Co‑expressed microRNAs, target genes and pathways related to metabolism, inflammation and endocrine function in individuals at risk for type 2 diabetes

Flowers, Elena; Asam, Kesava; Allen, Isabel Elaine; Kanaya, Alka M.; Aouizerat, Bradley E.

doi:10.3892/mmr.2022.12672

Cited by 9 publications

(13 citation statements)

References 48 publications

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“…The gene targets and miRNAs identified in this review mostly differ from those that have been previously emphasized for T1D and T2D, suggesting a distinct pathophysiology for GDM ( 76 – 78 ). Some exceptions are miR-16-5p ( 43 ), miR-29a-3p, miR-146a, miR-182, and miR-342-3p as previously mentioned in section 4.7 ( 18 , 31 – 34 , 36 , 37 , 43 , 70 , 76 , 77 , 79 , 80 ). MiR-29a-3p was reported with mixed results in both GDM and T2D studies, with one or more study showing it to be upregulated and one or more showing it to be downregulated compared with controls.…”

Section: Discussionmentioning

confidence: 95%

“…A prior study conducted by Flowers et al. (2022) assessed pathways targeted by differentially expressed miRNAs in people at risk for T2D and identified three themes (i.e., metabolism and inflammation, endocrine, and hormone) ( 43 ). Many of the implicated pathways identified by the studies included in this review also fit within these themes.…”

Section: Discussionmentioning

confidence: 99%

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Systematic review of transcriptome and microRNAome associations with gestational diabetes mellitus

et al. 2023

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PurposeGestational diabetes (GDM) is associated with increased risk for preterm birth and related complications for both the pregnant person and newborn. Changes in gene expression have the potential to characterize complex interactions between genetic and behavioral/environmental risk factors for GDM. Our goal was to summarize the state of the science about changes in gene expression and GDM.DesignThe systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.MethodsPubMed articles about humans, in English, from any date were included if they described mRNA transcriptome or microRNA findings from blood samples in adults with GDM compared with adults without GDM.ResultsSixteen articles were found representing 1355 adults (n=674 with GDM, n=681 controls) from 12 countries. Three studies reported transcriptome results and thirteen reported microRNA findings. Identified pathways described various aspects of diabetes pathogenesis, including glucose and insulin signaling, regulation, and transport; natural killer cell mediated cytotoxicity; and fatty acid biosynthesis and metabolism. Studies described 135 unique miRNAs that were associated with GDM, of which eight (miR-16-5p, miR-17-5p, miR-20a-5p, miR-29a-3p, miR-195-5p, miR-222-3p, miR-210-3p, and miR-342-3p) were described in 2 or more studies. Findings suggest that miRNA levels vary based on the time in pregnancy when GDM develops, the time point at which they were measured, sex assigned at birth of the offspring, and both the pre-pregnancy and gestational body mass index of the pregnant person.ConclusionsThe mRNA, miRNA, gene targets, and pathways identified in this review contribute to our understanding of GDM pathogenesis; however, further research is warranted to validate previous findings. In particular, longitudinal repeated-measures designs are needed that control for participant characteristics (e.g., weight), use standardized data collection methods and analysis tools, and are sufficiently powered to detect differences between subgroups. Findings may be used to improve early diagnosis, prevention, medication choice and/or clinical treatment of patients with GDM.

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

Systematic review of transcriptome and microRNAome associations with gestational diabetes mellitus

et al. 2023

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“…A useful feature of the most cited databases gives the user the option to download the validated data, making it easy to manipulate further in programs such as R. The download feature was applied to this review, which allowed us to identify with certainty the total numbers of the miRNAs and genes included. Regarding citations, frequent use by the scientific community suggests that those tools are user-friendly and intuitive ( 39 , 40 ).…”

Section: Discussionmentioning

confidence: 99%

Review of databases for experimentally validated human microRNA–mRNA interactions

et al. 2023

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MicroRNAs (miRs) may contribute to disease etiology by influencing gene expression. Numerous databases are available for miR target prediction and validation, but their functionality is varied, and outputs are not standardized. The purpose of this review is to identify and describe databases for cataloging validated miR targets. Using Tools4miRs and PubMed, we identified databases with experimentally validated targets, human data, and a focus on miR–messenger RNA (mRNA) interactions. Data were extracted about the number of times each database was cited, the number of miRs, the target genes, the interactions per database, experimental methodology and key features of each database. The search yielded 10 databases, which in order of most cited to least were: miRTarBase, starBase/The Encyclopedia of RNA Interactomes, DIANA-TarBase, miRWalk, miRecords, miRGator, miRSystem, miRGate, miRSel and targetHub. Findings from this review suggest that the information presented within miR target validation databases can be enhanced by adding features such as flexibility in performing queries in multiple ways, downloadable data, ongoing updates and integrating tools for further miR–mRNA target interaction analysis. This review is designed to aid researchers, especially those new to miR bioinformatics tools, in database selection and to offer considerations for future development and upkeep of validation tools. Database URL http://mirtarbase.cuhk.edu.cn/

