Type 2 Diabetes Mellitus and its comorbidity, Alzheimer’s disease: Identifying critical microRNA using machine learning

Hind Alamro, Vladan Bajic, Mirjana T. Macvanin, Esma R. Isenovic, Takashi Gojobori, Magbubah Essack, Xin Gao

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.
Original languageEnglish (US)
JournalFrontiers in Endocrinology
Volume13
DOIs
StatePublished - Jan 19 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-01-23
Acknowledged KAUST grant number(s): BAS/1/1059-01-01, BAS/1/1624-01-01, FCC/1/1976-20-01, FCC/1/1976-26-01, OSR#4129, REI/1/4216-01-01, REI/1/4437-01-01, REI/1/4473-01-01, URF/1/3450-01-01, URF/1/4098-01-01.
Acknowledgements: The research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST) through grant awards Nos. BAS/1/1059-01-01, BAS/1/1624-01-01, FCC/1/1976-20-01, FCC/1/1976-26-01, URF/1/3450-01-01, REI/1/4216-01-01, REI/1/4437-01-01, REI/1/4473-01-01, and URF/1/4098-01-01. This work is part of the collaboration between the Laboratory of Radiobiology and Molecular Genetics, Vinca Institute of Nuclear Sciences, University of Belgrade, Belgrade, Serbia and King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, Saudi Arabia. Also, this work was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia (Contract No. #451-03-9/2021-14/200017) and KAUST grant OSR#4129 (awarded to EI and TG).

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism

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