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pmid:37449137

Transcriptome analysis identifies the role of Class I histone deacetylase in Alzheimer's disease.

Fan Geng · Na Zhao · Xiu Chen · XueTing Liu · MengMeng Zhu · Ying Jiang · QingGuo Ren

Heliyon · 2023 doi:10.1016/j.heliyon.2023.e18008

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Experiment

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pmid:37449137
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title
Transcriptome analysis identifies the role of Class I histone deacetylase in Alzheimer's disease.
doi
10.1016/j.heliyon.2023.e18008
pmid
37449137
authors array · 7 items
[0]
Fan Geng
[1]
Na Zhao
[2]
Xiu Chen
[3]
XueTing Liu
[4]
MengMeng Zhu
[5]
Ying Jiang
[6]
QingGuo Ren
journal
Heliyon
year
2023
abstract
Epigenetics modification is a process that does not change the sequence of deoxyribonucleic acid (DNA) in disease progression but can alter the genetic expression of the brain in Alzheimer's disease (AD). In this study, we deployed the weighted gene co-expression network analysis (WGCNA) to explore the role of Class I histone deacetylases (HDACs) in AD, which included HDAC1, HDAC2, HDAC3, and HDAC8. The aim of the study was to find how Class I HDACs affected AD pathology by analyzing the Gene Expression Omnibus (GEO) microarray datasets GSE33000. We found that HDAC1 and HDAC8 were more highly expressed in the cortex of AD patients than in Controls, while HDAC2 and HDAC3 were lower expressed. By WGCNA analysis, we found the blue module was associated with HDAC1 and HDAC8, and the turquoise module was related to HDAC2 and HDAC3. Functional enrichment analysis revealed that the Wnt signaling pathway and synaptic plasticity played an important role in the modification of HDAC1 and HDAC8 while gap junction and cell-cell junction were involved in the regulation of HDAC2 and HDAC3 in the disease progression of AD. By Receiver Operating Characteristics (ROC) analysis, we concluded that HDAC1 might be the most probable diagnostic biomarker of Class I HDACs for AD. Our study provided a comprehensive understanding of Class I HDACs and provided new insight into the function of HDAC1 in AD disease progression.
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The ROC analysis is a statistical method used to evaluate the performance of a binary classification model by plotting the true positive rate against the false positive rate at different classification thresholds.
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sections[7] "ROC curve analysis" paragraph 2
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The ROC analysis is a statistical method used to evaluate the performance of a binary classification model by plotting the true positive rate against the false positive rate at different classification thresholds.
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The data that support the findings of this study are openly available in GEO datasets of NCBI at [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33000], reference number [10].
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sections[19] "Data availability" paragraph 2
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The data that support the findings of this study are openly available in GEO datasets of NCBI at [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33000], reference number [10].
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HDAC1 had the highest ROC AUC among the four Class I HDACs and was identified as a potential diagnostic biomarker for Alzheimer’s disease.
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sections[16] "Verifying the diagnostic value of Class I histone deacetylase" paragraph 2
quote
The area under the curve (AUC) of HDAC1, HDAC2, HDAC3 and HDAC8 were 0.839, 0.649, 0.720 and 0.741 between AD patients VS. non-demented control, indicating that HDAC1 might be a prospective biomarker and diagnostic value for AD.
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HDAC1 had the highest ROC AUC among the four Class I HDACs and was identified as a potential diagnostic biomarker for Alzheimer’s disease.
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The data that support the findings of this study are openly available in GEO datasets of NCBI at [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33000], reference number [10].
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Gene expression profiles of human prefrontal cortex brain tissues
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37449137", "kind": "science-record", "revision": 3, "content_hash": "sha256:e4d0dccf33921ad8e7424c6fadd461ee6bc4a11d069d19fa814fd2d169866277", "created_at": "2026-11-01T19:02:13Z", "updated_at": "2026-11-01T22:09:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Transcriptome analysis identifies the role of Class I histone deacetylase in Alzheimer's disease.", "doi": "10.1016/j.heliyon.2023.e18008", "pmid": "37449137", "authors": ["Fan Geng", "Na Zhao", "Xiu Chen", "XueTing Liu", "MengMeng Zhu", "Ying Jiang", "QingGuo Ren"], "journal": "Heliyon", "year": 2023, "abstract": "Epigenetics modification is a process that does not change the sequence of deoxyribonucleic acid (DNA) in disease progression but can alter the genetic expression of the brain in Alzheimer's disease (AD). In this study, we deployed the weighted gene co-expression network analysis (WGCNA) to explore the role of Class I histone deacetylases (HDACs) in AD, which included HDAC1, HDAC2, HDAC3, and HDAC8. The aim of the study was to find how Class I HDACs affected AD pathology by analyzing the Gene Expression Omnibus (GEO) microarray datasets GSE33000. We found that HDAC1 and HDAC8 were more highly expressed in the cortex of AD patients than in Controls, while HDAC2 and HDAC3 were lower expressed. By WGCNA analysis, we found the blue module was associated with HDAC1 and HDAC8, and the turquoise module was related to HDAC2 and HDAC3. Functional enrichment analysis revealed that the Wnt signaling pathway and synaptic plasticity played an important role in the modification of HDAC1 and HDAC8 while gap junction and cell-cell junction were involved in the regulation of HDAC2 and HDAC3 in the disease progression of AD. By Receiver Operating Characteristics (ROC) analysis, we concluded that HDAC1 might be the most probable diagnostic biomarker of Class I HDACs for AD. Our study provided a comprehensive understanding of Class I HDACs and provided new insight into the function of HDAC1 in AD disease progression.", "claims": [{"id": "claim-method", "statement": "The ROC analysis is a statistical method used to evaluate the performance of a binary classification model by plotting the true positive rate against the false positive rate at different classification thresholds.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37449137", "locator": "sections[7] \"ROC curve analysis\" paragraph 2", "quote": "The ROC analysis is a statistical method used to evaluate the performance of a binary classification model by plotting the true positive rate against the false positive rate at different classification thresholds."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of this study are openly available in GEO datasets of NCBI at [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33000], reference number [10].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37449137", "locator": "sections[19] \"Data availability\" paragraph 2", "quote": "The data that support the findings of this study are openly available in GEO datasets of NCBI at [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33000], reference number [10]."}], "state": "complete"}, {"id": "claim-target-result", "statement": "HDAC1 had the highest ROC AUC among the four Class I HDACs and was identified as a potential diagnostic biomarker for Alzheimer’s disease.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37449137", "locator": "sections[16] \"Verifying the diagnostic value of Class I histone deacetylase\" paragraph 2", "quote": "The area under the curve (AUC) of HDAC1, HDAC2, HDAC3 and HDAC8 were 0.839, 0.649, 0.720 and 0.741 between AD patients VS. non-demented control, indicating that HDAC1 might be a prospective biomarker and diagnostic value for AD."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "HDAC1 had the highest ROC AUC among the four Class I HDACs and was identified as a potential diagnostic biomarker for Alzheimer’s disease.", "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:37449137"], "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. 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