Integrative analysis of gene expression profiles of substantia nigra identifies potential diagnosis biomarkers in Parkinson's disease

• Published in: Scientific reports Vol. 14; no. 1; p. 2167
• Main Authors: Huang, Junming, Li, Bowen, Wei, Huangwei, Li, Chengxin, Liu, Chao, Mi, Hua, Chen, Shaohua
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 25.01.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/208; 631/250; 631/378; Biomarkers; Biosynthesis; Diagnosis; Dopamine receptors; Gene expression; Humanities and Social Sciences; Infiltration; Lewy bodies; Movement disorders; multidisciplinary; Nerve conduction; Neurodegeneration; Neurodegenerative diseases; Parkinson's disease; Peripheral blood; Plasma cells; Regression analysis; Science; Science (multidisciplinary); Substantia nigra
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-52276-0

Parkinson's disease (PD) is a progressive neurodegenerative disease whose etiology is attributed to development of Lewy bodies and degeneration of dopaminergic neurons in the substantia nigra (SN). Currently, there are no definitive diagnostic indicators for PD. In this study, we aimed to identify potential diagnostic biomarkers for PD and analyzed the impact of immune cell infiltrations on disease pathogenesis. The PD expression profile data for human SN tissue, GSE7621, GSE20141, GSE20159, GSE20163 and GSE20164 were downloaded from the Gene Expression Omnibus (GEO) database for use in the training model. After normalization and merging, we identified differentially expressed genes (DEGs) using the Robust rank aggregation (RRA) analysis. Simultaneously, DEGs after batch correction were identified. Gene interactions were determined through venn Diagram analysis. Functional analyses and protein–protein interaction (PPI) networks were used to the identify hub genes, which were visualized through Cytoscape. A Lasso Cox regression model was employed to identify the potential diagnostic genes. The GSE20292 dataset was used for validation. The proportion of infiltrating immune cells in the samples were determined via the CIBERSORT method. Sixty-two DEGs were screened in this study. They were found to be enriched in nerve conduction, dopamine (DA) metabolism, and DA biosynthesis Gene Ontology (GO) terms. The PPI network and Lasso Cox regression analysis revealed seven potential diagnostic genes, namely SLC18A2, TAC1, PCDH8, KIAA0319, PDE6H, AXIN1, and AGTR1, were subsequently validated in peripheral blood samples obtained from healthy control (HC) and PD patients, as well as in the GSE20292 dataset. The results revealed the exceptional sensitivity and specificity of these genes in PD diagnosis and monitoring. Moreover, PD patients exhibited a higher number of plasma cells, compared to HC individuals. The SLC18A2, TAC1, PCDH8, KIAA0319, PDE6H, AXIN1 , and AGTR1 are potential diagnostic biomarkers for PD. Our findings also reveal the essential roles of immune cell infiltration in both disease onset and trajectory.