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pmid:31797865

Differently Expressed Genes (DEGs) Relevant to Type 2 Diabetes Mellitus Identification and Pathway Analysis via Integrated Bioinformatics Analysis.

Xuanqiang Che · Ran Zhao · Hua Xu · Xue Liu · Shumiao Zhao · Hongwei Ma

Medical science monitor : international medical journal of experimental and clinical research · 2019 doi:10.12659/MSM.918407

Paper

complete

Experiment

complete

Data

partial

Analysis

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Execution

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
id
pmid:31797865
kind
science-record
content_hash
sha256:0841b23e2947dc2c9062d117d0221567c8b6a775d5243c5d649b22b38d915d42
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https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
created_at
2026-11-01T15:40:52Z
updated_at
2026-11-01T22:01:03Z
generator object · 2 fields
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corpus-assay-engine
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paper object · 10 fields
state
complete
title
Differently Expressed Genes (DEGs) Relevant to Type 2 Diabetes Mellitus Identification and Pathway Analysis via Integrated Bioinformatics Analysis.
doi
10.12659/MSM.918407
pmid
31797865
authors array · 6 items
[0]
Xuanqiang Che
[1]
Ran Zhao
[2]
Hua Xu
[3]
Xue Liu
[4]
Shumiao Zhao
[5]
Hongwei Ma
journal
Medical science monitor : international medical journal of experimental and clinical research
year
2019
abstract
BACKGROUND The aim of this study was to evaluate the differently expressed genes (DEGs) relevant to type 2 diabetes mellitus (T2DM) and pathway by performing integrated bioinformatics analysis. MATERIAL AND METHODS The gene expression datasets GSE7014 and GSE29221 were downloaded in GEO database, and DEGs from type 2 diabetes mellitus and normal skeletal muscle tissues were identified. Biological function analysis of the DEGs was enriched by GO and KEEG pathway. A PPI network for the identified DEGs was built using the STRING database. RESULTS Thirty top DEGs were identified from 2 datasets: GSE7014 and GSE29221. Of the 30 top DEGs, 20 were up-regulated and 10 were down-regulated. The 20 up-regulated genes were enriched in regulation of mRNA, protein biding, and phospholipase D signaling pathway. The 10 down-regulated genes were enriched in telomere maintenance via semi-conservative replication, AGE-RAGE signaling pathway in diabetic complications, and insulin resistance pathway. In the PPI network of 20 up-regulated DEGs, there were 40 nodes and 84 edges, with an average node degree of 4.2. For the 10 down-regulated DEGs, we found a total of 30 nodes and 105 edges, with an average node degree of 7.0 and local clustering coefficient of 0.812. Among the 30 DEGs, 10 hub genes (CNOT6L, CNOT6, CNOT1, CNOT7, RQCD1, RFC2, PRIM1, RFC4, RFC5, and RFC1) were also identified through Cytoscape. CONCLUSIONS DEGs of T2DM may play an essential role in disease development and may be potential pathogeneses of T2DM.
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For the GSE7014 dataset, gene expression of a total of 26 skeletal muscle samples, including 20 samples from T2DM patients and 6 healthy subjects were analyzed using the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.
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sections[3] "DEGs identification form datasets GSE7014 and GSE29221" paragraph 2
quote
For the GSE7014 dataset, gene expression of a total of 26 skeletal muscle samples, including 20 samples from T2DM patients and 6 healthy subjects were analyzed using the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.
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The claim, method and evidence span published with the source article.
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the published estimate and its evidence
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For the GSE7014 dataset, gene expression of a total of 26 skeletal muscle samples, including 20 samples from T2DM patients and 6 healthy subjects were analyzed using the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.
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For the GSE7014 dataset, gene expression of a total of 26 skeletal muscle samples, including 20 samples from T2DM patients and 6 healthy subjects were analyzed using the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.
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Global Gene Expression Profiles of Skeletal Muscle in Males with Type 2 Diabetes.
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For the GSE7014 dataset, gene expression of a total of 26 skeletal muscle samples, including 20 samples from T2DM patients and 6 healthy subjects were analyzed using the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.
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Expression data from DM1, DM2 and Normal Adult Skeletal Muscle Biopsies
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DEGs identification
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Table 1
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Twenty up-regulated genes identified through datasets GSE7014 and GSE29221.
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[1]
CCR4-NOT transcription complex subunit 7
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3.771885
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3.63E-03
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Insulin-like growth factor binding protein-like 1
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3.767067
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2.50E-05
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Microtubule-associated protein tau
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3.557061
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2.33E-04
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[1]
Hypoxia inducible lipid droplet-associated
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3.50515
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8.78E-04
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Tankyrase 2
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3.383897
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9.31E-04
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3.046473
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6.37E-03
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Cytosolic thiouridylase subunit 2
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2.931942
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7.95E-03
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Ankyrin repeat and KH domain containing 1
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3.67E-03
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ADP ribosylation factor 6
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trinucleotide repeat containing 6B
