Identification of significant immune-related genes for epilepsy via bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34430602", "kind": "science-record", "revision": 7, "content_hash": "sha256:7f8cc6f6632441fdc04898fd8a6a7ab7515acf0588f4de643b96429f1aa26e63", "created_at": "2026-11-01T15:41:49Z", "updated_at": "2026-11-01T22:04:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of significant immune-related genes for epilepsy via bioinformatics analysis.", "doi": "10.21037/atm-21-2792", "pmid": "34430602", "authors": ["Xiaodan Luo", "Tao Xiang", "Hongmi Huang", "Lin Ye", "Yifei Huang", "Yuan Wu"], "journal": "Annals of translational medicine", "year": 2021, "abstract": "BACKGROUND: Epilepsy is one of the most common neurological disorders, but its underlying mechanism has remained obscure, and the role of immune-related genes (IRGs) in epilepsy have not yet been investigated. Therefore, in this study, we explored the association between IRGs and epilepsy.\nMETHODS: An IRG list was collected from the ImmPort database. The gene expression profiles of GSE143272 were collected from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). Differentially expressed genes (DEGs) between epilepsy and normal samples were analyzed, and the intersections between IRGs and DEGs were identified using the VennDiagram package, with the intersected genes subjected to further analysis. Enrichment function for intersected genes were performed, constructed a protein-protein interaction (PPI) network via the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and the hub genes (top 10) of the PPI network were calculated by the cytoHubba plug-in in Cytoscape. The top correlated genes were selected to perform correlation analysis with immune cells infiltration and expression levels. Finally, we performed validation of the top correlated genes transcriptional expression levels using an animal model.\nRESULTS: There were a total of 245 DEGs detected in GSE143272, among which 143 were upregulated and 102 downregulated genes in epilepsy. A total of 44 differential IRGs were obtained via intersection of DEGs and IRGs. Enrichment function analysis of DEGs showed that they played a significant role in immune response. The gene CXCL1 was the most correlated with other differentially expressed IRGs via the PPI network. The results of immune cell infiltration analysis indicated that epilepsy patients had higher activated mast cells infiltration (P=0.021), but lower activated CD4 memory T cells (P=0.001), resting CD4 memory T cells (P=0.011), and gamma delta T cells (P=0.038) infiltration. It was revealed that CXCL1 and activated mast cells (R=0.25, P=0.019) and neutrophils (R=0.3, P=0.0043), and a negative correlation with T cells gamma delta (R=-0.25, P=0.018). The levels of CXCL1 expression were significantly lower in epilepsy patients than those in normal samples.\nCONCLUSIONS: In this study, the results showed that IRGs such as CXCL1 have a significant influence on epilepsy via regulation of immune cells infiltration.", "claims": [{"id": "claim-data-access", "statement": "Data Sharing Statement: Available at https://dx.doi.org/10.21037/atm-21-2792", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34430602", "locator": "sections[25] \"(untitled)\" paragraph 1", "quote": "Data Sharing Statement: Available at https://dx.doi.org/10.21037/atm-21-2792"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:34430602"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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