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pmid:38935970

Novel Molecular Networks and Regulatory MicroRNAs in Type 2 Diabetes Mellitus: Multiomics Integration and Interactomics Study.

Manoj Khokhar · Dipayan Roy · Sojit Tomo · Ashita Gadwal · Praveen Sharma · Purvi Purohit

JMIR bioinformatics and biotechnology · 2022 doi:10.2196/32437

Paper

complete

Experiment

partial

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Analysis

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title
Novel Molecular Networks and Regulatory MicroRNAs in Type 2 Diabetes Mellitus: Multiomics Integration and Interactomics Study.
doi
10.2196/32437
pmid
38935970
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Manoj Khokhar
[1]
Dipayan Roy
[2]
Sojit Tomo
[3]
Ashita Gadwal
[4]
Praveen Sharma
[5]
Purvi Purohit
journal
JMIR bioinformatics and biotechnology
year
2022
abstract
BACKGROUND: Type 2 diabetes mellitus (T2DM) is a metabolic disorder with severe comorbidities. A multiomics approach can facilitate the identification of novel therapeutic targets and biomarkers with proper validation of potential microRNA (miRNA) interactions. OBJECTIVE: The aim of this study was to identify significant differentially expressed common target genes in various tissues and their regulating miRNAs from publicly available Gene Expression Omnibus (GEO) data sets of patients with T2DM using in silico analysis. METHODS: Using differentially expressed genes (DEGs) identified from 5 publicly available T2DM data sets, we performed functional enrichment, coexpression, and network analyses to identify pathways, protein-protein interactions, and miRNA-mRNA interactions involved in T2DM. RESULTS: We extracted 2852, 8631, 5501, 3662, and 3753 DEGs from the expression profiles of GEO data sets GSE38642, GSE25724, GSE20966, GSE26887, and GSE23343, respectively. DEG analysis showed that 16 common genes were enriched in insulin secretion, endocrine resistance, and other T2DM-related pathways. Four DEGs, MAML3, EEF1D, NRG1, and CDK5RAP2, were important in the cluster network regulated by commonly targeted miRNAs (hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-124-3p, hsa-mir-1-3p), which are involved in the advanced glycation end products (AGE)-receptor for advanced glycation end products (RAGE) signaling pathway, culminating in diabetic complications and endocrine resistance. CONCLUSIONS: This study identified tissue-specific DEGs in T2DM, especially pertaining to the heart, liver, and pancreas. We identified a total of 16 common DEGs and the top four common targeting miRNAs (hsa-let-7b-5p, hsa-miR-124-3p, hsa-miR-1-3p, and has-miR-155-5p). The miRNAs identified are involved in regulating various pathways, including the phosphatidylinositol-3-kinase-protein kinase B, endocrine resistance, and AGE-RAGE signaling pathways.
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The DEGs were obtained from the five data sets using the online interactive tool GEO2R [16]. The cutoff for the selection was kept at the default of P<.05. The meta-analysis was based on the functional modules with the MetaDE R package. For this study, we used the “effect size” parameter estimation with a fixed-effects model and an adjusted P value threshold of .05.
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sections[5] "Identification and Assortment of Differentially Expressed mRNAs" paragraph 2
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The DEGs were obtained from the five data sets using the online interactive tool GEO2R [16].
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sections[5] "Identification and Assortment of Differentially Expressed mRNAs" paragraph 2
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The cutoff for the selection was kept at the default of P<.05.
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sections[8] "Integrative Gene Expression Meta-analysis" paragraph 2
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The meta-analysis was based on the functional modules with the MetaDE R package.
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sections[8] "Integrative Gene Expression Meta-analysis" paragraph 2
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For this study, we used the “effect size” parameter estimation with a fixed-effects model and an adjusted P value threshold of .05.
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We searched several keywords, including “type 2 diabetes mellitus,” “tissue,” “pancreas,” “liver,” “heart,” “expression profiling by array,” and “Homo sapiens,” in the GEO data sets, among which five were selected for this study: GSE38642 [9-11], GSE25724 [12], GSE20966 [13], GSE26887 [14], and GSE23343 [15] (Table 1).
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sections[3] "Data Collection" paragraph 2
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We searched several keywords, including “type 2 diabetes mellitus,” “tissue,” “pancreas,” “liver,” “heart,” “expression profiling by array,” and “Homo sapiens,” in the GEO data sets, among which five were selected for this study: GSE38642 [9-11], GSE25724 [12], GSE20966 [13], GSE26887 [14], and GSE23343 [15] (Table 1).
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We identified a total of 16 common DEGs and the top four common targeting miRNAs (hsa-let-7b-5p, hsa-miR-124-3p, hsa-miR-1-3p, and has-miR-155-5p).
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We identified a total of 16 common DEGs and the top four common targeting miRNAs (hsa-let-7b-5p, hsa-miR-124-3p, hsa-miR-1-3p, and has-miR-155-5p).
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We identified a total of 16 common DEGs and the top four common targeting miRNAs (hsa-let-7b-5p, hsa-miR-124-3p, hsa-miR-1-3p, and has-miR-155-5p).
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title
Type 2 Diabetes mellitus: mRNA and miRNA profiling
