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. 2023 May 9:14:1156894.
doi: 10.3389/fneur.2023.1156894. eCollection 2023.

APOE as potential biomarkers of moyamoya disease

Haibin Wu #  1 , Jiang Xu #  1 , Jiarong Sun #  1 , Jian Duan  1 , Jinlin Xiao  1 , Quan Ren  1 , Pengfei Zhou  1 , Jian Yan  1 , Youping Li  1 , Xiaoxing Xiong  2 , Erming Zeng  1
Affiliations

APOE as potential biomarkers of moyamoya disease

Haibin Wu et al. Front Neurol. .

Abstract

Objective: The mechanisms underpinning Moyamoya disease (MMD) remain unclear, and effective biomarkers remain unknown. The purpose of this study was to identify novel serum biomarkers of MMD.

Methods: Serum samples were collected from 23 patients with MMD and 30 healthy controls (HCs). Serum proteins were identified using tandem tandem-mass-tag (TMT) labeling combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS). Differentially expressed proteins (DEPs) in the serum samples were identified using the SwissProt database. The DEPs were assessed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, Gene Ontology (GO), and protein-protein interaction (PPI) networks, and hub genes were identified and visualized using Cytoscape software. Microarray datasets GSE157628, GSE189993, and GSE100488 from the Gene Expression Omnibus (GEO) database were collected. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DE-miRNAs) were identified, and miRNA targets of DEGs were predicted using the miRWalk3.0 database. Serum apolipoprotein E (APOE) levels were compared in 33 MMD patients and 28 Moyamoya syndrome (MMS) patients to investigate the potential of APOE to be as an MMD biomarker.

Results: We identified 85 DEPs, of which 34 were up- and 51 down-regulated. Bioinformatics analysis showed that some DEPs were significantly enriched in cholesterol metabolism. A total of 1105 DEGs were identified in the GSE157628 dataset (842 up- and 263 down-regulated), whereas 1290 were identified in the GSE189993 dataset (200 up- and 1,090 down-regulated). The APOE only overlaps with the upregulated gene expression in Proteomic Profiling and in GEO databases. Functional enrichment analysis demonstrated that APOE was associated with cholesterol metabolism. Moreover, 149 miRNAs of APOE were predicted in the miRWalk3.0 database, and hsa-miR-718 was the only DE-miRNA overlap identified in MMD samples. Serum APOE levels were significantly higher in patients with MMD than in those without. The performance of APOE as an individual biomarker to diagnose MMD was remarkable.

Conclusions: We present the first description of the protein profile of patients with MMD. APOE was identified as a potential biomarker for MMD. Cholesterol metabolism was found to potentially be related to MMD, which may provide helpful diagnostic and therapeutic insights for MMD.

Keywords: apolipoprotein E; biomarkers; carotid artery; cholesterol; moyamoya disease.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Detection of differentially expressed proteins. Overall distribution of differentially expressed proteins. (A) Heat map of 85 differentially expressed proteins in the two groups. In the color bar, red represents high expression, and purple represents low expression. (B) Volcano plot of the differentially expressed proteins identified in the two groups.
Figure 2
Figure 2
Bioinformatics analysis of differentially expressed proteins. Functional enrichment and protein–protein interaction analysis (A) GO enrichment results for DEPs in biological processes. GO, Gene Ontology; BP, biological processes; CC, cellular components; MF, molecular function. (B) Bubble chart displaying the enrichment of differentially expressed genes and the top 10 enriched KEGG pathways. (C) Chord plot displaying the enriched enrichment network of differentially expressed genes and the top 10 enriched KEGG pathways. (D) The protein-protein interaction network was analyzed using the STRING database. There were two nodes and 83 edges in the network.
Figure 3
Figure 3
Overlaps gene between with proteomic profiling and GEO database and bioinformatics analysis. (A) Volcano plot of differentially expressed genes in the GSE157628 dataset. (B) Volcano plot of differentially expressed genes in the GSE189993 dataset. (C) Venn diagram showing the intersection of differentially expressed genes between the GSE189993 and GSE157628 datasets. (D) Venn diagram showing the intersection of highly expressed genes between GSE189993 and GSE157628 datasets. (E) Venn diagram showing the intersection of highly expressed genes in the GEO database and Proteomic Profiling. (F) The protein-protein interaction network of APOE was constructed using Cytoscape.
Figure 4
Figure 4
Identification of DE-miRNAs between MMD and MCA. Differentially expressed miRNAs were identified using the GSE100488 dataset. (A) Heat map of miRNAs in the GSE100488 dataset (red indicates high expression, and blue indicates low expression). (B) Heat map of the 36 differentially expressed miRNAs in the GSE100488 dataset (red indicates high expression and blue indicates low expression). (C) Volcano plots of differentially expressed miRNAs in the two groups. (D) The Venn diagram reveals the intersection of DE-miRNAs between the GSE100488 dataset and predicted miRNAs of APOE using the miRWalk3 database.
Figure 5
Figure 5
Validation of APOE as an individual biomarker in an independent cohort. (A) Serum APOE expression levels in the two groups were visualized using violin plots. (B) Serum TG, TC, LDL, and HDL levels between the two groups are shown by violin plots. (C) Distribution of age between the two groups is shown using violin plots. (D) ROC curve and corresponding AUC of APOE in the independent validation cohort (n = 61). DE, differentially expressed; miRNA, microRNA.
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