ArticleBMC psychiatry2024
An association study of m6A methylation with major depressive disorder.
Article in BMC psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The expressions and mFrontiers in genetics · 2026Article
- Rewriting the brain: m6A RNA methylation as an emerging epitranscriptomic regulator in major depressive disorder.Frontiers in molecular neuroscience · 2026Review
- Novel screening tool for psychiatric samples with major depressive disorder: a two-centre study.BMJ mental health · 2025Article
- Decoding the role of mNeurochemistry international · 2025Review
- MNeuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025Article
- Epitranscriptomic shifts in M6A RNA methylation influencing transcriptional dynamics in the prefrontal cortex of chronic restraint stress rats.Neurochemistry international · 2025Article
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Authors and funding
9 authors.
Funding
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Abstract
objectiveTo find the relationship between N6-methyladenosine (m6A) genes and Major Depressive Disorder (MDD).
methodsDifferential expression of m6A associated genes between normal and MDD samples was initially identified. Subsequent analysis was conducted on the functions of these genes and the pathways they may affect. A diagnostic model was constructed using the expression matrix of these differential genes, and visualized using a nomogram. Simultaneously, an unsupervised classification method was employed to classify all patients based on the expression of these m6A associated genes. Following this, common differential genes among different clusters were computed. By analyzing the functions of the common differential expressed genes among clusters, the role of m6A-related genes in the pathogenesis of MDD patients was elucidated.
resultsDifferential expression was observed in ELAVL1 and YTHDC2 between the MDD group and the control group. ELAVL1 was associated with comorbid anxiety in MDD patients. A linear regression model based on these two genes could accurately predict whether patients in the GSE98793 dataset had MDD and could provide a net benefit for clinical decision-making. Based on the expression matrix of ELAVL1 and YTHDC2, MDD patients were classified into three clusters. Among these clusters, there were 937 common differential genes. Enrichment analysis was also performed on these genes. The ssGSEA method was applied to predict the content of 23 immune cells in the GSE98793 dataset samples. The relationship between these immune cells and ELAVL1, YTHDC2, and different clusters was analyzed.
conclusionAmong all the m6A genes, ELAVL1 and YTHDC2 are closely associated with MDD, ELAVL1 is related to comorbid anxiety in MDD. ELAVL1 and YTHDC2 have opposite associations with immune cells in MDD.
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