The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches
BackgroundSome 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 depressio...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1479616/full |
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| author | Lu Zhang Lu Zhang YuJing Lv Mengqing Ma Jile Lv Jie Chen Shang Lei Yi Man Guimei Xing Yu Wang |
| author_facet | Lu Zhang Lu Zhang YuJing Lv Mengqing Ma Jile Lv Jie Chen Shang Lei Yi Man Guimei Xing Yu Wang |
| author_sort | Lu Zhang |
| collection | DOAJ |
| description | BackgroundSome 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.MethodsGSE76826 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).ResultsThe 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.ConclusionThis study identified three hub genes (JDP2, ALOX5, and KPNB1) associated with histone acetylation, offering new insight into the diagnosis and treatment of DD. |
| format | Article |
| id | doaj-art-d7953804764148679f5c9dfcad7c46ea |
| institution | OA Journals |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-04-01 |
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| series | Frontiers in Neuroscience |
| spelling | doaj-art-d7953804764148679f5c9dfcad7c46ea2025-08-20T02:13:22ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-04-011910.3389/fnins.2025.14796161479616The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approachesLu Zhang0Lu Zhang1YuJing Lv2Mengqing Ma3Jile Lv4Jie Chen5Shang Lei6Yi Man7Guimei Xing8Yu Wang9Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Neurology, Anhui No. 2 Provincial People’s Hospital, Hefei, ChinaGraduate School, Bengbu Medical University, Bengbu, ChinaGraduate School, Bengbu Medical University, Bengbu, ChinaGraduate School, Bengbu Medical University, Bengbu, ChinaDepartment of Psychiatry, Affiliated Psychological Hospital of Anhui Medical University, Hefei, ChinaGraduate School, Bengbu Medical University, Bengbu, ChinaDepartment of Oncology, Anhui Jimin Cancer Hospital, Hefei, ChinaDepartment of Education, Anhui No. 2 Provincial People’s Hospital, Hefei, ChinaDepartment of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, ChinaBackgroundSome 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.MethodsGSE76826 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).ResultsThe 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.ConclusionThis study identified three hub genes (JDP2, ALOX5, and KPNB1) associated with histone acetylation, offering new insight into the diagnosis and treatment of DD.https://www.frontiersin.org/articles/10.3389/fnins.2025.1479616/fulldepression disorderhistone acetylationhub genesimmune infiltrationbioinformatics |
| spellingShingle | Lu Zhang Lu Zhang YuJing Lv Mengqing Ma Jile Lv Jie Chen Shang Lei Yi Man Guimei Xing Yu Wang The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches Frontiers in Neuroscience depression disorder histone acetylation hub genes immune infiltration bioinformatics |
| title | The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches |
| title_full | The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches |
| title_fullStr | The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches |
| title_full_unstemmed | The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches |
| title_short | The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches |
| title_sort | identification and validation of histone acetylation related biomarkers in depression disorder based on bioinformatics and machine learning approaches |
| topic | depression disorder histone acetylation hub genes immune infiltration bioinformatics |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2025.1479616/full |
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