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pmid:40370665

The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches.

Lu Zhang · YuJing Lv · Mengqing Ma · Jile Lv · Jie Chen · Shang Lei · Yi Man · Guimei Xing · Yu Wang

Frontiers in neuroscience · 2025 doi:10.3389/fnins.2025.1479616

Paper

complete

Experiment

partial

Data

partial

Analysis

partial

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:40370665
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science-record
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2026-11-01T20:30:31Z
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paper object · 10 fields
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title
The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches.
doi
10.3389/fnins.2025.1479616
pmid
40370665
authors array · 9 items
[0]
Lu Zhang
[1]
YuJing Lv
[2]
Mengqing Ma
[3]
Jile Lv
[4]
Jie Chen
[5]
Shang Lei
[6]
Yi Man
[7]
Guimei Xing
[8]
Yu Wang
journal
Frontiers in neuroscience
year
2025
abstract
Background: Some studies indicated that histone modification may be involved in depression disorder (DD). The maintenance of the histone acetylation state is the work of histone acetyltransferase (HAT) and histone deacetylase (HDAC), which is thought to be a potential diagnostic biomarker of depression. However, it is still unknown how histone acetylation-related genes (HAC-RGs) contribute to the onset and progression of DD. Methods: GSE76826 and GSE98793were obtained from the Gene Expression Omnibus (GEO) database, HAC-RGs were acquired from the GeneCards database. Initially, the differentially expressed genes (DEGs) in GSE76826 were investigated. We used weighted gene co-expression network analysis (WGCNA) to screen key module genes. Candidate genes were selected by intersecting DEGs, key module genes, and HAC-RGs, followed by functional analysis. Two machine learning algorithms were used to identify hub genes, which were used for drug prediction, immunological infiltration studies, nomogram construction, and regulatory network building. The expression levels were verified using the GSE76826 and GSE98793 datasets. Hub gene expression levels in the clinical samples were verified using reverse transcription quantitative PCR (RT-qPCR). Results: The 23 candidate genes were obtained by intersecting 2,316 DEGs, 1,010 HAC-RGs and 2,617 key module genes. Three hub genes (JDP2, ALOX5, and KPNB1) were gained by two machine learning algorithms. The nomogram constructed based on these three hub genes showed high predictive accuracy. Additionally, the three hub genes were enriched in the kegg_ribosome. The 9 different immune cells were identified in GSE76826, which were associated with three hub genes. A hub gene-drug network (98 nodes, 106 edges) and an lncRNA-miRNA-mRNA network (56 nodes, 87 edges), were built using the database. The expression level verification indicated that, with the exception of the KPNB1 gene, the DD group had higher levels of JDP2 and ALOX5 and that the expression patterns in GSE76826 and GSE98793 were consistent, with RT-qPCR confirming higher ALOX5 and JDP2 expression in DD samples. Conclusion: This study identified three hub genes (JDP2, ALOX5, and KPNB1) associated with histone acetylation, offering new insight into the diagnosis and treatment of DD.
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The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
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This study identified three hub genes (JDP2, ALOX5, and KPNB1) associated with histone acetylation, offering new insight into the diagnosis and treatment of DD.
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This study identified three hub genes (JDP2, ALOX5, and KPNB1) associated with histone acetylation, offering new insight into the diagnosis and treatment of DD.
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This study identified three hub genes (JDP2, ALOX5, and KPNB1) associated with histone acetylation, offering new insight into the diagnosis and treatment of DD.
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sing reverse transcription quantitative PCR (RT-qPCR). Results: The 23 candidate genes were obtai
[1]
g reverse transcription quantitative PCR (RT-qPCR). Results: The 23 candidate genes were obtai
[2]
The reverse transcription quantitative PCR (RT-qPCR) results showed that the relative expression
[3]
into cDNA. The 2x Universal Blue SYBR Green qPCR Master Mix was used for qPCR analysis. The q
order_basis
paper_text_order:1356
id
assay:1
[1] object · 11 fields
role
analysis
definition_id
group_comparison
definition_version
f2132e96ae6c7a4e
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
Wilcoxon
order_basis
consumes_earlier_output
id
assay:2
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40370665", "kind": "science-record", "revision": 3, "content_hash": "sha256:38224772a9662ee115ca8dcbaf301d7fec0b77dae2a46b6bccce427731be983c", "created_at": "2026-11-01T20:30:31Z", "updated_at": "2026-11-01T22:14:04Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches.", "doi": "10.3389/fnins.2025.1479616", "pmid": "40370665", "authors": ["Lu Zhang", "YuJing Lv", "Mengqing Ma", "Jile Lv", "Jie Chen", "Shang Lei", "Yi Man", "Guimei Xing", "Yu Wang"], "journal": "Frontiers in neuroscience", "year": 2025, "abstract": "Background: Some studies indicated that histone modification may be involved in depression disorder (DD). The maintenance of the histone acetylation state is the work of histone acetyltransferase (HAT) and histone deacetylase (HDAC), which is thought to be a potential diagnostic biomarker of depression. However, it is still unknown how histone acetylation-related genes (HAC-RGs) contribute to the onset and progression of DD.