{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["14"],"submitter":["Deng B"],"pubmed_abstract":["<h4>Background</h4>Endoplasmic reticulum stress (ERS) is a critical factor in the development of ulcerative colitis (UC); however, the underlying molecular mechanisms remain unclear. This study aims to identify pivotal molecular mechanisms related to ERS in UC pathogenesis and provide novel therapeutic targets for UC.<h4>Methods</h4>Colon tissue gene expression profiles and clinical information of UC patients and healthy controls were obtained from the Gene Expression Omnibus (GEO) database, and the ERS-related gene set was downloaded from GeneCards for analysis. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were utilized to identify pivotal modules and genes associated with UC. A consensus clustering algorithm was used to classify UC patients. T"],"journal":["Frontiers in immunology"],"pagination":["1158648"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10243217"],"repository":["biostudies-literature"],"pubmed_title":["Comprehensive analysis of endoplasmic reticulum stress-associated genes signature of ulcerative colitis."],"pmcid":["PMC10243217"],"pubmed_authors":["Deng B","Liu Y","Dong W","Liao F","Wei S","Liu C","He P"],"additional_accession":[]},"is_claimable":false,"name":"Comprehensive analysis of endoplasmic reticulum stress-associated genes signature of ulcerative colitis.","description":"<h4>Background</h4>Endoplasmic reticulum stress (ERS) is a critical factor in the development of ulcerative colitis (UC); however, the underlying molecular mechanisms remain unclear. This study aims to identify pivotal molecular mechanisms related to ERS in UC pathogenesis and provide novel therapeutic targets for UC.<h4>Methods</h4>Colon tissue gene expression profiles and clinical information of UC patients and healthy controls were obtained from the Gene Expression Omnibus (GEO) database, and the ERS-related gene set was downloaded from GeneCards for analysis. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were utilized to identify pivotal modules and genes associated with UC. A consensus clustering algorithm was used to classify UC patients. T","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023","modification":"2026-06-24T03:28:02.919Z","creation":"2025-02-19T02:38:04.394Z"},"accession":"S-EPMC10243217","cross_references":{"pubmed":["37287987"],"doi":["10.3389/fimmu.2023.1158648"]}}