Department of Neurobiology, Neurology and Geriatrics, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Disorders, Beijing, China.
Clinical Center for Parkinson's Disease, Capital Medical University, Beijing, China.
Aging (Albany NY). 2024 Sep 10;16(17):12191-12208. doi: 10.18632/aging.205954.
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.
帕金森病（PD）是一种多因素疾病，缺乏可靠的诊断生物标志物。现在很清楚，衰老时 PD 发病的最大危险因素。因此，有必要确定与 PD 衰老相关的新型生物标志物。在这项研究中，我们从人类衰老基因数据库中下载了与衰老相关的基因。为了筛选和验证 PD 的生物标志物，我们使用了 11 名 PD 患者和 13 名健康对照（HC）个体的全血 RNA-Seq 数据作为训练数据集，并使用从基因表达综合数据库（GEO）数据库中检索到的三个数据集作为验证数据集。使用 R 中的 limma 包，我们在训练数据集中发现了 1435 个差异表达基因（DEGs）。在这些基因中，有 29 个基因既存在于 DEGs 中，也存在于 307 个与衰老相关的基因中。通过使用机器学习算法（LASSO、RF、SVM 和 RR）、Venn 图和 LASSO 回归，从这四个基因中确定了四个可能的 PD 生物标志物；并在外部验证数据集中以及在 10 名 PD 患者和 10 名 HC 个体的外周血单核细胞（PBMC）中通过 qRT-PCR 进行了验证。基于这些生物标志物，我们开发了一种具有可靠预测能力的 PD 诊断模型。鉴定出的两个生物标志物与 PD 患者和 HC 个体的免疫细胞浸润状态具有明显的相关性。总之，确定了四个与衰老相关的基因作为稳健的诊断生物标志物，它们可能作为 PD 治疗的潜在靶点。
