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IDENTIFICATION OF A NOVEL PROTEOMIC BIOMARKER IN PARKINSON’S DISEASE: DISCOVERY AND REPLICATION IN BLOOD, BRAIN AND CSF
Abstract
Aims
Biomarkers to aid diagnosis and understand progression of Parkinson’s Disease (PD) are vital for targeting treatment in the early phases of disease. Here, we aim to discover a multi-protein panel representative of PD and make mechanistic inferences from protein expression profiles with the broader objective of finding novel biomarkers.
Methods
We used aptamer-based technology (SomaLogic®) to measure proteins in 1599 serum samples, 85 CSF samples and 37 brain tissue samples collected from two observational longitudinal cohorts (Oxford Parkinson’s Disease Centre and Tracking Parkinson’s) and the PD Brain Bank respectively. Random forest machine learning was performed to discover new proteins related to disease status. Differential regulation analysis and pathway analysis was performed to identify functional and mechanistic disease associations.
Results
We have generated multi-protein expression signatures containing potential novel biomarkers. The diagnostic classifier signature was tested across modalities (CSF AUC = 0.74, p-value 0.0009; brain AUC = 0.75, p-value = 0.006). In the validation dataset we showed the same classifiers were significantly related to disease status (p-values < 0.001). Differential expression analysis and Weighted Gene Correlation Network Analysis (WGCNA) highlighted key proteins and pathways with known relationships to PD. Of note are proteins from the complement and coagulation cascades suggesting a disease relationship to immune response.
Conclusions
The combined analytical approaches in a relatively large number of samples, across tissue types and with replication as well as validation provides mechanistic insights into disease as well as nominating a protein signature that might be a starting point for further biomarker evaluation.