Analysis of key lncRNA related to Parkinson's disease based on gene co-expression weight networks.

Objectives: To identify a key Long chain non-coding RNAs (lncRNAs) related to PD and provide a new perspective on the role of LncRNAs in Parkinson's disease (PD) pathophysiology.

Methods: Our study involved analyzing gene chips from the substantia nigra and white blood cells, both normal and PD-inclusive, in the Gene Expression Omnibus (GEO) database, utilizing a weighted gene co-expression network analysis (WGCNA). The technique of WGCNA facilitated the examination of differentially expressed genes (DEGs) in the substantia nigra and the white blood cells of individuals with PD. When merged with clinical data, gene modules containing crucial clinical details were chosen for network integration in GO and KEGG enrichment analysis.

Results: A pair of LncRNA modules were identified. The crucial component in GSE7621 was the turquoise module. The DEGs were acquired using GSE133347. GO functions focused on phosphatidylinositol phosphate binding, inflammatory responses, and the regulation of nerves and synapses. KEGG analyses were largely enriched within the P13K-Akt, FaxO, mTOR, Oxytocin, and cGMP-PKG signaling pathways. A Venn diagram revealed that the two key LncRNA were CH17-189H20.1 and RP11-168O16.1.

Conclusion: Using the WGCNA method, we obtained PD-related modules, identified biologically significant gene modules, obtained core LncRNAs, and found potential target genes for enrichment analysis. The objective of our research was to advance more detailed and efficient treatment methods for lncRNAs associated with PD.