Bioinformatics analysis of immune cell infiltration patterns and potential diagnostic markers in atherosclerosis.

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Expression profile datasets (GSE28829 and GSE43292) of patients with atherosclerosis and healthy controls were downloaded from the GEO database. Glutamine (GLN) metabolism-associated genes were obtained from the Molecular Signatures Database (MSigDB). The limma package in R was used to identify differentially expressed genes (DEGs). Significant modules were filtered using Weighted Gene Co-expression Network Analysis (WGCNA). MSigDB sets were subjected to Gene Set Enrichment Analysis and Gene Set Variation Analysis. The biological functions of DEGs were examined using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. STRING and Cytoscape software were used to identify hub genes and functional modules through protein-protein interaction (PPI) network analysis. The xCell software was adopted to assess the composition patterns of immune and stromal cells. Correlation analyses were performed for key genes and immune cell subtypes. We identified 308 DEGs and GLN-associated genes. Functional enrichment analysis showed that these genes were strongly enriched in muscle contract, muscle tissue development, cutile fiber, mycobacterial, and actin binding. Enriched KEGG pathways comprised dilated cardiomyopathy, hypertrophic cardiomyopathy, and the cAMP signaling pathway. In the PPI network analysis, 27 genes were identified as hub genes. The area under the curve (AUC) values of 27 biomarkers were relatively high, indicating high diagnostic values. The atherosclerosis group exhibited a markedly higher degree of infiltration than the control group. This study identified 27 GLN-associated genes as potential biomarkers for the diagnosis of atherosclerosis. It provides a new perspective on immune responses that facilitates exploration of the molecular mechanisms of atherosclerosis.", "claims": [{"id": "claim-method", "statement": "The “limma (version 3.50.0)”36 package in R was adopted to screen DEGs in atherosclerosis (n = 48) and normal (n = 45) samples, with thresholds of p-adjusted < 0.05 and |log2FC|> 0.5 for inclusion in subsequent evaluations. The correlation between two parameters was inferred using Spearman’s correlation analysis. Inter-group differences were compared using Wilcoxon tests. Multi-group (three or more groups) differences were compared using the Kruskal–Wallis test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37963970", "locator": "sections[18] \"DEGs associated with atherosclerosis\" paragraph 2", "quote": "The “limma (version 3.50.0)”36 package in R was adopted to screen DEGs in atherosclerosis (n = 48) and normal (n = 45) samples, with thresholds of p-adjusted < 0.05 and |log2FC|> 0.5 for inclusion in subsequent evaluations."}, {"asset": "source:pmid:37963970", "locator": "sections[27] \"Statistical methods\" paragraph 2", "quote": "The correlation between two parameters was inferred using Spearman’s correlation analysis."}, {"asset": "source:pmid:37963970", "locator": "sections[27] \"Statistical methods\" paragraph 2", "quote": "Inter-group differences were compared using Wilcoxon tests."}, {"asset": "source:pmid:37963970", "locator": "sections[27] \"Statistical methods\" paragraph 2", "quote": "Multi-group (three or more groups) differences were compared using the Kruskal–Wallis test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37963970", "locator": "sections[33] \"Data availability\" paragraph 2", "quote": "The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "This study identified 27 GLN-associated genes as potential biomarkers for the diagnosis of atherosclerosis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37963970", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "This study identified 27 GLN-associated genes as potential biomarkers for the diagnosis of atherosclerosis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "This study identified 27 GLN-associated genes as potential biomarkers for the diagnosis of atherosclerosis.", "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:37963970"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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