundBreast cancer (BC) is a significant cause of morbidity and mortality in women. Although the important role of metabolism in the molecular pathogenesis of BC is known, there is still a need for robust metabolomic biomarkers and predictive models that will enable the detection and prognosis...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2024.1426964/full |
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| author | Fatma Hilal Yagin Yasin Gormez Fahaid Al-Hashem Irshad Ahmad Fuzail Ahmad Luca Paolo Ardigò |
| author_facet | Fatma Hilal Yagin Yasin Gormez Fahaid Al-Hashem Irshad Ahmad Fuzail Ahmad Luca Paolo Ardigò |
| author_sort | Fatma Hilal Yagin |
| collection | DOAJ |
| description | BackgroundBreast cancer (BC) is a significant cause of morbidity and mortality in women. Although the important role of metabolism in the molecular pathogenesis of BC is known, there is still a need for robust metabolomic biomarkers and predictive models that will enable the detection and prognosis of BC. This study aims to identify targeted metabolomic biomarker candidates based on explainable artificial intelligence (XAI) for the specific detection of BC.MethodsData obtained after targeted metabolomics analyses using plasma samples from BC patients (n = 102) and healthy controls (n = 99) were used. Machine learning (ML) models based on raw data were developed, then feature selection methods were applied, and the results were compared. SHapley Additive exPlanations (SHAP), an XAI method, was used to clinically explain the decisions of the optimal model in BC prediction.ResultsThe results revealed that variable selection increased the performance of ML models in BC classification, and the optimal model was obtained with the logistic regression (LR) classifier after support vector machine (SVM)-SHAP-based feature selection. SHAP annotations of the LR model revealed that Leucine, isoleucine, L-alloisoleucine, norleucine, and homoserine acids were the most important potential BC diagnostic biomarkers. Combining the identified metabolite markers provided robust BC classification measures with precision, recall, and specificity of 89.50%, 88.38%, and 83.67%, respectively.ConclusionIn conclusion, this study adds valuable information to the discovery of BC biomarkers and underscores the potential of targeted metabolomics-based diagnostic advances in the management of BC. |
| format | Article |
| id | doaj-art-abb4db0cf6fd4054a0e47218fe050ce1 |
| institution | DOAJ |
| issn | 2296-889X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj-art-abb4db0cf6fd4054a0e47218fe050ce12025-08-20T02:48:58ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2024-12-011110.3389/fmolb.2024.14269641426964Biomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explainable artificial intelligenceFatma Hilal Yagin0Yasin Gormez1Fahaid Al-Hashem2Irshad Ahmad3Fuzail Ahmad4Luca Paolo Ardigò5Department of Biostatistics and Medical Informatics, Faculty of Medicine, InonuUniversity, Malatya, TürkiyeDepartment of Management Information Systems, Faculty of Economics and Administrative Sciences, Sivas Cumhuriyet University, Sivas, TürkiyeDepartment of Physiology, College of Medicine, King Khalid University, Abha, Saudi ArabiaDepartment of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi ArabiaRespiratory Care Department, College of Applied Sciences, Almareefa University, Riyadh, Saudi ArabiaDepartment of Teacher Education, NLA University College, Oslo, NorwayBackgroundBreast cancer (BC) is a significant cause of morbidity and mortality in women. Although the important role of metabolism in the molecular pathogenesis of BC is known, there is still a need for robust metabolomic biomarkers and predictive models that will enable the detection and prognosis of BC. This study aims to identify targeted metabolomic biomarker candidates based on explainable artificial intelligence (XAI) for the specific detection of BC.MethodsData obtained after targeted metabolomics analyses using plasma samples from BC patients (n = 102) and healthy controls (n = 99) were used. Machine learning (ML) models based on raw data were developed, then feature selection methods were applied, and the results were compared. SHapley Additive exPlanations (SHAP), an XAI method, was used to clinically explain the decisions of the optimal model in BC prediction.ResultsThe results revealed that variable selection increased the performance of ML models in BC classification, and the optimal model was obtained with the logistic regression (LR) classifier after support vector machine (SVM)-SHAP-based feature selection. SHAP annotations of the LR model revealed that Leucine, isoleucine, L-alloisoleucine, norleucine, and homoserine acids were the most important potential BC diagnostic biomarkers. Combining the identified metabolite markers provided robust BC classification measures with precision, recall, and specificity of 89.50%, 88.38%, and 83.67%, respectively.ConclusionIn conclusion, this study adds valuable information to the discovery of BC biomarkers and underscores the potential of targeted metabolomics-based diagnostic advances in the management of BC.https://www.frontiersin.org/articles/10.3389/fmolb.2024.1426964/fullbreast cancermetabolomicsfeature selectionexplainable artificial intelligenceprognostic model |
| spellingShingle | Fatma Hilal Yagin Yasin Gormez Fahaid Al-Hashem Irshad Ahmad Fuzail Ahmad Luca Paolo Ardigò Biomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explainable artificial intelligence Frontiers in Molecular Biosciences breast cancer metabolomics feature selection explainable artificial intelligence prognostic model |
| title | Biomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explainable artificial intelligence |
| title_full | Biomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explainable artificial intelligence |
| title_fullStr | Biomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explainable artificial intelligence |
| title_full_unstemmed | Biomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explainable artificial intelligence |
| title_short | Biomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explainable artificial intelligence |
| title_sort | biomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients a hybrid methodology integrating machine learning and explainable artificial intelligence |
| topic | breast cancer metabolomics feature selection explainable artificial intelligence prognostic model |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2024.1426964/full |
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