特发性帕金森病血液基因表达分析
Analysis of blood-based gene expression in idiopathic Parkinson disease.
作者信息
Shamir Ron, Klein Christine, Amar David, Vollstedt Eva-Juliane, Bonin Michael, Usenovic Marija, Wong Yvette C, Maver Ales, Poths Sven, Safer Hershel, Corvol Jean-Christophe, Lesage Suzanne, Lavi Ofer, Deuschl Günther, Kuhlenbaeumer Gregor, Pawlack Heike, Ulitsky Igor, Kasten Meike, Riess Olaf, Brice Alexis, Peterlin Borut, Krainc Dimitri
机构信息
From the School of Computer Science (R.S., D.A., H.S.), Tel Aviv University, Israel; Institute of Neurogenetics (C.K., E.-J.V., H.P., M.K.), University of Lübeck, Germany; Department of Psychiatry and Psychotherapy (E.-J.V., M.K.), University of Lübeck, Germany; Institute of Medical Genetics and Applied Genomics (M.B., S.P., O.R.), University of Tübingen, Germany; IMGM Laboratories GmbH (M.B.), Martinsried, Germany; Mediterranean Institute for Life Sciences (M.U.), Split, Croatia; Department of Neurology (Y.C.W., D.K.), Northwestern University Feinberg School of Medicine, Chicago, IL; Clinical Institute of Medical Genetics (A.M., B.P.), University Medical Center Ljubljana, Slovenia; Sorbonne Universités (J.C.-C., S.L., A.B.), UPMC Université Paris 6 UMR S 1127, Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France; Centre d'Investigation Clinique Pitié Neurosciences CIC-1422 (J.C.-C.), Paris, France; Machine Learning Technologies Group (O.L.), IBM Research-Haifa, Mount Carmel, Israel; Department of Neurology (G.D., G.K.), Kiel University, Germany; and Department of Biological Regulation (I.U.), Weizmann Institute of Science, Rehovot, Israel.
出版信息
Neurology. 2017 Oct 17;89(16):1676-1683. doi: 10.1212/WNL.0000000000004516. Epub 2017 Sep 15.
OBJECTIVE
To examine whether gene expression analysis of a large-scale Parkinson disease (PD) patient cohort produces a robust blood-based PD gene signature compared to previous studies that have used relatively small cohorts (≤220 samples).
METHODS
Whole-blood gene expression profiles were collected from a total of 523 individuals. After preprocessing, the data contained 486 gene profiles (n = 205 PD, n = 233 controls, n = 48 other neurodegenerative diseases) that were partitioned into training, validation, and independent test cohorts to identify and validate a gene signature. Batch-effect reduction and cross-validation were performed to ensure signature reliability. Finally, functional and pathway enrichment analyses were applied to the signature to identify PD-associated gene networks.
RESULTS
A gene signature of 100 probes that mapped to 87 genes, corresponding to 64 upregulated and 23 downregulated genes differentiating between patients with idiopathic PD and controls, was identified with the training cohort and successfully replicated in both an independent validation cohort (area under the curve [AUC] = 0.79, = 7.13E-6) and a subsequent independent test cohort (AUC = 0.74, = 4.2E-4). Network analysis of the signature revealed gene enrichment in pathways, including metabolism, oxidation, and ubiquitination/proteasomal activity, and misregulation of mitochondria-localized genes, including downregulation of , , and .
CONCLUSIONS
We present a large-scale study of PD gene expression profiling. This work identifies a reliable blood-based PD signature and highlights the importance of large-scale patient cohorts in developing potential PD biomarkers.
目的
与之前使用相对较小队列（≤220个样本）的研究相比，研究对大规模帕金森病（PD）患者队列进行基因表达分析是否能产生可靠的基于血液的PD基因特征。
方法
共收集了523名个体的全血基因表达谱。预处理后，数据包含486个基因谱（n = 205例PD患者，n = 233例对照，n = 48例其他神经退行性疾病），这些基因谱被分为训练、验证和独立测试队列，以识别和验证基因特征。进行了批次效应降低和交叉验证以确保特征的可靠性。最后，对该特征进行功能和通路富集分析，以识别与PD相关的基因网络。
结果
通过训练队列确定了一个由100个探针组成的基因特征，这些探针映射到87个基因，对应于64个上调基因和23个下调基因，可区分特发性PD患者和对照，并且在独立验证队列（曲线下面积[AUC] = 0.79，P = 7.13E - 6）和随后的独立测试队列（AUC = 0.74，P = 4.2E - 4）中均成功复制。对该特征的网络分析揭示了基因在包括代谢、氧化和泛素化/蛋白酶体活性等通路中的富集，以及线粒体定位基因的失调，包括ATP5A1、ATP5B和NDUFA4的下调。
结论
我们展示了一项关于PD基因表达谱的大规模研究。这项工作确定了一个可靠的基于血液的PD特征，并强调了大规模患者队列在开发潜在PD生物标志物中的重要性。
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