ArticleBMC immunology2022
A novel 4 immune-related genes as diagnostic markers and correlated with immune infiltrates in major depressive disorder.
Article in BMC immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 12 citations in OpenAlex.
- Article
- The expressions and mFrontiers in genetics · 2026Article
- The role of senescence-related genes in major depressive disorder: insights from machine learning and single cell analysis.BMC psychiatry · 2025Article
- Understanding the Biological Relationship between Migraine and Depression.Biomolecules · 2024Review
- The Association of HHV-6 and theViruses · 2023Article
- Identification of mitophagy-related biomarkers and immune infiltration in major depressive disorder.BMC genomics · 2023Article
- Identification of novel biomarkers linking depressive disorder and Alzheimer's disease based on an integrative bioinformatics analysis.BMC genomic data · 2023Article
- Gene Signatures Associated with Temporal Rhythm as Diagnostic Markers of Major Depressive Disorder and Their Role in Immune Infiltration.International journal of molecular sciences · 2022Article
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10 authors at 1 institution in 1 country.
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Abstract
backgroundImmune response is prevalently related with major depressive disorder (MDD) pathophysiology. However, the study on the relationship between immune-related genes (IRGs) and immune infiltrates of MDD remains scarce.
methodsWe extracted expression data of 148 MDD patients from 2 cohorts, and systematically characterized differentially expressed IRGs by using limma package in R software. Then, the LASSO and multivariate logistic regression analysis was used to identify the most powerful IRGs. Next, we analyzed the relationship between IRGs and immune infiltrates of MDD. Finally, GSE76826 was used to to verificate of IRGs as a diagnostic markers in MDD.
results203 different IRGs s in MDD has been identified (P < 0.05). GSEA revealed that the different IRGs was more likely to be enriched in immune-specific pathways. Then, a 9 IRGs was successfully established to predict MDD based on LASSO. Next, 4 IRGs was obtained by multivariate logistic regression analysis, and AUC for CD1C, SPP1, CD3D, CAMKK2, and IRGs model was 0.733, 0.767, 0.816, 0.800, and 0.861, suggesting that they have a good diagnostic performance. Furthermore, the proportion of T cells CD8, T cells γδ, macrophages M0, and NK cells resting in MDD group was lower than that in the healthy controls, suggesting that the immune system in MDD group is impaired. Simultaneously, CD3D was validated a reliable marker in MDD, and was positively correlated with T cells CD8. GSEA revealed high expression CD3D was more likely to be enriched in immune-specific pathways, and low expression CD3D was more likely to be enriched in glucose metabolism metabolism-specific pathways.
conclusionsWe applied bioinformatics approaches to suggest that a 4 IRGs could serve as diagnostic markers to provide a novel direction to explore the pathogenesis of MDD.
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