Deciphering MMRN1 diagnostic and therapeutic implications in the substantia nigra of Parkinson's disease patients via integrative bioinformatic analysis and multi-omics studies.

Background: Parkinson's disease (PD) is a neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra (SN), primarily due to Objective: This study aimed to identify a novel diagnostic and druggable target in the SN of PD patients. Methods: We first nominated PD risk-associated differentially expressed genes (DEGs) in the SN bulk profile (GSE7621) of PD patients using Limma, weighted gene co-expression network analysis (WGCNA), and summary-data-based Mendelian randomization (SMR). Next, three machine learning algorithms [random forest (RF), least absolute shrinkage and selection operator LASSO, and support vector machine (SVM)] were performed for the identification of central pathogenic factors among the risk DEGs in the training PD patient SN bulk profile (integrated GSE20163 and GSE20164). In addition, the diagnostic performance of the identified central pathogenic factor for PD was evaluated in GSE7621, integrated GSE20163 and GSE20164, and independent PD patient SN bulk profiles (GSE140231). In addition, the molecular and immune patterns of the central pathogenic factor were assessed in SN single-cell data from PD patients (GSE7621) were estimated by a cutting-edge analytical framework in temporal and spatial manners. Furthermore, network-based drug screening and molecular docking were subsequently performed to identify potential therapeutic agents targeting the central pathogenic factors. Finally, Results: Multimerin 1 (MMRN1) was identified as an upregulated pathogenic factor predominantly expressed in neurons. Quercetin was highlighted as a promising repurposed drug candidate targeting MMRN1. Conclusion: This study illustrated a novel diagnostic and therapeutic target for PD, which provides novel clues into clinical applications of PD patients.