Identification of immune signatures in Parkinson's disease based on co-expression networks.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36733342", "kind": "science-record", "revision": 3, "content_hash": "sha256:8e36464f16b5774d910aeb1fbce17dcd49b98b4e4922346be7a4987c0fa68180", "created_at": "2026-11-01T19:01:18Z", "updated_at": "2026-11-01T22:08:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of immune signatures in Parkinson's disease based on co-expression networks.", "doi": "10.3389/fgene.2023.1090382", "pmid": "36733342", "authors": ["Xiaolin Dong", "Yanping Li", "Qingyun Li", "Wenhao Li", "Gang Wu"], "journal": "Frontiers in genetics", "year": 2023, "abstract": "Parkinson's disease (PD) is a common neurodegenerative disease in middle-aged and elderly people, and there is less research on the relationship between immunity and PD. In this study, the protein-protein interaction networks (PPI) data, 2747 human immune-related genes (HIRGs), 2078 PD-related genes (PDRGs), and PD-related datasets (GSE49036 and GSE20292) were downloaded from the Human Protein Reference Database (HPRD), Amigo 2, DisGeNET, and Gene Expression Omnibus (GEO) databases, respectively. An immune- or PD-directed neighbor co-expressed network construction (IOPDNC) was drawn based on the GSE49036 dataset and HPRD database. Furthermore, a PD-directed neighbor co-expressed network was constructed. Modular clustering analysis was performed on the genes of the gene interaction network obtained in the first step to obtain the central core genes using the GraphWeb online website. The modules with the top 5 functional scores and the number of core genes greater than six were selected as PD-related gene modules. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of different module genes were performed. The single sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to calculate the immune cell infiltration of the PD and the normal samples. The quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) was performed to investigate the expression of module genes. An IOPDNC and PD-directed neighbor co-expressed network (PDNC network) were constructed. Furthermore, a total of 5 immune-PD modules were identified which could distinguish between PD and normal samples, and these module genes were strongly related to PD in protein interaction level or gene expression level. In addition, functional analysis indicated that module genes were involved in various neurodegenerative diseases, such as Alzheimer disease, Huntington disease, Parkinson disease, and Long-term depression. In addition, the genes of the 6 modules were significantly associated with these 4 differential immune cells (aDC cells, eosinophils, neutrophils, and Th2 cells). Finally, the result of qRT-PCR manifested that the expression of 6 module genes was significantly higher in normal samples than in PD samples. In our study, the immune-related genes were found to be strongly related to PD and might play key roles in PD.", "claims": [{"id": "claim-method", "statement": "Based on the 24 immune cell sets, the single sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to calculate the immune cell infiltration of the PD and normal samples, and the rank sum test was used to analyze the immune differences between the PD and normal samples of cell infiltration.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36733342", "locator": "sections[8] \"Differential analysis of immune cell infiltration by modular genes\" paragraph 2", "quote": "Based on the 24 immune cell sets, the single sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to calculate the immune cell infiltration of the PD and normal samples, and the rank sum test was used to analyze the immune differences between the PD and normal samples of cell infiltration."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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