Identification of Immune-Related Gene Signature in Stanford Type A Aortic Dissection.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35795203", "kind": "science-record", "revision": 5, "content_hash": "sha256:eda4940e9c4431d762836e7db1e88a27341df6d2902ee663f0e23fccb19811ba", "created_at": "2026-11-01T18:14:50Z", "updated_at": "2026-11-01T22:49:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Immune-Related Gene Signature in Stanford Type A Aortic Dissection.", "doi": "10.3389/fgene.2022.911750", "pmid": "35795203", "authors": ["Zhaoshui Li", "Jumiao Wang", "Qiao Yu", "Ruxin Shen", "Kun Qin", "Yu Zhang", "Youjin Qiao", "Yifan Chi"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Stanford type A aortic dissection (ATAAD) is a common life-threatening event in the aorta. Recently, immune disorder has been linked to the risk factors that cause ATAAD at the molecular level. However, the specific immune-related gene signature during the progression is unclear. Methods: The GSE52093 and GSE98770 datasets related to ATAAD from the Gene Expression Omnibus (GEO) database were acquired. The immune gene expression levels were analyzed by single sample gene set enrichment analysis (ssGSEA). The correlations between gene networks and immune scores were determined by weighted gene correlation network analysis (WGCNA). The different immune subgroups were finally divided by consensus clustering. The differentially expressed genes (DEGs) were identified and subsequent functional enrichment analyses were conducted. The hub genes were identified by protein-protein interaction (PPI) network and functional similarities analyses. The immune cell infiltration proportion was determined by the CIBERSORT algorithm. Results: According to the ssGSEA results, the 13 ATAAD samples from the GEO database were divided into high- and low-immune subgroups according to the ssGSEA, WGCNA, and consensus clustering analysis results. Sixty-eight immune-related DEGs (IRDEGs) between the two subgroups were enriched in inflammatory-immune response biological processes, including leukocyte cell-cell adhesion, mononuclear cell migration, and myeloid leukocyte migration. Among these IRDEGs, 8 genes (CXCR4, LYN, CCL19, CCL3L3, SELL, F11R, DPP4, and VAV3) were identified as hub genes that represented immune-related signatures in ATAAD after the PPI and functional similarities analyses. The proportions of infiltrating CD8 T cells and M1 macrophages were significantly higher in ATAAD patients in the immune-high group than the immune-low group. Conclusion: Eight immune-related genes were identified as hub genes representing potential biomarkers and therapeutic targets linked to the immune response in ATAAD patients.", "claims": [{"id": "claim-method", "statement": "For the differentially expressed genes (DEGs) between the high- and low-immunity subgroups, limma R package (Ritchie et al., 2015) was used to identify the significant genes with the threshold of log2FC > 1 and P adj < 0.05.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35795203", "locator": "sections[5] \"Identification of DEGs and the Enrichment Analysis\" paragraph 2", "quote": "For the differentially expressed genes (DEGs) between the high- and low-immunity subgroups, limma R package (Ritchie et al., 2015) was used to identify the significant genes with the threshold of log2FC > 1 and P adj < 0.05."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35795203", "locator": "sections[23] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "According to the ssGSEA results, the 13 ATAAD samples from the GEO database were divided into high- and low-immune subgroups according to the ssGSEA, WGCNA, and consensus clustering analysis results.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35795203", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "According to the ssGSEA results, the 13 ATAAD samples from the GEO database were divided into high- and low-immune subgroups according to the ssGSEA, WGCNA, and consensus clustering analysis results."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "According to the ssGSEA results, the 13 ATAAD samples from the GEO database were divided into high- and low-immune subgroups according to the ssGSEA, WGCNA, and consensus clustering analysis results.", "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:35795203"], "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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