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title
Expression data from human pancreatic islets
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We searched several keywords, including “type 2 diabetes mellitus,” “tissue,” “pancreas,” “liver,” “heart,” “expression profiling by array,” and “Homo sapiens,” in the GEO data sets, among which five were selected for this study: GSE38642 [9-11], GSE25724 [12], GSE20966 [13], GSE26887 [14], and GSE23343 [15] (Table 1).
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We searched several keywords, including “type 2 diabetes mellitus,” “tissue,” “pancreas,” “liver,” “heart,” “expression profiling by array,” and “Homo sapiens,” in the GEO data sets, among which five were selected for this study: GSE38642 [9-11], GSE25724 [12], GSE20966 [13], GSE26887 [14], and GSE23343 [15] (Table 1).
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miRNA-27b-3p and miRNA-1228-3p correlate with the progression of Kidney Fibrosis in Diabetic Nephropathy
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2026-11-01T17:30:42Z
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The DEGs were obtained from the five data sets using the online interactive tool GEO2R [16]. The cutoff for the selection was kept at the default of P<.05. The meta-analysis was based on the functional modules with the MetaDE R package. For this study, we used the “effect size” parameter estimation with a fixed-effects model and an adjusted P value threshold of .05.
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We searched several keywords, including “type 2 diabetes mellitus,” “tissue,” “pancreas,” “liver,” “heart,” “expression profiling by array,” and “Homo sapiens,” in the GEO data sets, among which five were selected for this study: GSE38642 [9-11], GSE25724 [12], GSE20966 [13], GSE26887 [14], and GSE23343 [15] (Table 1).
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The paper names GSE38642, GSE25724, GSE20966, GSE26887, GSE23343, GSE51674, GSE26168 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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The DEGs were obtained from the five data sets using the online interactive tool GEO2R [16]. The cutoff for the selection was kept at the default of P<.05. The meta-analysis was based on the functional modules with the MetaDE R package. For this study, we used the “effect size” parameter estimation with a fixed-effects model and an adjusted P value threshold of .05.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38935970", "kind": "science-record", "revision": 3, "content_hash": "sha256:c7748b32212ff57b3014e23ef2c0dcb08748dd5d7d83e007fd0ff64775d6a4eb", "created_at": "2026-11-01T20:29:00Z", "updated_at": "2026-11-01T22:12:08Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Novel Molecular Networks and Regulatory MicroRNAs in Type 2 Diabetes Mellitus: Multiomics Integration and Interactomics Study.", "doi": "10.2196/32437", "pmid": "38935970", "authors": ["Manoj Khokhar", "Dipayan Roy", "Sojit Tomo", "Ashita Gadwal", "Praveen Sharma", "Purvi Purohit"], "journal": "JMIR bioinformatics and biotechnology", "year": 2022, "abstract": "BACKGROUND: Type 2 diabetes mellitus (T2DM) is a metabolic disorder with severe comorbidities. A multiomics approach can facilitate the identification of novel therapeutic targets and biomarkers with proper validation of potential microRNA (miRNA) interactions.\nOBJECTIVE: The aim of this study was to identify significant differentially expressed common target genes in various tissues and their regulating miRNAs from publicly available Gene Expression Omnibus (GEO) data sets of patients with T2DM using in silico analysis.\nMETHODS: Using differentially expressed genes (DEGs) identified from 5 publicly available T2DM data sets, we performed functional enrichment, coexpression, and network analyses to identify pathways, protein-protein interactions, and miRNA-mRNA interactions involved in T2DM.\nRESULTS: We extracted 2852, 8631, 5501, 3662, and 3753 DEGs from the expression profiles of GEO data sets GSE38642, GSE25724, GSE20966, GSE26887, and GSE23343, respectively. DEG analysis showed that 16 common genes were enriched in insulin secretion, endocrine resistance, and other T2DM-related pathways. Four DEGs, MAML3, EEF1D, NRG1, and CDK5RAP2, were important in the cluster network regulated by commonly targeted miRNAs (hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-124-3p, hsa-mir-1-3p), which are involved in the advanced glycation end products (AGE)-receptor for advanced glycation end products (RAGE) signaling pathway, culminating in diabetic complications and endocrine resistance.\nCONCLUSIONS: This study identified tissue-specific DEGs in T2DM, especially pertaining to the heart, liver, and pancreas. We identified a total of 16 common DEGs and the top four common targeting miRNAs (hsa-let-7b-5p, hsa-miR-124-3p, hsa-miR-1-3p, and has-miR-155-5p). The miRNAs identified are involved in regulating various pathways, including the phosphatidylinositol-3-kinase-protein kinase B, endocrine resistance, and AGE-RAGE signaling pathways.", "claims": [{"id": "claim-method", "statement": "The DEGs were obtained from the five data sets using the online interactive tool GEO2R [16]. The cutoff for the selection was kept at the default of P<.05. The meta-analysis was based on the functional modules with the MetaDE R package. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE20966"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:fixed_effects_pool", "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {"i_squared": "llowed by fixed-effects ImaGEO meta-analysis usin"}, "inputs": ["data:geo:GSE20966"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:fixed_effects_pool"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38935970", "role": "manuscript", "format": 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