Revealing immune infiltrate characteristics and potential diagnostic value of immune-related genes in ulcerative colitis
An integrative genomic analysis
Bibliographic Data
| ID | 22072840 |
|---|---|
| Authors | Jinke Huang (0000-0002-5623-0564, Chinese Academy of Medical Sciences & Peking Union Medical College), Jiaqi Zhang (0000-0002-7999-8330, Chinese Academy of Medical Sciences & Peking Union Medical College), Fengyun Wang (0000-0002-5558-423X, Chinese Academy of Medical Sciences & Peking Union Medical College), Beihua Zhang (0000-0001-8874-8328, Chinese Academy of Medical Sciences & Peking Union Medical College), Xudong Tang (0000-0003-0391-3895, Chinese Academy of Medical Sciences & Peking Union Medical College, corresponding author) |
| Year | 2022 |
| Volume | 10 |
| Pages | 1003002-1003002 |
| Publication date | 2022-10-31 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2022.1003002 |
| PMID | 36388363 |
| OpenAlex | W4307868881 |
| Language | EN |
| References cited | 38 |
Objectives Ulcerative colitis (UC) is an autoimmune disease of the colon. The aim of this study was to explore the characteristics of immune infiltrates in UC patients and identify immune-related diagnostic biomarkers for UC. Methods Three gene expression profiles were acquired from the GEO database, followed by identification of differentially expressed genes (DEGs) by Linear Modeling of Microarray Data. Enrichment analysis of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Disease Ontology (DO) were performed to analyze the biological functions of DEGs. Subsequently, the single sample gene set enrichment analysis (ssGSEA) was performed to identify immune infiltration characteristics of UC. Correlations between diagnostic genes and immune infiltration were explored to identify markers with the greatest diagnostic potential, and a UC diagnostic model was subsequently constructed. Finally, the prediction performance of the model was quantified by nomogram, non-correlated nomogram, and ROC curve. Results A total of 3111 DEGs (1,608 up-regulated and 1,503 down-regulated genes) were identified. DEGs were significantly involved in the immune system and UC-related pathways. Immune infiltration profiles of colonic tissue were significantly different between healthy individuals and UC patients. High proportions of resting of aDCs, B cells, CD8 + T cells, DCs, iDCs, Macrophages, Neutrophils, pDCs, T helper cells, Tfh, Th1 cells, Th2 cells, TIL and Treg were found in UC samples. A 5-gene based diagnostic prediction model was constructed and the results of nomogram, non-correlated nomogram and ROC curve suggested the powerful diagnostic value of the model. Conclusions This study identified the immune infiltrate characteristics and 5 immune-related genes for UC. The model based on the immune-related genes facilitates the early diagnosis of UC and provides a basis for the evaluation of the prognosis of UC
Biology · Computational biology · Disease · Gene · Gene expression · Gene expression profiling · Gene ontology · Immune system · KEGG · Microarray · Microarray analysis techniques · Nomogram · Pathology · Ulcerative colitis · Ferroptosis and cancer prognosis · Inflammatory Bowel Disease · Medicine · Single-cell and spatial transcriptomics · Genetics · Immunology · Oncology
| Highly cited | No |
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