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Identification of a diagnostic model and molecular subtypes of major depressive disorder based on endoplasmic
Shuwen Huang1,2, Yong Li1,2, Jianying Shen1,2
1Research Base of Chinese Medicine Syndrome, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China.
Subject:
Major depressive disorder (MDD) negatively affects patients' behaviours and daily lives. Due to the high heterogeneity and complex pathological features of MDD, its diagnosis remains challenging. Evidence suggests that endoplasmic reticulum stress (ERS) is involved in the pathogenesis of MDD; however, relevant diagnostic markers have not been well studied. This study aimed to screen for ERS genes with potential diagnostic value in MDD.
Methods:
Gene expression data on MDD samples were downloaded from the GEO database, and ERS-related genes were obtained from the GeneCards and MSigDB databases. Differentially expressed genes (DEGs) in MDD patients and healthy subjects were identified and then integrated with ERS genes. ERS diagnostic model and nomogram were developed based on biomarkers screened using the LASSO method. The diagnostic performance of this model was evaluated. ERS-associated subtypes were identified. CIBERSORT and GSEA were used to explore the differences between the different subtypes. Finally, WGCNA was performed to identify hub genes related to the subtypes.
Results:
A diagnostic model was developed based on seven ERS genes: KCNE1, PDIA4, STAU1, TMED4, MGST1, RCN1, and SHC1. The validation analysis showed that this model had a good diagnostic performance. KCNE1 expression was positively correlated with M0 macrophages and negatively correlated with resting CD4+ memory T cells. Two subtypes (SubA and SubB) were identified, and these two subtypes showed different ER score. The SubB group showed higher immune infiltration than the SubA group. Finally, NCF4, NCF2, CSF3R, and FPR2 were identified as hub genes associated with ERS molecular subtypes.
Conclusion:
Our current study provides novel diagnostic biomarkers for MDD from an ERS perspective, and these findings further facilitate the use of precision medicine in MDD.
Insights
This study identifies seven endoplasmic reticulum stress (ERS) genes as novel diagnostic biomarkers for major depressive disorder (MDD), paving the way for precision medicine approaches in MDD diagnosis and treatment.
Area of Science:
- Neuroscience
- Genetics
- Molecular Biology
Background:
- Major depressive disorder (MDD) presents diagnostic challenges due to its heterogeneity and complex pathology.
- Endoplasmic reticulum stress (ERS) is implicated in MDD pathogenesis, but diagnostic markers remain underexplored.
Purpose of the Study:
- To screen for endoplasmic reticulum stress (ERS) genes with potential diagnostic value in major depressive disorder (MDD).
- To develop and validate a diagnostic model for MDD based on ERS-related genes.
Main Methods:
- Downloaded gene expression data for MDD from the GEO database.
- Integrated ERS genes from GeneCards and MSigDB with differentially expressed genes (DEGs) in MDD.
- Developed and validated an ERS diagnostic model and nomogram using LASSO regression.
- Identified ERS-associated subtypes and analyzed immune infiltration using CIBERSORT and GSEA.
- Performed WGCNA to identify hub genes related to ERS molecular subtypes.
Main Results:
- A diagnostic model was developed using seven ERS genes (KCNE1, PDIA4, STAU1, TMED4, MGST1, RCN1, SHC1) with good diagnostic performance.
- KCNE1 expression showed correlations with M0 macrophages and resting CD4+ memory T cells.
- Two MDD subtypes (SubA and SubB) with distinct ER scores and immune infiltration levels were identified.
- NCF4, NCF2, CSF3R, and FPR2 were identified as hub genes associated with ERS molecular subtypes.
Conclusions:
- The study identified novel diagnostic biomarkers for MDD from an endoplasmic reticulum stress (ERS) perspective.
- These findings support the application of precision medicine strategies for MDD diagnosis and management.
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