Blood SSR1: A Possible Biomarker for Early Prediction of Parkinson’s Disease

Abstract

Parkinson’s disease (PD) is the second most common neurodegenerative disease associated with age. Early diagnosis of PD is key to preventing the loss of dopamine neurons. Peripheral-blood biomarkers have shown their value in recent years because of their easy access and long-term monitoring advantages. However, few peripheral-blood biomarkers have proven useful. This study aims to explore potential peripheral-blood biomarkers for the early diagnosis of PD. Three substantia nigra (SN) transcriptome datasets from the Gene Expression Omnibus (GEO) database were divided into a training cohort and a test cohort. We constructed a protein–protein interaction (PPI) network and a weighted gene co-expression network analysis (WGCNA) network, found their overlapping differentially expressed genes and studied them as the key genes. Analysis of the peripheral-blood transcriptome datasets of PD patients from GEO showed that three key genes were upregulated in PD over healthy participants. Analysis of the relationship between their expression and survival and analysis of their brain expression suggested that these key genes could become biomarkers. Then, animal models were studied to validate the expression of the key genes, and only SSR1 (the signal sequence receptor subunit1) was significantly upregulated in both animal models in peripheral blood. Correlation analysis and logistic regression analysis were used to analyze the correlation between brain dopaminergic neurons and SSR1 expression, and it was found that SSR1 expression was negatively correlated with dopaminergic neuron survival. The upregulation of SSR1 expression in peripheral blood was also found to precede the abnormal behavior of animals. In addition, the application of artificial intelligence technology further showed the value of SSR1 in clinical PD prediction. The three classifiers all showed that SSR1 had high predictability for PD. The classifier with the best prediction accuracy was selected through AUC and MCC to construct a prediction model. In short, this research not only provides potential biomarkers for the early diagnosis of PD but also establishes a possible artificial intelligence model for predicting PD.