Integrated Analysis of Single-Cell RNA Sequencing and Transcriptome Data Identifies a Pyroptosis-Associated Diagnostic Model for Parkinson’s Disease

Background : Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by insidious onset. Despite the emphasis on motor symptom-based diagnosis, there remains an unmet clinical need for effective diagnostic approaches during the prodromal phase of PD. Recent advancements in single-cell RNA sequencing (scRNA-seq) and transcriptomic analyses of PD patients open avenues for identifying potential diagnostic biomarkers. Methods : A comprehensive cell trajectory analysis was conducted using scRNA-seq datasets to pinpoint gene expressions associated with cellular transition from healthy to PD-affiliated state. Integrating the scRNA-seq datasets with Weighted Gene Co-expression Network Analysis (WGCNA) allowed the extraction of pyroptosis-associated differentially expressed genes (PDEGs). Leveraging LASSO logistic regression, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and random forest methodologies, we devised a diagnostic model centered on PDEGs. Additionally, immunoinfiltration, inflammatory signaling pathways, and intercellular communication were discerned through scRNA-seq analyses. Results : In PD patients, the number of cells including metencephalic-like cells, excitatory neurons, inhibitory neurons, and MHB-like cells were significantly reduced, whereas the proportion of astrocytes and microglia, the immunoinfiltration and inflammatory signaling pathways were upregulated as compared with healthy individuals. Using scRNA-seq and WGCNA analyses, two pyroptosis-related diagnostic genes POLR2K and TIMM8B were identified, and a diagnostic model based on them was constructed, which showed promising performance upon validation. Conclusion : This study cleverly established a pyroptosis-related diagnostic model for PD through the analyses of scRNA-seq combined with transcriptome data, which improved the understanding of the role of PDEGs in PD and provided new insights into the diagnostic strategies for this neurodegenerative ailment.