<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>14</volume><submitter>Deng B</submitter><pubmed_abstract>&lt;h4>Background&lt;/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.&lt;h4>Methods&lt;/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</pubmed_abstract><journal>Frontiers in immunology</journal><pagination>1158648</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC10243217</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Comprehensive analysis of endoplasmic reticulum stress-associated genes signature of ulcerative colitis.</pubmed_title><pmcid>PMC10243217</pmcid><pubmed_authors>Deng B</pubmed_authors><pubmed_authors>Liu Y</pubmed_authors><pubmed_authors>Dong W</pubmed_authors><pubmed_authors>Liao F</pubmed_authors><pubmed_authors>Wei S</pubmed_authors><pubmed_authors>Liu C</pubmed_authors><pubmed_authors>He P</pubmed_authors></additional><is_claimable>false</is_claimable><name>Comprehensive analysis of endoplasmic reticulum stress-associated genes signature of ulcerative colitis.</name><description>&lt;h4>Background&lt;/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.&lt;h4>Methods&lt;/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</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023</publication><modification>2026-06-24T03:28:02.919Z</modification><creation>2025-02-19T02:38:04.394Z</creation></dates><accession>S-EPMC10243217</accession><cross_references><pubmed>37287987</pubmed><doi>10.3389/fimmu.2023.1158648</doi></cross_references></HashMap>