ArticleFrontiers in neuroscience2023
Identification of necroptosis-related genes in Parkinson's disease by integrated bioinformatics analysis and experimental validation.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 22 citations in OpenAlex.
- Deciphering the rules of regulated cell death subroutines by in silico methods.Journal of advanced research · 2026Review
- Ferroptosis-Based Peripheral Immune Dysregulation and Diagnostic Signatures in Parkinson's Disease: An RNA Transcriptomic and Single-Cell Immune Sequencing Analysis.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- ASGR2 and CLEC12A as Prognostically Relevant C-Type Lectin Hubs in Glioblastoma.International journal of molecular sciences · 2026Article
- Elucidate biomarkers and the molecular pathways associated with genetic variants that contribute to the etiology of Parkinson's disease.Acta neurologica Belgica · 2025Article
- The metabolic crossroads: glycolysis in cancer and neurodegeneration.BMC neurology · 2025Article
- Article
- Understanding the Pre-Clinical Stages of Parkinson's Disease: Where Are We in Clinical and Research Settings?International journal of molecular sciences · 2025Review
- Exploring the mechanism of metabolic cell death-related genes AKR1C2 and MAP1LC3A as biomarkers in Parkinson's disease.Scientific reports · 2025Article
- An integrated bioinformatics and machine learning approach to identifying biomarkers connecting parkinson's disease with purine metabolism-related genes.BMC neurology · 2025Article
- Construction and Identification of Inflammation-Related TF-mRNA-miRNA Coexpression Network and Immune Infiltration in Parkinson's Disease.Parkinson's disease · 2025Article
- Identification of biomarkers associated with inflammatory response in Parkinson's disease by bioinformatics and machine learning.PloS one · 2025Article
- The interplay between α-synuclein aggregation and necroptosis in Parkinson's disease: a spatiotemporal perspective.Frontiers in neuroscience · 2025Review
- Early concentrate starter introduction induces rumen epithelial parakeratosis by blocking keratinocyte differentiation with excessive ruminal butyrate accumulation.Journal of advanced research · 2024Article
- Potential Effects of Low-Level Toluene Exposure on the Nervous System of Mothers and Infants.International journal of molecular sciences · 2024Article
- Integrated bioinformatics analysis for exploring potential biomarkers related to Parkinson's disease progression.BMC medical genomics · 2024Article
- Screening of genes co-associated with osteoporosis and chronic HBV infection based on bioinformatics analysis and machine learning.Frontiers in immunology · 2024Article
- Parkinson's disease models and death signaling: what do we know until now?Frontiers in neuroanatomy · 2024Review
- Die Hard: Necroptosis and its Impact on Age-Dependent Neuroinflammatory Diseases.Frontiers in cell death · 2024Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Parkinson's disease (PD) is the second most common neurodegeneration disease worldwide. Necroptosis, which is a new form of programmed cell death with high relationship with inflammation, plays a vital role in the progression of PD. However, the key necroptosis related genes in PD are not fully elucidated. Purpose: Identification of key necroptosis-related genes in PD. Method: The PD associated datasets and necroptosis related genes were downloaded from the GEO Database and GeneCards platform, respectively. The DEGs associated with necroptosis in PD were obtained by gap analysis, and followed by cluster analysis, enrichment analysis and WGCNA analysis. Moreover, the key necroptosis related genes were generated by PPI network analysis and their relationship by spearman correlation analysis. Immune infiltration analysis was used for explore the immune state of PD brain accompanied with the expression levels of these genes in various types of immune cells. Finally, the gene expression levels of these key necroptosis related genes were validated by an external dataset, blood samples from PD patients and toxin-induced PD cell model using real-time PCR analysis. Result: Twelve key necroptosis-related genes including ASGR2, CCNA1, FGF10, FGF19, HJURP, NTF3, OIP5, RRM2, SLC22A1, SLC28A3, WNT1 and WNT10B were identified by integrated bioinformatics analysis of PD related dataset GSE7621. According to the correlation analysis of these genes, RRM2 and WNT1 were positively and negatively correlated with SLC22A1 respectively, while WNT10B was positively correlated with both OIF5 and FGF19. As the results from immune infiltration analysis, M2 macrophage was the highest population of immune cell in analyzed PD brain samples. Moreover, we found that 3 genes (CCNA1, OIP5 and WNT10B) and 9 genes (ASGR2, FGF10, FGF19, HJURP, NTF3, RRM2, SLC22A1, SLC28A3 and WNT1) were down- and up- regulated in an external dataset GSE20141, respectively. All the mRNA expression levels of these 12 genes were obviously upregulated in 6-OHDA-induced SH-SY5Y cell PD model while CCNA1 and OIP5 were up- and down- regulated, respectively, in peripheral blood lymphocytes of PD patients. Conclusion: Necroptosis and its associated inflammation play fundamental roles in the progression of PD and these identified 12 key genes might be served as new diagnostic markers and therapeutic targets for PD.
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