Identification of pyroptosis-associated genes with diagnostic value in calcific aortic valve disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38333413", "kind": "science-record", "revision": 3, "content_hash": "sha256:cfe40dcad332eb9e66acbfab0d053409e960ecc85753028e7fb828969c4dbbd4", "created_at": "2026-11-01T19:20:16Z", "updated_at": "2026-11-01T22:11:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of pyroptosis-associated genes with diagnostic value in calcific aortic valve disease.", "doi": "10.3389/fcvm.2024.1340199", "pmid": "38333413", "authors": ["Chenxi Yu", "Yifeng Zhang", "Ling Yang", "Mirenuer Aikebaier", "Shuyao Shan", "Qing Zha", "Ke Yang"], "journal": "Frontiers in cardiovascular medicine", "year": 2024, "abstract": "Background: Calcific aortic valve disease (CAVD) is one of the most prevalent valvular diseases and is the second most common cause for cardiac surgery. However, the mechanism of CAVD remains unclear. This study aimed to investigate the role of pyroptosis-related genes in CAVD by performing comprehensive bioinformatics analysis.\nMethods: Three microarray datasets (GSE51472, GSE12644 and GSE83453) and one RNA sequencing dataset (GSE153555) were obtained from the Gene Expression Omnibus (GEO) database. Pyroptosis-related differentially expressed genes (DEGs) were identified between the calcified and the normal valve samples. LASSO regression and random forest (RF) machine learning analyses were performed to identify pyroptosis-related DEGs with diagnostic value. A diagnostic model was constructed with the diagnostic candidate pyroptosis-related DEGs. Receiver operating characteristic (ROC) curve analysis was performed to estimate the diagnostic performances of the diagnostic model and the individual diagnostic candidate genes in the training and validation cohorts. CIBERSORT analysis was performed to estimate the differences in the infiltration of the immune cell types. Pearson correlation analysis was used to investigate associations between the diagnostic biomarkers and the immune cell types. Immunohistochemistry was used to validate protein concentration.\nResults: We identified 805 DEGs, including 319 down-regulated genes and 486 up-regulated genes. These DEGs were mainly enriched in pathways related to the inflammatory responses. Subsequently, we identified 17 pyroptosis-related DEGs by comparing the 805 DEGs with the 223 pyroptosis-related genes. LASSO regression and RF algorithm analyses identified three CAVD diagnostic candidate genes (TREM1, TNFRSF11B, and PGF), which were significantly upregulated in the CAVD tissue samples. A diagnostic model was constructed with these 3 diagnostic candidate genes. The diagnostic model and the 3 diagnostic candidate genes showed good diagnostic performances with AUC values >0.75 in both the training and the validation cohorts based on the ROC curve analyses. CIBERSORT analyses demonstrated positive correlation between the proportion of M0 macrophages in the valve tissues and the expression levels of TREM1, TNFRSF11B, and PGF.\nConclusion: Three pyroptosis-related genes (TREM1, TNFRSF11B and PGF) were identified as diagnostic biomarkers for CAVD. These pyroptosis genes and the pro-inflammatory microenvironment in the calcified valve tissues are potential therapeutic targets for alleviating CAVD.", "claims": [{"id": "claim-method", "statement": "After identifying the pyroptosis-related DEGs, least absolute shrinkage and selection operator (LASSO) regression analysis and random forest (RF) machine learning algorithm were used to further filter the candidate genes and select the variables for CAVD.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38333413", "locator": "sections[6] \"Machine learning and the diagnostic model construction\" paragraph 2", "quote": "After identifying the pyroptosis-related DEGs, least absolute shrinkage and selection operator (LASSO) regression analysis and random forest (RF) machine learning algorithm were used to further filter the candidate genes and select the variables for CAVD."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Gene Expression Omnibus, GSE51472, GSE12644 and GSE153555.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38333413", "locator": "sections[24] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Gene Expression Omnibus, GSE51472, GSE12644 and GSE153555."}], "state": "complete"}, {"id": "claim-target-result", "statement": "LASSO regression and random forest analyses identified TREM1, TNFRSF11B, and PGF as candidate diagnostic genes for calcified aortic valve disease.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38333413", "locator": "sections[14] \"Identification of diagnostic genes for CAVD\" paragraph 2", "quote": "LASSO regression analysis was performed and nine candidate diagnostic genes were identified for CAVD (Figures 5A,B)."}, {"asset": "source:pmid:38333413", "locator": "sections[14] \"Identification of diagnostic genes for CAVD\" paragraph 2", "quote": "Subsequently, we overlapped candidate genes from the LASSO model and the RF algorithm and identified three common candidate diagnostic genes, TREM1, TNFRSF11B, and PGF (Figure 5D)."}, {"asset": "source:pmid:38333413", "locator": "sections[15] \"Diagnostic performance of the model and potential biomarkers in detecting CAVD\" paragraph 4", "quote": "The diagnostic performance of the model was highly favorable for detecting CAVD, with an AUC value of 1.00 in the training set, an AUC value of 0.93 in the GSE153555 dataset and an AUC value of 0.92 in the GSE83453 dataset (Figure 7D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "LASSO regression and random forest analyses identified TREM1, TNFRSF11B, and PGF as candidate diagnostic genes for calcified aortic valve disease.", "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:38333413"], "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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Methods: Three microarray datasets (GSE51472, GSE12644 and GSE83453) a", "www.ncbi.nlm.nih.gov/geo/). Among these, two microarray datasets, GSE51472 and GSE12644, included 30", "used as the training dataset (13). The third microarray dataset, GSE83453, included 27 samples (8 no"], "order_basis": "paper_text_order:403", "id": "assay:1"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ts (GSE51472, GSE12644 and GSE83453) and one RNA sequencing dataset (GSE153555) were obtained from the G", "valves and 19 calcific aortic valves) and an RNA sequencing dataset, GSE153555, included 20 samples (10", "c valves and 10 calcific aortic valves). 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