{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13"],"submitter":["He B"],"pubmed_abstract":["<h4>Background</h4>Aortic disease (aortic aneurysm (AA), dissection (AD)) is a serious threat to patient lives. Little is currently known about the molecular mechanisms and immune infiltration patterns underlying the development and progression of thoracic and abdominal aortic aneurysms (TAA and AAA), warranting further research.<h4>Methods</h4>We downloaded AA (includes TAA and AAA) datasets from the GEO database. The potential biomarkers in TAA and AAA were identified using differential expression analysis and two machine-learning algorithms. The discrimination power of the potential biomarkers and their diagnostic accuracy was assessed in validation datasets using ROC curve analysis. Then, GSEA, KEGG, GO and DO analyses were conducted. Furthermore, two immuno-infiltration analysis algor"],"journal":["Frontiers in immunology"],"pagination":["1030976"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9633949"],"repository":["biostudies-literature"],"pubmed_title":["Common molecular mechanism and immune infiltration patterns of thoracic and abdominal aortic aneurysms."],"pmcid":["PMC9633949"],"pubmed_authors":["Pan X","Liu Y","Cai C","Huang D","Li Z","Liu L","Wei Q","Yu D","Quan L","He B","Zhan Y"],"additional_accession":[]},"is_claimable":false,"name":"Common molecular mechanism and immune infiltration patterns of thoracic and abdominal aortic aneurysms.","description":"<h4>Background</h4>Aortic disease (aortic aneurysm (AA), dissection (AD)) is a serious threat to patient lives. Little is currently known about the molecular mechanisms and immune infiltration patterns underlying the development and progression of thoracic and abdominal aortic aneurysms (TAA and AAA), warranting further research.<h4>Methods</h4>We downloaded AA (includes TAA and AAA) datasets from the GEO database. The potential biomarkers in TAA and AAA were identified using differential expression analysis and two machine-learning algorithms. The discrimination power of the potential biomarkers and their diagnostic accuracy was assessed in validation datasets using ROC curve analysis. Then, GSEA, KEGG, GO and DO analyses were conducted. Furthermore, two immuno-infiltration analysis algor","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2026-06-20T03:20:30.818Z","creation":"2025-02-19T00:31:50.439Z"},"accession":"S-EPMC9633949","cross_references":{"pubmed":["36341412"],"doi":["10.3389/fimmu.2022.1030976"]}}