Identification of a diagnostic model and molecular subtypes of major depressive disorder based on endoplasmic reticulum stress-related genes

Shuwen Huang; Yong Li; Jianying Shen; Wenna Liang; Candong Li

doi:10.3389/fpsyt.2023.1168516

Identification of a diagnostic model and molecular subtypes of major depressive disorder based on endoplasmic reticulum stress-related genes

Front Psychiatry. 2023 Aug 15:14:1168516. doi: 10.3389/fpsyt.2023.1168516. eCollection 2023.

Authors

Shuwen Huang^{1

2}, Yong Li^{1

2}, Jianying Shen^{1

2}, Wenna Liang^{1

2}, Candong Li^{1

2

3}

Affiliations

¹ Research Base of Chinese Medicine Syndrome, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China.
² FuJian Key Laboratory of TCM Health State, Fuzhou, Fujian, China.
³ LI Candong Qihuang Scholar Studio, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China.

Abstract

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.

Keywords: bioinformatics; diagnostic model; endoplasmic reticulum stress; major depressive disorder; molecular subtype.