Transcriptomics profiling of Parkinson's disease progression subtypes reveals distinctive patterns of gene expression.
Background: Parkinson's Disease (PD) varies widely among individuals, and Artificial Intelligence (AI) has recently helped to identify three disease progression subtypes. While their clinical features are already known, their gene expression profiles remain unexplored. Objectives: The objectives of...
| Publicado en: | Journal of Central Nervous System Disease pp. 1 - 18 |
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| Autores principales: | , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
1/27/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182500982&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182500982 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11795735 B3L8 jtl: Journal of Central Nervous System Disease issn: 11795735 maglogo: Y pubinfo: dt: 1/27/2025 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 182500982 182500982 182500982 10.1177/11795735241286821 182500982 ppf: 1 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Transcriptomics profiling of Parkinson's disease progression subtypes reveals distinctive patterns of gene expression. aug: au: Fabrizio, Carlo Termine, Andrea Caltagirone, Carlo affil: Data Science Unit, 9362 Santa Lucia Foundation IRCCS, Rome, Italy sug: subj: Gene Expression Profiling Parkinson Disease Familial and Genetic Disease Progression Familial and Genetic Gene Expression Evaluation Parkinson Disease Classification Disease Progression Risk Factors Risk Assessment Boosting Machine Learning Algorithms Genetic Risk Score Prediction Algorithms Human Retrospective Design Record Review Prospective Studies Machine Learning Algorithms RNA Sequence Analysis Individualized Medicine Funding Source Parkinson Disease Prognosis ROC Curve Cell Physiology Energy Metabolism Genetic Markers ab: Background: Parkinson's Disease (PD) varies widely among individuals, and Artificial Intelligence (AI) has recently helped to identify three disease progression subtypes. While their clinical features are already known, their gene expression profiles remain unexplored. Objectives: The objectives of this study were (1) to describe the transcriptomics characteristics of three PD progression subtypes identified by AI, and (2) to evaluate if gene expression data can be used to predict disease subtype at baseline. Design: This is a retrospective longitudinal cohort study utilizing the Parkinson's Progression Markers Initiative (PPMI) database. Methods: Whole blood RNA-Sequencing data underwent differential gene expression analysis, followed by multiple pathway analyses. A Machine Learning (ML) classifier, namely XGBoost, was trained using data from multiple modalities, including gene expression values. Results: Our study identified differentially expressed genes (DEGs) that were uniquely associated with Parkinson's disease (PD) progression subtypes. Importantly, these DEGs had not been previously linked to PD. Gene-pathway analysis revealed both distinct and shared characteristics between the subtypes. Notably, two subtypes displayed opposite expression patterns for pathways involved in immune response alterations. In contrast, the third subtype exhibited a more unique profile characterized by increased expression of genes related to detoxification processes. All three subtypes showed a significant modulation of pathways related to the regulation of gene expression, metabolism, and cell signaling. ML revealed that the progression subtype with the worst prognosis can be predicted at baseline with 0.877 AUROC, yet the contribution of gene expression was marginal for the prediction of the subtypes. Conclusion: This study provides novel information regarding the transcriptomics profiles of PD progression subtypes, which may foster precision medicine with relevant indications for a finer-grained diagnosis and prognosis. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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