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Comprehensive analysis of endoplasmic reticulum stress-associated genes of ulcerative colitis

Published on Nov. 01, 2024Total Views: 695 times Total Downloads: 120 times Download Mobile

Author: LIU Peng 1 ZHAO Lin 1 LI Zhiyuan 1 BAI Yangyang 1 GAO Suya 2 GUO Weisheng 1

Affiliation: 1. Department of General Surgery, Henan Province Hospital of TCM (The Second Affiliated Hospital of Henan University of Chinese Medicine), Zhengzhou 450002, China 2. Department of Gastroenterology, Xinzheng Huaxin Minsheng Hospital (formerly Xinzheng People's Hospital), Xinzheng 451100, Henan Province, China

Keywords: Ulcerative colitis Endoplasmic reticulum stress-associated genes miRNA Gene regulatory network Bioinformatics

DOI: 10.12173/j.issn.1004-4337.202406093

Reference: Liu P, Zhao L, Li ZY, Bai YY, Gao SY, Guo WS. Comprehensive analysis of endoplasmic reticulum stress-associated genes of ulcerative colitis[J]. Journal of Mathematical Medicine, 2024, 37(10): 722-733. DOI: 10.12173/j.issn.1004-4337.202406093. [Article in Chinese]

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Abstract

Objective  To study the endoplasmic reticulum stress (ERS)-associated genes with diagnostic and therapeutic potential for ulcerative colitis (UC) based on bioinformatics and Mendelian randomization analysis. Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Gnomes (KEGG) analysis were performed on relevant genes, and a ceRNA regulatory network was constructed.

Methods  Gene expression profiles and clinical information of colon tissues from UC patients and healthy controls were obtained from Gene Expression Omnibus (GEO) database. Heteroexpression analysis was used to screen for differentially expressed genes related to UC. The ERS-associated gene set was downloaded from GeneCards database for analysis. The intersection of differentially expressed genes and ERS-associated genes was used to obtain differentially expressed ERS-associated genes. GO and KEGG were used to analyze differentially expressed ERS-associated genes. Three methods including Lasso regression, support vector machine (SVM) and random forest tree were used to screen UC disease characteristic genes, the intersection of the results obtained from the three methods was used to obtain UC disease characteristic genes. Mendelian randomization analysis was performed using expression quantitative trait locus (eQTL) exposure data and UC outcome data to obtain target genes with diagnostic and therapeutic potential. GSEA, Gene Set Variation Analysis (GSVA) and CIBERSORT immune cell infiltration analysis were used to explore the correlation between target genes and immune cell composition. MiRNAs and lncRNAs related to target genes were identified to construct a ceRNA regulatory network.

Results  Results of Mendelian randomization analysis showed that the gene ANXA5 increased the risk of developing UC.Results of GSEA showed that the main enriched pathways of target genes were chemokine signaling, cytokine receptor interaction, hematopoietic cell lineage, JAK/STAT signaling pathway, Leishmania infection.Results of GSVA showed the upregulation of complement and coagulation cascades pathway, etc, and the downregulation of valine leucine and isoleucine degradation pathway, etc.Results of immune cell infiltration analysis showed that neutrophils, activated mast cells, etc. were positively regulated by the gene ANXA5, while stationary dendritic cells, macrophages M2, etc. were negatively regulated by the gene ANXA5. Three key miRNAs and 15 lncRNAs were identified, and the ceRNA regulatory network was mapped.

Conclusion  This study predicted that the ERS-associated gene ANXA5 increased the risk of UC. The miRNA and lncRNA identified might be potential biomarkers for UC.

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References

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