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Serum proteomic biomarker investigation of vascular depression using data-independent acquisition: a pilot study
Liuyi Lan1, Sisi Peng2, Ran Zhang1
1Department of Neurology, Zhongnan Hospital, Wuhan University, Wuhan, China.
Insights
This study identifies unique protein signatures in vascular depression (VaD) using serum proteomic analysis. A novel diagnostic model combining five proteins shows promise for predicting VaD risk.
Area of Science:
- Neuroscience
- Biochemistry
- Genomics
Background:
- Vascular depression (VaD) is a complex depressive disorder linked to cerebrovascular disease and vascular risk factors.
- Current diagnostic challenges and limited understanding of VaD pathophysiology hinder effective management.
- Identifying reliable biomarkers is crucial for improving VaD diagnosis and treatment.
Purpose of the Study:
- To analyze serum proteomic signatures in patients with VaD.
- To identify potential protein biomarkers with diagnostic significance for VaD.
- To develop a predictive model for VaD risk using proteomic data.
Main Methods:
- Serum proteome profiling of 35 VaD patients and 36 controls using liquid chromatography-tandem mass spectrometry (LC-MS/MS).
- Functional enrichment analysis utilizing Gene Ontology (GO), KEGG pathway, and Reactome databases.
- Machine learning algorithms for candidate protein screening and development of a diagnostic model.
Main Results:
- Significant differences in protein expression were observed, with 29 proteins upregulated and 31 downregulated in VaD patients.
- Enrichment analyses indicated dysregulation in neurobiological processes, including synaptic vesicle cycle and axon guidance.
- A nomogram combining HECTD3, NID2, FTO, GOLM1, and NPL demonstrated favorable efficacy in predicting VaD risk.
Conclusions:
- This study provides a comprehensive proteomic profile of VaD.
- The developed proteomics-based diagnostic model offers a promising tool for VaD risk prediction.
- Further validation of these biomarkers could enhance clinical diagnostics for VaD.
Background:
Vascular depression (VaD) is a depressive disorder closely associated with cerebrovascular disease and vascular risk factors. It remains underestimated owing to challenging diagnostics and limited information regarding the pathophysiological mechanisms of VaD. The purpose of this study was to analyze the proteomic signatures and identify the potential biomarkers with diagnostic significance in VaD.
Methods:
Deep profiling of the serum proteome of 35 patients with VaD and 36 controls was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Functional enrichment analysis of the quantified proteins was based on Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Reactome databases. Machine learning algorithms were used to screen candidate proteins and develop a protein-based model to effectively distinguish patients with VaD.
Results:
There were 29 up-regulated and 31 down-regulated proteins in the VaD group compared to the controls (|log2FC| ≥ 0.26, p ≤ 0.05). Enrichment pathways analyses showed that neurobiological processes related to synaptic vesicle cycle and axon guidance may be dysregulated in VaD. Extrinsic component of synaptic vesicle membrane was the most enriched term in the cellular components (CC) terms. 19 candidate proteins were filtered for further modeling. A nomogram was developed with the combination of HECT domain E3 ubiquitin protein ligase 3 (HECTD3), Nidogen-2 (NID2), FTO alpha-ketoglutarate-dependent dioxygenase (FTO), Golgi membrane protein 1 (GOLM1), and N-acetylneuraminate lyase (NPL), which could be used to predict VaD risk with favorable efficacy.
Conclusion:
This study offers a comprehensive and integrated view of serum proteomics and contributes to a valuable proteomics-based diagnostic model for VaD.
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