Abstract
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, p = 7.13E-6) and a subsequent independent test cohort (AUC = 0.74, p = 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 COX4I1, ATP5A1, and VDAC3. 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.
| Original language | English |
|---|---|
| Journal | Neurology |
| Volume | 89 |
| Issue number | 16 |
| Pages (from-to) | 1676-1683 |
| Number of pages | 8 |
| ISSN | 0028-3878 |
| DOIs | |
| Publication status | Published - 17.10.2017 |
Funding
*These authors contributed equally to this work as first authors. †These authors contributed equally to this work as senior authors. 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. Go to Neurology.org for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article. Supported by GENEPARK (FP6 and FP7), the Israel Science Foundation (317/13), the Raymond and Beverly Sackler Chair in Bioinformatics, the Hermann and Lilly Schilling Foundation, and DFG (FOR 2488).
Research Areas and Centers
- Academic Focus: Center for Brain, Behavior and Metabolism (CBBM)
