Identification of Cuproptosis Clusters and Integrative Analyses in Parkinson’s Disease

Abstract

Parkinson’s disease (PD) is the second most common neurodegenerative disease; it mainly occurs in the elderly population. Cuproptosis is a newly discovered form of regulated cell death involved in the progression of various diseases. Combining multiple GEO datasets, we analyzed the expression profile and immunity of cuproptosis-related genes (CRGs) in PD. Dysregulated CRGs and differential immune responses were identified between PD and non-PD substantia nigra. Two CRG clusters were defined in PD. Immune analysis suggested that CRG cluster 1 was characterized by a high immune response. The enrichment analysis showed that CRG cluster 1 was significantly enriched in immune activation pathways, such as the Notch pathway and the JAK-STAT pathway. KIAA0319, AGTR1, and SLC18A2 were selected as core genes based on the LASSO analysis. We built a nomogram that can predict the occurrence of PD based on the core genes. Further analysis found that the core genes were significantly correlated with tyrosine hydroxylase activity. This study systematically evaluated the relationship between cuproptosis and PD and established a predictive model for assessing the risk of cuproptosis subtypes and the outcome of PD patients. This study provides a new understanding of PD-related molecular mechanisms and provides new insights into the treatment of PD.

Identification of cuproptosis-realated key genes and pathways in Parkinson’s disease via bioinformatics analysis

“…Another study showed that CRGs were significantly enriched in immunity-related pathways like JAK-STAT and Notch pathway. Based on the LASSO analysis, core genes were identified and utilized in a prognostic prediction model [ 19 ]. Recently, Zhang et al found that Cu exposed to mice lead to neuronal degeneration and promoted the expression of cuproptosis-related proteins ferredoxin 1 and dihydrolipoamide S-acetyltransferase, providing experimental evidence for the involvement of cuproptosis in neurodegenerative diseases [ 59 ].…”

“…Currently, there are few bioinformatic analyses targeting cuproptosis in PD as mentioned previously [18][19][20].A recent study was dedicated to exploring the potential CRGs, the immune infiltration patterns in PD, and consequently, the associated copper chelators therapy [18]. Another study showed that CRGs were significantly enriched in immunity-related pathways like JAK-STAT and Notch pathway.…”

“…and utilized in a prognostic prediction model [19]. Recently, Zhang et al found that Cu exposed to mice lead to neuronal degeneration and promoted the expression of cuproptosisrelated proteins ferredoxin 1 and dihydrolipoamide S-acetyltransferase, providing experimental evidence for the involvement of cuproptosis in neurodegenerative diseases [59].…”

“…Predicting key differentially expressed genes (DEGs) for disease by comprehensive biochemical computational methods is a prevalent approach that efficiently and rapidly offers molecular insights into the disease utilizing a variety of resources of sequencing results in a high-throughput manner. Currently, there have been some studies predicting the cuproptosisrelated biomarkers of the neurodegenerative diseases based on bioinformatics tools [17][18][19][20]. Lai et al [17] combined the weighted gene co-expression network analysis (WGCNA) algorithm with several machine models such as random forest model to explore the cuproptosisrelated molecular clusters and construct a prediction model for AD.…”

“…Lai et al [17] combined the weighted gene co-expression network analysis (WGCNA) algorithm with several machine models such as random forest model to explore the cuproptosisrelated molecular clusters and construct a prediction model for AD. As for PD, few studies [18][19][20] aimed to discover the DEGs about cuproptosis by means of different computational biological methods. A recent bioinformatics analysis [20] found that the cuprotosis-related key genes intervened in the progression of PD through the integration of DEGs, WGCNA-related significant module genes (with the soft threshold of 10) and immune cell infiltration, resulting in three key genes different from our study, which might be attributed to the different selection of parameters.…”