机构：[1]Department of Neurobiology, Neurology and Geriatrics, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Disorders, Beijing, China.神经科系统内科系统神经内科老年医学科首都医科大学宣武医院[2]Clinical Center for Parkinson's Disease, Capital Medical University, Beijing, China.[3]Key Laboratory for Neurodegenerative Disease of the Ministry of Education, Beijing Key Laboratory for Parkinson's Disease, Parkinson Disease Center of Beijing Institute for Brain Disorders, Beijing, China.
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.
基金：
This study was supported by the National Key R&D Program of China (2021YFC2501205), the Beijing Municipal Administration of Hospitals’ Mission Plan (No. SML20150803), the Beijing Municipal Science and Technology Commission (No. Z161100005116011, Z171100000117013), and the Beijing Municipal Commission of Health and Family Planning (No. PXM2017_026283_000002).
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2023]版：
大类|3 区医学
小类|3 区细胞生物学3 区老年医学
最新[2023]版：
大类|3 区医学
小类|3 区细胞生物学3 区老年医学
第一作者：
第一作者机构：[1]Department of Neurobiology, Neurology and Geriatrics, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Disorders, Beijing, China.
通讯作者：
通讯机构：[1]Department of Neurobiology, Neurology and Geriatrics, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Disorders, Beijing, China.[2]Clinical Center for Parkinson's Disease, Capital Medical University, Beijing, China.[3]Key Laboratory for Neurodegenerative Disease of the Ministry of Education, Beijing Key Laboratory for Parkinson's Disease, Parkinson Disease Center of Beijing Institute for Brain Disorders, Beijing, China.
推荐引用方式(GB/T 7714)：
Yang Weiwei,Xu Shengli,Zhou Ming,et al.Aging-related biomarkers for the diagnosis of Parkinson's disease based on bioinformatics analysis and machine learning[J].Aging.2024,16(17):12191-12208.doi:10.18632/aging.205954.
APA：
Yang Weiwei,Xu Shengli,Zhou Ming&Chan Piu.(2024).Aging-related biomarkers for the diagnosis of Parkinson's disease based on bioinformatics analysis and machine learning.Aging,16,(17)
MLA：
Yang Weiwei,et al."Aging-related biomarkers for the diagnosis of Parkinson's disease based on bioinformatics analysis and machine learning".Aging 16..17(2024):12191-12208
