Analysis of Inflammation-Related Genes in Patients with Stanford Type A Aortic Dissection.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37373979", "kind": "science-record", "revision": 4, "content_hash": "sha256:6ed5e7b0ce8af70eae09a5f101d793c83797c10a8ca1fff8ec769ddd02a2aba9", "created_at": "2026-11-01T19:02:07Z", "updated_at": "2026-11-01T22:52:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Analysis of Inflammation-Related Genes in Patients with Stanford Type A Aortic Dissection.", "doi": "10.3390/jpm13060990", "pmid": "37373979", "authors": ["Lin Li", "Ziwei Zeng", "Vugar Yagublu", "Nuh Rahbari", "Christoph Reißfelder", "Michael Keese"], "journal": "Journal of personalized medicine", "year": 2023, "abstract": "Background: Aortic dissection (AD) is a life-threatening cardiovascular disease. Pathophysiologically, it has been shown that aortic wall inflammation promotes the occurrence and development of aortic dissection. Thus, the aim of the current research was to determine the inflammation-related biomarkers in AD. Methods: In this study, we conducted differentially expressed genes (DEGs) analysis using the GSE153434 dataset containing 10 type A aortic dissection (TAAD) and 10 normal samples downloaded from the Gene Expression Omnibus (GEO) database. The intersection of DEGs and inflammation-related genes was identified as differential expressed inflammation-related genes (DEIRGs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for DEIRGs. We then constructed the protein-protein interaction (PPI) network using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and identified hub genes using the Cytoscape plugin MCODE. Finally, least absolute shrinkage and selection operator (LASSO) logistic regression was used to construct a diagnostic model. Results: A total of 1728 DEGs were identified between the TAAD and normal samples. Thereafter, 61 DEIRGs are obtained by taking the intersection of DEGs and inflammation-related genes. The GO indicated that DEIRGs were mainly enriched in response to lipopolysaccharide, in response to molecules of bacterial origin, secretory granule membrane, external side of plasma, receptor ligand activity, and signaling receptor activator activity. KEGG analysis indicated that DEIRGs were mainly enriched in cytokine-cytokine receptor interaction, TNF signaling pathway, and proteoglycans in cancer. We identified MYC, SELL, HIF1A, EDN1, SERPINE1, CCL20, IL1R1, NOD2, TLR2, CD69, PLAUR, MMP14, and HBEGF as hub genes using the MCODE plug-in. The ROC indicated these genes had a good diagnostic performance for TAAD. Conclusion: In conclusion, our study identified 13 hub genes in the TAAD. This study will be of significance for the future development of a preventive therapy of TAAD.", "claims": [{"id": "claim-method", "statement": "The DEGs between TAAD and normal samples in the GSE153434 dataset were evaluated using the “DESeq2” [7] R package in the R software (1.4.1023). Finally, least absolute shrinkage and selection operator (LASSO) logistic regression was used to construct a diagnostic model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37373979", "locator": "sections[4] \"2.2. Screening of DEGs\" paragraph 2", "quote": "The DEGs between TAAD and normal samples in the GSE153434 dataset were evaluated using the “DESeq2” [7] R package in the R software (1.4.1023)."}, {"asset": "source:pmid:37373979", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Finally, least absolute shrinkage and selection operator (LASSO) logistic regression was used to construct a diagnostic model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All datasets of this study are available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/, GSE153434 and GSE52093, accessed on 1 October 2022).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37373979", "locator": "sections[25] \"Data Availability Statement\" paragraph 2", "quote": "All datasets of this study are available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/, GSE153434 and GSE52093, accessed on 1 October 2022)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study established a LASSO diagnostic model based on HIF1A, SERPINE1, TLR2, CD69, MMP14, and HBEGF that achieved an AUC of 1 in both the training and external validation datasets.", "kind": "quantitative", "target": {"value": 1.0, "unit": "auc", "tolerance": null, "source": "The ROC curve indicated that the model had a high accuracy (AUC = 1, Figure 9C)."}, "evidence": [{"asset": "source:pmid:37373979", "locator": "sections[18] \"3.7. LASSO Diagnostic Model for TAAD Was Established Based on Hub Genes\" paragraph 2", "quote": "Thus, we constructed a Lasso diagnostic model based on HIF1A, SERPINE1, TLR2, CD69, MMP14, and HBEGF (Figure 9A,B)."}, {"asset": "source:pmid:37373979", "locator": "sections[18] \"3.7. LASSO Diagnostic Model for TAAD Was Established Based on Hub Genes\" paragraph 2", "quote": "The ROC curve indicated that the model had a high accuracy (AUC = 1, Figure 9C)."}, {"asset": "source:pmid:37373979", "locator": "sections[18] \"3.7. LASSO Diagnostic Model for TAAD Was Established Based on Hub Genes\" paragraph 2", "quote": "Furthermore, we tested the LASSO diagnostic model in an external dataset (GSE52093). The ROC curve (AUC = 1), the PR curve, and the DCA curve verified that the model maintained high diagnostic accuracy (Figure 9F–H)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study established a LASSO diagnostic model based on HIF1A, SERPINE1, TLR2, CD69, MMP14, and HBEGF that achieved an AUC of 1 in both the training and external validation datasets.", "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:37373979"], "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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