Multi-Transcriptome-Informed Network Pharmacology Reveals Novel Biomarkers and Therapeutic Candidates for Parkinson's...

Background: Parkinson's disease (PD) is a complex neurodegenerative disorder in aged people with multifaceted molecular underpinnings. It poses a severe threat to millions of older adults worldwide. The understanding of the molecular mechanisms of PD development and the performance of its therapeutic strategies has not yet reached a satisfactory level. Methods: This study integrated six transcriptomic datasets to uncover key genes (KGs) and their underlying pathogenic mechanisms, providing insights into potential therapeutic strategies for PD. We designed a comprehensive computational pipeline using various bioinformatics tools and databases to investigate PD-causing KGs, focusing on their functions, pathways, regulatory mechanisms, and potential therapeutic drug molecules. Results: In order to explore PD-causing KGs, we initially identified 303 differentially expressed genes (DEGs) between PD and control samples with 204 upregulated and 99 downregulated DEGs using the LIMMA approach with threshold values at Adj. p-value < 0.05 and abs (log₂FC) ≥ 1.0. Then, protein-protein interaction (PPI) network analysis pinpointed seven top-ranked DEGs (GAPDH, PTEN, CCND1, APOE, ESR1, MAPK3/ERK1, and SNCA) as KGs or central modulators of PD pathogenesis. Regulatory network analysis of KGs identified 3 top-ranked transcription factors (FOXC1, NFKB1, and TFAP2A) and 6 microRNAs (hsa-let-7b-5p, hsa-mir-16-5p, and others) as the pivotal regulators of KGs. Gene Ontology (GO) terms and KEGG pathway enrichment analyses with KGs revealed several crucial biological processes, molecular functions, cellular components, and neurodegenerative pathways associated with the development of PD. Finally, the top five molecules guided by KGs (Nilotinib, Bromocriptine, Withaferin-A, Celastrol, and Donepezil) were identified as promising drug candidates against PD and validated computationally through ADME/T analysis and molecular dynamics simulation studies. Conclusions: The findings of this study may serve as valuable resources for developing effective treatment strategies for PD patients.