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. 2023 Apr 15;24(1):22.
doi: 10.1186/s12863-023-01120-x.

Identification of novel biomarkers linking depressive disorder and Alzheimer's disease based on an integrative bioinformatics analysis

Jin Song #  1   2 , Zilong Ma #  3   4 , Huishi Zhang  5   6   7 , Ting Liang  8   9 , Jun Zhang  10   11
Affiliations

Identification of novel biomarkers linking depressive disorder and Alzheimer's disease based on an integrative bioinformatics analysis

Jin Song et al. BMC Genom Data. .

Abstract

Background: Previous reports revealed that a history of major depressive disorder (MDD) increased the risk of Alzheimer's disease (AD). The immune disorder is associated with MDD and AD pathophysiology. We aimed to identify differentially expressed immune-related genes (DEIRGs) that are involved in the pathogenesis of MDD and AD.

Methods: We downloaded mRNA expression profiles (GSE76826 and GSE5281) from the Gene Expression Omnibus (GEO) database. The R software was used to identify DEIRGs for the two diseases separately. Functional enrichment analysis and PPI network of DEIRGs were performed. Finally, the relationship between shared DEIRGs and immune infiltrates of AD and MDD were analyzed, respectively.

Results: A total of 121 DEIRGs linking AD and MDD were identified. These genes were significantly enriched in immune-related pathways, such as the JAK-STAT signaling pathway, regulation of chemotaxis, chemotaxis, cytokine-cytokine receptor interaction, and primary immunodeficiency. Furthermore, three shared DEIRGs (IL1R1, CHGB, and NRG1) were identified. Correlation analysis between DEIRGs and immune cells revealed that IL1R1 and NRG1 had a negative or positive correlation with some immune cells both in AD and MDD.

Conclusion: Both DEIRGs and immune cell infiltrations play a vital role in the pathogenesis of AD and MDD. Our findings indicated that there are common genes and biological processes between MDD and AD, which provides a theoretical basis for the study of the comorbidity of MDD and AD.

Keywords: Alzheimer’s disease; Bioinformatics; Depressive; Immune cells; Immune-related genes.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Identification of DEIRGs in the GSE5281 dataset. Heatmap of DEIRGs in AD and control samples
Fig. 2
Fig. 2
Identification of DEIRGs in the GSE76826 dataset. Heatmap of DEIRGs in MDD and control samples
Fig. 3
Fig. 3
Functional enrichment analysis of DEIRGs using Metascape. A Heatmap of the top 20 enriched terms across targets associated with AD and MDD, colored based on the p-value. B Network of the top 20 enriched terms colored based on cluster ID
Fig. 4
Fig. 4
The protein–protein interaction (PPI) network analysis. A PPI network of the DEIRGs from AD and MDD. Red nodes were identified from the GSE5281 dataset, and blue nodes were identified from the GSE76826 dataset. B All lists are merged and Colored by Cluster (Full connection). C All lists are merged and Colored by Cluster (Keep MCODE nodes only)
Fig. 5
Fig. 5
Identification of shared DEIRGs in AD and MDD. A Venn diagram of DEIRGs between AD and MDD. B ROC curve of DEIRGs in AD. C ROC curve of DEIRGs in MDD
Fig. 6
Fig. 6
Validation of shared DEIRGs in AD and MDD. B ROC curve of DEIRGs in GSE132903 dataset. C ROC curve of DEIRGs in GSE98793 dataset
Fig. 7
Fig. 7
Single gene GSEA of CHGB (A), IL1R1 (B), and NRG1 (C) in AD
Fig. 8
Fig. 8
Single gene GSEA of CHGB (A), IL1R1 (B), and NRG1 (C) in MDD
Fig. 9
Fig. 9
The landscape of immune cell infiltration between control and AD groups. A The violin plots indicate the abundance of immune cell populations in control and AD groups. Lollipop diagram of a correlation between CHGB (B), IL1R1 (C), NRG1 (D) expression, and immune cell infiltration level
Fig. 10
Fig. 10
The landscape of immune cell infiltration between control and MDD groups. A The violin plots indicate the abundance of immune cell populations in control and MDD groups. Lollipop diagram of a correlation between CHGB (B), IL1R1 (C), NRG1 (D) expression, and immune cell infiltration level
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