{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T11:52:04Z","timestamp":1790596324221,"version":"4.1.0"},"reference-count":44,"publisher":"American Institute of Mathematical Sciences (AIMS)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MBE"],"published-print":{"date-parts":[[2022]]},"abstract":"<abstract>\n\t\t\t<p><italic>Background<\/italic>: Calcific aortic valve stenosis (CAVS) is a crucial cardiovascular disease facing aging societies. Our research attempts to identify immune-related genes through bioinformatics and machine learning analysis. Two machine learning strategies include Least Absolute Shrinkage Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE). In addition, we deeply explore the role of immune cell infiltration in CAVS, aiming to study the potential therapeutic targets of CAVS and explore possible drugs. <italic>Methods<\/italic>: Download three data sets related to CAVS from the Gene Expression Omnibus. Gene set variation analysis (GSVA) looks for potential mechanisms, determines differentially expressed immune-related genes (DEIRGs) by combining the ImmPort database with CAVS differential genes, and explores the functions and pathways of enrichment. Two machine learning methods, LASSO and SVM-RFE, screen key immune signals and validate them in external data sets. Single-sample GSEA (ssGSEA) and CIBERSORT analyze the subtypes of immune infiltrating cells and integrate the analysis with DEIRGs and key immune signals. Finally, the possible targeted drugs are analyzed through the Connectivity Map (CMap). <italic>Results<\/italic>: GSVA analysis of the gene set suggests that it is highly correlated with multiple immune pathways. 266 differential genes (DEGs) integrate with immune genes to obtain 71 DEIRGs. Enrichment analysis found that DEIRGs are related to oxidative stress, synaptic membrane components, receptor activity, and a variety of cardiovascular diseases and immune pathways. Angiotensin II Receptor Type 1(<italic>AGTR1<\/italic>), Phospholipid Transfer Protein (<italic>PLTP<\/italic>), Secretogranin II (<italic>SCG2<\/italic>) are identified as key immune signals of CAVS by machine learning. Immune infiltration found that B cells na\u00ef ve and Macrophages M2 are less in CAVS, while Macrophages M0 is more in CAVS. Simultaneously, <italic>AGTR1<\/italic>, <italic>PLTP<\/italic>, <italic>SCG2<\/italic> are highly correlated with a variety of immune cell subtypes. CMap analysis found that isoliquiritigenin, parthenolide, and pyrrolidine-dithiocarbamate are the top three targeted drugs related to CAVS immunity. <italic>Conclusion<\/italic>: The key immune signals, immune infiltration and potential drugs obtained from the research play a vital role in the pathophysiological progress of CAVS.<\/p>\n\t\t<\/abstract><\/jats:p>","DOI":"10.3934\/mbe.2022174","type":"journal-article","created":{"date-parts":[[2022,2,12]],"date-time":"2022-02-12T10:06:08Z","timestamp":1644660368000},"page":"3787-3802","source":"Crossref","is-referenced-by-count":5,"title":["<i>AGTR1<\/i>, <i>PLTP<\/i>, and <i>SCG2<\/i> associated with immune genes and immune cell infiltration in calcific aortic valve stenosis: analysis from integrated bioinformatics and machine learning"],"prefix":"10.3934","volume":"19","author":[{"given":"Chenyang","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weidong","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"Department of Cardiology, Nantong Traditional Chinese Medicine Hospital, Nantong 226001, China","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2321","reference":[{"key":"key-10.3934\/mbe.2022174-1","doi-asserted-by":"publisher","unstructured":"P. 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