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单细胞 RNA 测序和批量转录组数据的综合分析确定了帕金森病相关的焦亡诊断模型。

Integrated analysis of single-cell RNA sequencing and bulk transcriptome data identifies a pyroptosis-associated diagnostic model for Parkinson's disease.

机构信息

Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, 130033, Jilin, China.

出版信息

Sci Rep. 2024 Nov 18;14(1):28548. doi: 10.1038/s41598-024-80185-9.

Abstract

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by an insidious onset. Despite the emphasis on motor symptom-based diagnosis, there remains an unmet clinical need for effective diagnostic approaches during the prodromal phase of PD. Recent advances in single-cell RNA sequencing (scRNA-seq) and bulk transcriptomic analyses of PD patients open avenues for identifying potential diagnostic biomarkers. A comprehensive cell trajectory analysis was conducted using scRNA-seq datasets to identify gene expressions associated with the cellular transition from healthy to PD-associated states. Integration of scRNA-seq datasets with weighted gene co-expression network analysis (WGCNA) allowed extraction of pyroptosis-associated differentially expressed genes (PDEGs). Using LASSO logistic regression, support vector machine recursive feature elimination (SVM-RFE) and random forest methods, we developed a diagnostic model centred on PDEGs. In addition, immunoinfiltration, inflammatory signalling pathways and intercellular communication were detected by scRNA-seq analyses. In PD patients, the number of cells including metencephalic-like cells, excitatory neurons, inhibitory neurons and MHB-like cells was significantly reduced, whereas the proportion of astrocytes and microglia, immunoinfiltration and inflammatory signalling pathways were upregulated compared to healthy individuals. Using scRNA-seq and WGCNA analyses, two pyroptosis-related diagnostic genes, POLR2K and TIMM8B, were identified and a diagnostic model based on them was constructed, which showed promising performance upon validation. This study established a pyroptosis-related diagnostic model for PD through the analyses of scRNA-seq combined with bulk transcriptome data, which improved the understanding of the role of PDEGs in PD and provided new insights into the diagnostic strategies for this neurodegenerative disease.

摘要

帕金森病(PD)是一种进行性神经退行性疾病,其特征为隐匿性发作。尽管强调基于运动症状的诊断,但在 PD 的前驱期仍需要有效的诊断方法。单细胞 RNA 测序(scRNA-seq)和 PD 患者的批量转录组分析的最新进展为确定潜在的诊断生物标志物开辟了途径。使用 scRNA-seq 数据集进行了全面的细胞轨迹分析,以确定与从健康到与 PD 相关状态的细胞转变相关的基因表达。scRNA-seq 数据集与加权基因共表达网络分析(WGCNA)的整合允许提取与细胞焦亡相关的差异表达基因(PDEGs)。使用 LASSO 逻辑回归、支持向量机递归特征消除(SVM-RFE)和随机森林方法,我们开发了一个以 PDEGs 为中心的诊断模型。此外,通过 scRNA-seq 分析检测到免疫浸润、炎症信号通路和细胞间通讯。在 PD 患者中,包括后脑样细胞、兴奋性神经元、抑制性神经元和 MHB 样细胞在内的细胞数量明显减少,而星形胶质细胞和小胶质细胞、免疫浸润和炎症信号通路的比例与健康个体相比上调。使用 scRNA-seq 和 WGCNA 分析,确定了两个与细胞焦亡相关的诊断基因 POLR2K 和 TIMM8B,并构建了基于它们的诊断模型,该模型在验证时表现出有前景的性能。本研究通过 scRNA-seq 结合批量转录组数据的分析,建立了一个与细胞焦亡相关的 PD 诊断模型,这提高了我们对 PDEGs 在 PD 中的作用的理解,并为这种神经退行性疾病的诊断策略提供了新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e11c/11574289/b1a0a18371a1/41598_2024_80185_Fig1_HTML.jpg

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