\nMethods: GSE76826 and GSE98793were obtained from the Gene Expression Omnibus (GEO) database, HAC-RGs were acquired from the GeneCards database. Initially, the differentially expressed genes (DEGs) in GSE76826 were investigated. We used weighted gene co-expression network analysis (WGCNA) to screen key module genes. Candidate genes were selected by intersecting DEGs, key module genes, and HAC-RGs, followed by functional analysis. Two machine learning algorithms were used to identify hub genes, which were used for drug prediction, immunological infiltration studies, nomogram construction, and regulatory network building. The expression levels were verified using the GSE76826 and GSE98793 datasets. Hub gene expression levels in the clinical samples were verified using reverse transcription quantitative PCR (RT-qPCR).\nResults: The 23 candidate genes were obtained by intersecting 2,316 DEGs, 1,010 HAC-RGs and 2,617 key module genes. Three hub genes (JDP2, ALOX5, and KPNB1) were gained by two machine learning algorithms. The nomogram constructed based on these three hub genes showed high predictive accuracy. Additionally, the three hub genes were enriched in the kegg_ribosome. The 9 different immune cells were identified in GSE76826, which were associated with three hub genes. A hub gene-drug network (98 nodes, 106 edges) and an lncRNA-miRNA-mRNA network (56 nodes, 87 edges), were built using the database. The expression level verification indicated that, with the exception of the KPNB1 gene, the DD group had higher levels of JDP2 and ALOX5 and that the expression patterns in GSE76826 and GSE98793 were consistent, with RT-qPCR confirming higher ALOX5 and JDP2 expression in DD samples.\nConclusion: This study identified three hub genes (JDP2, ALOX5, and KPNB1) associated with histone acetylation, offering new insight into the diagnosis and treatment of DD.", "claims": [{"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40370665", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "This study identified three hub genes (JDP2, ALOX5, and KPNB1) associated with histone acetylation, offering new insight into the diagnosis and treatment of DD.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40370665", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "This study identified three hub genes (JDP2, ALOX5, and KPNB1) associated with histone acetylation, offering new insight into the diagnosis and treatment of DD."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "This study identified three hub genes (JDP2, ALOX5, and KPNB1) associated with histone acetylation, offering new insight into the diagnosis and treatment of DD.", "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:40370665"], "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:GSE76826"], "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:group_comparison_wilcoxon", "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:GSE76826"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40370665", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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included in the article/supplementary material, further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2038455", "GSM2038456", "GSM2038457", "GSM2038458", "GSM2038459", "GSM2038460", "GSM2038461", "GSM2038462", "GSM2038463", "GSM2038464", "GSM2038465", "GSM2038466", "GSM2038467", "GSM2038468", "GSM2038469", "GSM2038470", "GSM2038471", "GSM2038472", "GSM2038473", "GSM2038474", "GSM2038475", "GSM2038476", "GSM2038477", "GSM2038478", "GSM2038479", "GSM2038480", "GSM2038481", "GSM2038482", "GSM2038483", "GSM2038484", "GSM2038485", "GSM2038486"], "sample_count": 32, "group_sizes": null, "group_sizes_basis": null, "platform": "17077", "taxon": "Homo sapiens", "title": "Blood transcriptomic markers in patients with late-onset major depressive disorder", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE98793", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE98793", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE98793", "version": null, "note": "The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2612096", "GSM2612097", "GSM2612098", "GSM2612099", "GSM2612100", "GSM2612101", "GSM2612102", "GSM2612103", "GSM2612104", "GSM2612105", "GSM2612106", "GSM2612107", "GSM2612108", "GSM2612109", "GSM2612110", "GSM2612111", "GSM2612112", "GSM2612113", "GSM2612114", "GSM2612115", "GSM2612116", "GSM2612117", "GSM2612118", "GSM2612119", "GSM2612120", "GSM2612121", "GSM2612122", "GSM2612123", "GSM2612124", "GSM2612125", "GSM2612126", "GSM2612127", "GSM2612128", "GSM2612129", "GSM2612130", "GSM2612131", "GSM2612132", "GSM2612133", "GSM2612134", "GSM2612135", "GSM2612136", "GSM2612137", "GSM2612138", "GSM2612139", "GSM2612140", "GSM2612141", "GSM2612142", "GSM2612143", "GSM2612144", "GSM2612145", "GSM2612146", "GSM2612147", "GSM2612148", "GSM2612149", "GSM2612150", "GSM2612151", "GSM2612152", "GSM2612153", "GSM2612154", 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"GSM2612226", "GSM2612227", "GSM2612228", "GSM2612229", "GSM2612230", "GSM2612231", "GSM2612232", "GSM2612233", "GSM2612234", "GSM2612235", "GSM2612236", "GSM2612237", "GSM2612238", "GSM2612239", "GSM2612240", "GSM2612241", "GSM2612242", "GSM2612243", "GSM2612244", "GSM2612245", "GSM2612246", "GSM2612247", "GSM2612248", "GSM2612249", "GSM2612250", "GSM2612251", "GSM2612252", "GSM2612253", "GSM2612254", "GSM2612255", "GSM2612256", "GSM2612257", "GSM2612258", "GSM2612259", "GSM2612260", "GSM2612261", "GSM2612262", "GSM2612263", "GSM2612264", "GSM2612265", "GSM2612266", "GSM2612267", "GSM2612268", "GSM2612269", "GSM2612270", "GSM2612271", "GSM2612272", "GSM2612273", "GSM2612274", "GSM2612275", "GSM2612276", "GSM2612277", "GSM2612278", "GSM2612279", "GSM2612280", "GSM2612281", "GSM2612282", "GSM2612283", "GSM2612284", "GSM2612285", "GSM2612286", "GSM2612287"], "sample_count": 192, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Gene 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Results: The 23 candidate genes were obtai", "g reverse transcription quantitative PCR (RT-qPCR). Results: The 23 candidate genes were obtai", "The reverse transcription quantitative PCR (RT-qPCR) results showed that the relative expression", "into cDNA. The 2x Universal Blue SYBR Green qPCR Master Mix was used for qPCR analysis. The q"], "order_basis": "paper_text_order:1356", "id": "assay:1"}, {"role": "analysis", "definition_id": "group_comparison", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["Wilcoxon"], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}