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Type 2 Diabetes Mellitus and its comorbidity, Alzheimer's disease: Identifying critical microRNA using machine learning.
Front Endocrinol (Lausanne). 2022; 13:1084656.FE

Abstract

MicroRNAs (miRNAs) are critical regulators of gene expression in healthy and diseased states, and numerous studies have established their tremendous potential as a tool for improving the diagnosis of Type 2 Diabetes Mellitus (T2D) and its comorbidities. In this regard, we computationally identify novel top-ranked hub miRNAs that might be involved in T2D. We accomplish this via two strategies: 1) by ranking miRNAs based on the number of T2D differentially expressed genes (DEGs) they target, and 2) using only the common DEGs between T2D and its comorbidity, Alzheimer's disease (AD) to predict and rank miRNA. Then classifier models are built using the DEGs targeted by each miRNA as features. Here, we show the T2D DEGs targeted by hsa-mir-1-3p, hsa-mir-16-5p, hsa-mir-124-3p, hsa-mir-34a-5p, hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-129-2-3p, and hsa-mir-146a-5p are capable of distinguishing T2D samples from the controls, which serves as a measure of confidence in the miRNAs' potential role in T2D progression. Moreover, for the second strategy, we show other critical miRNAs can be made apparent through the disease's comorbidities, and in this case, overall, the hsa-mir-103a-3p models work well for all the datasets, especially in T2D, while the hsa-mir-124-3p models achieved the best scores for the AD datasets. To the best of our knowledge, this is the first study that used predicted miRNAs to determine the features that can separate the diseased samples (T2D or AD) from the normal ones, instead of using conventional non-biology-based feature selection methods.

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Authors+Show Affiliations

Alamro H
Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia.
Bajic V
Department of Radiology and Molecular Genetics, VINCA Institute of Nuclear Science - National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia.
Macvanin MT
Department of Radiology and Molecular Genetics, VINCA Institute of Nuclear Science - National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia.
Isenovic ER
Department of Radiology and Molecular Genetics, VINCA Institute of Nuclear Science - National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia.
Gojobori T
Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Essack M
Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Gao X
Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

MeSH

HumansAlzheimer DiseaseComorbidityDiabetes Mellitus, Type 2Machine LearningMicroRNAs

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

36743910

Citation

Alamro, Hind, et al. "Type 2 Diabetes Mellitus and Its Comorbidity, Alzheimer's Disease: Identifying Critical microRNA Using Machine Learning." Frontiers in Endocrinology, vol. 13, 2022, p. 1084656.
Alamro H, Bajic V, Macvanin MT, et al. Type 2 Diabetes Mellitus and its comorbidity, Alzheimer's disease: Identifying critical microRNA using machine learning. Front Endocrinol (Lausanne). 2022;13:1084656.
Alamro, H., Bajic, V., Macvanin, M. T., Isenovic, E. R., Gojobori, T., Essack, M., & Gao, X. (2022). Type 2 Diabetes Mellitus and its comorbidity, Alzheimer's disease: Identifying critical microRNA using machine learning. Frontiers in Endocrinology, 13, 1084656. https://doi.org/10.3389/fendo.2022.1084656
Alamro H, et al. Type 2 Diabetes Mellitus and Its Comorbidity, Alzheimer's Disease: Identifying Critical microRNA Using Machine Learning. Front Endocrinol (Lausanne). 2022;13:1084656. PubMed PMID: 36743910.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Type 2 Diabetes Mellitus and its comorbidity, Alzheimer's disease: Identifying critical microRNA using machine learning. AU - Alamro,Hind, AU - Bajic,Vladan, AU - Macvanin,Mirjana T, AU - Isenovic,Esma R, AU - Gojobori,Takashi, AU - Essack,Magbubah, AU - Gao,Xin, Y1 - 2023/01/19/ PY - 2022/10/30/received PY - 2022/12/23/accepted PY - 2023/2/6/entrez PY - 2023/2/7/pubmed PY - 2023/2/8/medline KW - Alzheimer’s disease KW - biomarker KW - comorbidity KW - diabetes KW - hsa-mir-103a-3p KW - hsa-mir-124-3p KW - machine learning KW - microRNA SP - 1084656 EP - 1084656 JF - Frontiers in endocrinology JO - Front Endocrinol (Lausanne) VL - 13 N2 - MicroRNAs (miRNAs) are critical regulators of gene expression in healthy and diseased states, and numerous studies have established their tremendous potential as a tool for improving the diagnosis of Type 2 Diabetes Mellitus (T2D) and its comorbidities. In this regard, we computationally identify novel top-ranked hub miRNAs that might be involved in T2D. We accomplish this via two strategies: 1) by ranking miRNAs based on the number of T2D differentially expressed genes (DEGs) they target, and 2) using only the common DEGs between T2D and its comorbidity, Alzheimer's disease (AD) to predict and rank miRNA. Then classifier models are built using the DEGs targeted by each miRNA as features. Here, we show the T2D DEGs targeted by hsa-mir-1-3p, hsa-mir-16-5p, hsa-mir-124-3p, hsa-mir-34a-5p, hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-129-2-3p, and hsa-mir-146a-5p are capable of distinguishing T2D samples from the controls, which serves as a measure of confidence in the miRNAs' potential role in T2D progression. Moreover, for the second strategy, we show other critical miRNAs can be made apparent through the disease's comorbidities, and in this case, overall, the hsa-mir-103a-3p models work well for all the datasets, especially in T2D, while the hsa-mir-124-3p models achieved the best scores for the AD datasets. To the best of our knowledge, this is the first study that used predicted miRNAs to determine the features that can separate the diseased samples (T2D or AD) from the normal ones, instead of using conventional non-biology-based feature selection methods. SN - 1664-2392 UR - https://www.unboundmedicine.com/medline/citation/36743910/Type_2_Diabetes_Mellitus_and_its_comorbidity_Alzheimer's_disease:_Identifying_critical_microRNA_using_machine_learning_ DB - PRIME DP - Unbound Medicine ER -