Copyright (c) 2023 Zhe Zhong, Huiqing Wang, Min Ye, Fuling Yan
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The undersigned hereby assign all rights, included but not limited to copyright, for this manuscript to CMB Association upon its submission for consideration to publication on Cellular and Molecular Biology. The rights assigned include, but are not limited to, the sole and exclusive rights to license, sell, subsequently assign, derive, distribute, display and reproduce this manuscript, in whole or in part, in any format, electronic or otherwise, including those in existence at the time this agreement was signed. The authors hereby warrant that they have not granted or assigned, and shall not grant or assign, the aforementioned rights to any other person, firm, organization, or other entity. All rights are automatically restored to authors if this manuscript is not accepted for publication.Significance of Cuproptosis-related genes in immunological characterization, diagnosis and clusters classification in Parkinson's disease
Corresponding Author(s) : Min Ye
Cellular and Molecular Biology,
Vol. 69 No. 12: New discoveries in gene expression and mutation
Abstract
Parkinson's disease (PD) is a progressive neurological disorder that affects millions of people throughout the world. Cuproptosis is a newly discovered form of programmed cell death linked to several neurological disorders. Nevertheless, the precise mechanisms of Cuproptosis-related genes (CRGs) in PD remain unknown. This study investigated immune infiltration and CRG expression profiling in patients with Parkinson's disease and healthy controls. Subsequently, we construct a predictive model based on 5 significant CRGs. The performance of the predictive model was validated by nomograms and external datasets. Additionally, we classified PD patients into two clusters based on CRGs and three gene clusters based on differentially expressed genes (DEG) of CRGs clusters. We further evaluated immunological characterization between the different clusters and created the CRGs scores to quantify CRGs patterns. Finally, we investigate the prediction of CRGs drugs and the ceRNA network, providing new insights into the pathogenesis and management of PD.
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