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“…RFXANK mutations are prevalent in bare lymphocyte syndrome (BLS) group B, an immunodeficiency disorder affecting CD4+ T and B cells [ 50 ]. However, RFXANK has not been associated with obesity or T2D in previous studies, though MHCII has been found to play a role in obesity [ 51 ], and our own prior studies have identified pathways related to inflammation and immunity as common themes in individuals at risk for T2D [ 52 ]. Deng et al [ 51 ] analyzed RFXANK between 7 obese women and 7 lean postmenopausal women but did not find the expression to be significantly different.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Weight Loss to Decrease the Risk for Type 2 Diabetes Using Multidimensional Data in Filipino Americans: Secondary Analysis

Chang¹,

Fukuoka²,

Aouizerat³

et al. 2023

JMIR Diabetes

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Background Type 2 diabetes (T2D) has an immense disease burden, affecting millions of people worldwide and costing billions of dollars in treatment. As T2D is a multifactorial disease with both genetic and nongenetic influences, accurate risk assessments for patients are difficult to perform. Machine learning has served as a useful tool in T2D risk prediction, as it can analyze and detect patterns in large and complex data sets like that of RNA sequencing. However, before machine learning can be implemented, feature selection is a necessary step to reduce the dimensionality in high-dimensional data and optimize modeling results. Different combinations of feature selection methods and machine learning models have been used in studies reporting disease predictions and classifications with high accuracy. Objective The purpose of this study was to assess the use of feature selection and classification approaches that integrate different data types to predict weight loss for the prevention of T2D. Methods The data of 56 participants (ie, demographic and clinical factors, dietary scores, step counts, and transcriptomics) were obtained from a previously completed randomized clinical trial adaptation of the Diabetes Prevention Program study. Feature selection methods were used to select for subsets of transcripts to be used in the selected classification approaches: support vector machine, logistic regression, decision trees, random forest, and extremely randomized decision trees (extra-trees). Data types were included in different classification approaches in an additive manner to assess model performance for the prediction of weight loss. Results Average waist and hip circumference were found to be different between those who exhibited weight loss and those who did not exhibit weight loss (P=.02 and P=.04, respectively). The incorporation of dietary and step count data did not improve modeling performance compared to classifiers that included only demographic and clinical data. Optimal subsets of transcripts identified through feature selection yielded higher prediction accuracy than when all available transcripts were included. After comparison of different feature selection methods and classifiers, DESeq2 as a feature selection method and an extra-trees classifier with and without ensemble learning provided the most optimal results, as defined by differences in training and testing accuracy, cross-validated area under the curve, and other factors. We identified 5 genes in two or more of the feature selection subsets (ie, CDP-diacylglycerol-inositol 3-phosphatidyltransferase [CDIPT], mannose receptor C type 2 [MRC2], PAT1 homolog 2 [PATL2], regulatory factor X-associated ankyrin containing protein [RFXANK], and small ubiquitin like modifier 3 [SUMO3]). Conclusions Our results suggest that the inclusion of transcriptomic data in classification approaches for prediction has the potential to improve weight loss prediction models. Identification of which individuals are likely to respond to interventions for weight loss may help to prevent incident T2D. Out of the 5 genes identified as optimal predictors, 3 (ie, CDIPT, MRC2, and SUMO3) have been previously shown to be associated with T2D or obesity. Trial Registration ClinicalTrials.gov NCT02278939; https://clinicaltrials.gov/ct2/show/NCT02278939

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Co‑expressed microRNAs, target genes and pathways related to metabolism, inflammation and endocrine function in individuals at risk for type 2 diabetes

Cited by 9 publications

References 48 publications

Systematic review of transcriptome and microRNAome associations with gestational diabetes mellitus

Systematic review of transcriptome and microRNAome associations with gestational diabetes mellitus

Review of databases for experimentally validated human microRNA–mRNA interactions

Prediction of Weight Loss to Decrease the Risk for Type 2 Diabetes Using Multidimensional Data in Filipino Americans: Secondary Analysis

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