ArticleAging2024
Aging-related biomarkers for the diagnosis of Parkinson's disease based on bioinformatics analysis and machine learning.
Article in Aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Profiling peripheral immune cells in Parkinson's disease: A scoping review.Journal of Parkinson's disease · 2026Review
- Parkinson's Disease: From Metabolism to Genetics-A Comprehensive Review.Current issues in molecular biology · 2026Review
- Identification of age-specific urinary metabolic biomarkers in Wilson disease using machine learning: a comparative study of ensemble tree models.Open medicine (Warsaw, Poland) · 2026Article
- A comparative evaluation of sufficient dimension reduction and traditional statistical methods for composite biomarker score construction in diagnostic classification.BMC medical research methodology · 2025Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) is a multifactorial disease that lacks reliable biomarkers for its diagnosis. It is now clear that aging is the greatest risk factor for developing PD. Therefore, it is necessary to identify novel biomarkers associated with aging in PD. In this study, we downloaded aging-related genes from the Human Ageing Gene Database. To screen and verify biomarkers for PD, we used whole-blood RNA-Seq data from 11 PD patients and 13 healthy control (HC) subjects as a training dataset and three datasets retrieved from the Gene Expression Omnibus (GEO) database as validation datasets. Using the limma package in R, 1435 differentially expressed genes (DEGs) were found in the training dataset. Of these genes, 29 genes were found to occur in both DEGs and 307 aging-related genes. By using machine learning algorithms (LASSO, RF, SVM, and RR), Venn diagrams, and LASSO regression, four of these genes were determined to be potential PD biomarkers; these were further validated in external validation datasets and by qRT-PCR in the peripheral blood mononuclear cells (PBMCs) of 10 PD patients and 10 HC subjects. Based on the biomarkers, a diagnostic model was developed that had reliable predictive ability for PD. Two of the identified biomarkers demonstrated a meaningful correlation with immune cell infiltration status in the PD patients and HC subjects. In conclusion, four aging-related genes were identified as robust diagnostic biomarkers and may serve as potential targets for PD therapeutics.
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