Transcriptomics profiling of Parkinson's disease progression subtypes reveals distinctive patterns of gene expression.

ABSTRACT:

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.

SUBMITTER: Fabrizio C 

PROVIDER: S-EPMC11791511 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature