Application of Bayesian-Derived Gene Interaction Network and Subnetwork for Classification of Idiopathic Parkinson Disease with Biomarkers

Abstract

Given the expenses associated with Parkinson Disease (PD), it is imperative to develop a method for accurate diagnosis before emergence of motor symptoms. While there are tests that can measure early onset of PD, these tests still rely on subjective input from patients and can thus be biased. Factors influencing their accuracy include environmental agents such as education and culture. Thus, blood-based transcriptomic signatures are a promising direction for biomarkers in PD that are minimally invasive and more objective than clinical evaluation. In this paper, we analyzed gene expression from the PPMI cohort. Using a Bayesian framework, a gene interaction network was constructed to enhance differential expressed genes (DEG) analysis through signal diffusion. In tandem with clinical traits, 13 genes were selected for optimal discrimination using a random forest model. Within the holdout test set, the model achieved an AUC of 0.972, outperforming contemporary models. The model performance demonstrates the potential of using blood-based transcriptomics to enhance clinical diagnosis of PD. Other genes recovered by network diffusion but missed by traditional DEG analysis are already validated as potential biomarkers in contemporary PD research. By combining transcriptomic signatures with clinical evaluation, accurate diagnosis of PD can happen before the major destruction of dopaminergic neurons, which is critical for effective intervention and treatment.