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4.94E-05
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2.645071
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1.04E-03
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NEDD4 binding protein 2-like 1
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2.604427
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4.39E-03
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Metastasis associated lung adenocarcinoma transcript 1 (non-protein coding)
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2.535709
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7.52E-03
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Hyaluronoglucosaminidase 2
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3.36E-03
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Uveal autoantigen with coiled-coil domains and ankyrin repeats
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2.436325
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8.59E-03
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2.369664
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7.11E-03
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[1]
TEA domain transcription factor 3
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2.292119
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1.74E-03
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[1]
cAMP responsive element binding protein 3
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8.84E-04
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Ten down-regulated genes identified through datasets GSE7014 and GSE29221.
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DEGs identification
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Table 2
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Ten down-regulated genes identified through datasets GSE7014 and GSE29221.
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4.88E-03
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WNK lysine deficient protein kinase 1
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6.86E-03
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Suppressor of cancer cell invasion
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−3.95926
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5.49E-03
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Nuclear receptor subfamily 4 group A member 2
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1.55E-03
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Automated Assays Laravel application and simulated adapters
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For the GSE7014 dataset, gene expression of a total of 26 skeletal muscle samples, including 20 samples from T2DM patients and 6 healthy subjects were analyzed using the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.
basis
The paper names GSE7014, GSE29221 in GEO database and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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lineage object · 4 fields
parent_id
pmid:31797865
parent_hash
sha256:f31439d3b5f1affb52ac18b19bcc85a940939c406a12da3736e9ae4db5b0c617
fork_of
null
gaps array · 8 items
[0]
asset:data:geo:GSE29221 licence not read
[1]
asset:data:geo:GSE29221 not downloaded
[2]
asset:data:geo:GSE7014 licence not read
[3]
asset:data:geo:GSE7014 not downloaded
[4]
execution.not_run
[5]
review.human_critique
[6]
review.independent_reader
[7]
review.machine_critique
assays array · 1 item
[0] object · 11 fields
role
analysis
definition_id
network_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 3 items
[0]
build_graph
[1]
centrality_scores
[2]
pathway_enrich
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
PPI
order_basis
analysis_only
id
assay:1
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31797865", "kind": "science-record", "revision": 8, "content_hash": "sha256:0841b23e2947dc2c9062d117d0221567c8b6a775d5243c5d649b22b38d915d42", "created_at": "2026-11-01T15:40:52Z", "updated_at": "2026-11-01T22:01:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Differently Expressed Genes (DEGs) Relevant to Type 2 Diabetes Mellitus Identification and Pathway Analysis via Integrated Bioinformatics Analysis.", "doi": "10.12659/MSM.918407", "pmid": "31797865", "authors": ["Xuanqiang Che", "Ran Zhao", "Hua Xu", "Xue Liu", "Shumiao Zhao", "Hongwei Ma"], "journal": "Medical science monitor : international medical journal of experimental and clinical research", "year": 2019, "abstract": "BACKGROUND The aim of this study was to evaluate the differently expressed genes (DEGs) relevant to type 2 diabetes mellitus (T2DM) and pathway by performing integrated bioinformatics analysis. MATERIAL AND METHODS The gene expression datasets GSE7014 and GSE29221 were downloaded in GEO database, and DEGs from type 2 diabetes mellitus and normal skeletal muscle tissues were identified. Biological function analysis of the DEGs was enriched by GO and KEEG pathway. A PPI network for the identified DEGs was built using the STRING database. RESULTS Thirty top DEGs were identified from 2 datasets: GSE7014 and GSE29221. Of the 30 top DEGs, 20 were up-regulated and 10 were down-regulated. The 20 up-regulated genes were enriched in regulation of mRNA, protein biding, and phospholipase D signaling pathway. The 10 down-regulated genes were enriched in telomere maintenance via semi-conservative replication, AGE-RAGE signaling pathway in diabetic complications, and insulin resistance pathway. In the PPI network of 20 up-regulated DEGs, there were 40 nodes and 84 edges, with an average node degree of 4.2. For the 10 down-regulated DEGs, we found a total of 30 nodes and 105 edges, with an average node degree of 7.0 and local clustering coefficient of 0.812. Among the 30 DEGs, 10 hub genes (CNOT6L, CNOT6, CNOT1, CNOT7, RQCD1, RFC2, PRIM1, RFC4, RFC5, and RFC1) were also identified through Cytoscape. CONCLUSIONS DEGs of T2DM may play an essential role in disease development and may be potential pathogeneses of T2DM.", "claims": [{"id": "claim-data-access", "statement": "For the GSE7014 dataset, gene expression of a total of 26 skeletal muscle samples, including 20 samples from T2DM patients and 6 healthy subjects were analyzed using the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31797865", "locator": "sections[3] \"DEGs identification form datasets GSE7014 and GSE29221\" paragraph 2", "quote": "For the GSE7014 dataset, gene expression of a total of 26 skeletal muscle samples, including 20 samples from T2DM patients and 6 healthy subjects were analyzed using the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:31797865"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE29221"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE29221"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:31797865", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.12659/MSM.918407", "sha256": 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