Transcriptomics profiling of Parkinson’s disease progression subtypes reveals distinctive patterns of gene expression
Background Parkinson’s Disease (PD) varies widely among individuals, and Artificial Intelligence (AI) has recently helped to identify three disease progression subtypes. While their clinical features are already known, their gene expression profiles remain unexplored. Objectives The objectives of th...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-09-01
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| Series: | Journal of Central Nervous System Disease |
| Online Access: | https://doi.org/10.1177/11795735241286821 |
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| Summary: | Background Parkinson’s Disease (PD) varies widely among individuals, and Artificial Intelligence (AI) has recently helped to identify three disease progression subtypes. While their clinical features are already known, their gene expression profiles remain unexplored. Objectives The objectives of this study were (1) to describe the transcriptomics characteristics of three PD progression subtypes identified by AI, and (2) to evaluate if gene expression data can be used to predict disease subtype at baseline. Design This is a retrospective longitudinal cohort study utilizing the Parkinson’s Progression Markers Initiative (PPMI) database. Methods Whole blood RNA-Sequencing data underwent differential gene expression analysis, followed by multiple pathway analyses. A Machine Learning (ML) classifier, namely XGBoost, was trained using data from multiple modalities, including gene expression values. Results Our study identified differentially expressed genes (DEGs) that were uniquely associated with Parkinson’s disease (PD) progression subtypes. Importantly, these DEGs had not been previously linked to PD. Gene-pathway analysis revealed both distinct and shared characteristics between the subtypes. Notably, two subtypes displayed opposite expression patterns for pathways involved in immune response alterations. In contrast, the third subtype exhibited a more unique profile characterized by increased expression of genes related to detoxification processes. All three subtypes showed a significant modulation of pathways related to the regulation of gene expression, metabolism, and cell signaling. ML revealed that the progression subtype with the worst prognosis can be predicted at baseline with 0.877 AUROC, yet the contribution of gene expression was marginal for the prediction of the subtypes. Conclusion This study provides novel information regarding the transcriptomics profiles of PD progression subtypes, which may foster precision medicine with relevant indications for a finer-grained diagnosis and prognosis. |
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| ISSN: | 1179-5735 |