Integrated Transcriptomic and Machine Learning Analysis Identifies as a Diagnostic Biomarker and Key Pathogenic Factor in Parkinson's Disease.

BACKGROUND

Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons. This study aims to discover potential new genetic biomarkers for PD.

METHODS

Transcriptome data from a total of 56 patients with PD and 61 healthy controls were downloaded from the Gene Expression Omnibus (GEO) database. Differential gene expression (DEG) analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms (LASSO, Random Forest, SVM-RFE) were employed to identify pivotal PD-associated genes. Additionally, RT-qPCR experiments were conducted to validate our findings in clinical specimens. Functional enrichment analysis and Gene Set Enrichment Analysis (GSEA) were performed to explore the functional and pathway mechanisms of the identified genes in PD. Molecular docking studies revealed potential small-molecule drug targets for the key genes.

RESULTS

The results from the three machine learning algorithms identified () as a key gene in PD. Gene expression analysis indicated that is significantly downregulated in PD patients, and the receiver operating characteristic (ROC) analysis validated the diagnostic potential of . The results from RT-qPCR on clinical specimens confirmed the findings from public database analyses. Functional enrichment analysis suggested that is involved in dopamine biosynthesis and synaptic transmission for PD pathology. Additionally, expression correlated significantly with immune cell infiltration. Furthermore, molecular docking results indicated that Acalabrutinib, Tirabrutinib Hydrochloride, and Ibrutinib are potential targeted therapeutic agents for .

CONCLUSION

These findings underscore as a novel diagnostic biomarker and potential therapeutic target for PD, warranting further mechanistic studies and clinical validation.