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IntroductionThis study leverages bibliometric analysis to uncover the research landscape and spotlight emerging trends in the field of Parkinson's disease (PD) transcriptomics.MethodsThe relevant literature on Parkinson's disease and transcriptomics was retrieved from the Web of Science Core Collection database. Bibliometric analysis was conducted using VOSviewer, CiteSpace, and the Bibliometrix R package.ResultsA total of 208 research articles were retrieved from January 2011 to March 2025. The number of publications has shown a steady increase, particularly from 2020 to 2024, with an average annual publication rate of 29 articles during this period. The United States and China were the leading countries in terms of publication counts, while the University of Luxembourg and McGill University were the top contributing institutions. The most impactful journals included "Nature Communications" and "NPJ Parkinson's Disease." The co-occurrence analysis of keywords revealed that "Parkinson's disease," "transcriptomics," "neurodegeneration," and "biomarkers" are current research hotspots. Citation burst analysis identified key references related to genetics, transcriptomics, and data analysis tools that have significantly influenced the field.ConclusionThis study offers the first comprehensive bibliometric analysis of Parkinson's disease (PD) transcriptomics research from 2011 to 2025. We reveal a significant surge in research activity, particularly since 2020, driven by advancements in single-cell and spatial transcriptomics. The United States and China lead in publication output, with key contributions from the University of Luxembourg and McGill University. Research hotspots include neuroinflammation, biomarker discovery, and machine learning applications, indicating a shift toward translational research. However, challenges such as data heterogeneity and high biomarker validation failure rates persist. Future research should focus on standardizing methodologies and enhancing clinical relevance. Strategic directions include multi-omics integration, global collaboration, and linking transcriptomic signatures to clinical outcomes, aiming to improve early diagnosis and personalized therapies for PD.
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