Parkinson’s disease: an integrative bioinformatics and machine learning analysis reveals tryptophan metabolism-associated gene biomarkers

Abstract

Background: Parkinson’s disease (PD) represents a prevalent neurodegenerative disorder among the aging population, characterized by significant disruptions in neurological metabolic pathways. Recent evidence highlights the crucial role of tryptophan metabolism in the pathogenesis of PD; however, a comprehensive exploration of tryptophan metabolism genes (TMGs) and their specific contributions remains largely uncharted. Methods: Employing bioinformatics, this study aimed to identify and validate TMGs associated with PD. A differential expression analysis was performed on a carefully selected set of four candidate genes. GSEA and GSVA were utilized to elucidate the biological functions and pathways linked to these TMGs. Additionally, Lasso regression and SVM-RFE were implemented to identify key hub genes and assess the diagnostic potential of three TMGs in distinguishing PD from non-PD samples. The relationship between critical TMGs and clinical parameters was also investigated, with expression validation conducted using datasets GSE6613 and GSE7621. Results: Our analysis identified three TMGs—ALDH9A1, CYP1A1, and CYP1B1—as significantly associated with PD. These genes are implicated in essential biological processes, including the catabolism of small molecules, fatty acid metabolism, and alcohol metabolism, highlighting their extensive functional relevance in PD. Moreover, the diagnostic efficacy of these TMGs in differentiating PD from control samples showed promising results. Conclusions: This study identifies three TMGs with substantial associations to PD, enhancing our molecular understanding of the disease. These findings not only contribute to the elucidation of PD at the molecular level but also pave the way for the development of novel biomarkers.