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Predicting diagnostic biomarkers associated with immune infiltration in Crohn's disease based on machine learning and bioinformatics

OBJECTIVE: The objective of this study is to investigate potential biomarkers of Crohn's disease (CD) and the pathological importance of infiltration of associated immune cells in disease development using machine learning. METHODS: Three publicly accessible CD gene expression profiles were obt...

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Autores principales: Bao, Wenhui, Wang, Lin, Liu, Xiaoxiao, Li, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369716/
https://www.ncbi.nlm.nih.gov/pubmed/37496049
http://dx.doi.org/10.1186/s40001-023-01200-9
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author Bao, Wenhui
Wang, Lin
Liu, Xiaoxiao
Li, Ming
author_facet Bao, Wenhui
Wang, Lin
Liu, Xiaoxiao
Li, Ming
author_sort Bao, Wenhui
collection PubMed
description OBJECTIVE: The objective of this study is to investigate potential biomarkers of Crohn's disease (CD) and the pathological importance of infiltration of associated immune cells in disease development using machine learning. METHODS: Three publicly accessible CD gene expression profiles were obtained from the GEO database. Inflammatory tissue samples were selected and differentiated between colonic and ileal tissues. To determine the differentially expressed genes (DEGs) between CD and healthy controls, the larger sample size was merged as a training unit. The function of DEGs was comprehended through disease enrichment (DO) and gene set enrichment analysis (GSEA) on DEGs. Promising biomarkers were identified using the support vector machine-recursive feature elimination and lasso regression models. To further clarify the efficacy of potential biomarkers as diagnostic genes, the area under the ROC curve was observed in the validation group. Additionally, using the CIBERSORT approach, immune cell fractions from CD patients were examined and linked with potential biomarkers. RESULTS: Thirty-four DEGs were identified in colon tissue, of which 26 were up-regulated and 8 were down-regulated. In ileal tissues, 50 up-regulated and 50 down-regulated DEGs were observed. Disease enrichment of colon and ileal DEGs primarily focused on immunity, inflammatory bowel disease, and related pathways. CXCL1, S100A8, REG3A, and DEFA6 in colon tissue and LCN2 and NAT8 in ileum tissue demonstrated excellent diagnostic value and could be employed as CD gene biomarkers using machine learning methods in conjunction with external dataset validation. In comparison to controls, antigen processing and presentation, chemokine signaling pathway, cytokine–cytokine receptor interactions, and natural killer cell-mediated cytotoxicity were activated in colonic tissues. Cytokine–cytokine receptor interactions, NOD-like receptor signaling pathways, and toll-like receptor signaling pathways were activated in ileal tissues. NAT8 was found to be associated with CD8 T cells, while CXCL1, S100A8, REG3A, LCN2, and DEFA6 were associated with neutrophils, indicating that immune cell infiltration in CD is closely connected. CONCLUSION: CXCL1, S100A8, REG3A, and DEFA6 in colonic tissue and LCN2 and NAT8 in ileal tissue can be employed as CD biomarkers. Additionally, immune cell infiltration is crucial for CD development.
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spelling pubmed-103697162023-07-27 Predicting diagnostic biomarkers associated with immune infiltration in Crohn's disease based on machine learning and bioinformatics Bao, Wenhui Wang, Lin Liu, Xiaoxiao Li, Ming Eur J Med Res Research OBJECTIVE: The objective of this study is to investigate potential biomarkers of Crohn's disease (CD) and the pathological importance of infiltration of associated immune cells in disease development using machine learning. METHODS: Three publicly accessible CD gene expression profiles were obtained from the GEO database. Inflammatory tissue samples were selected and differentiated between colonic and ileal tissues. To determine the differentially expressed genes (DEGs) between CD and healthy controls, the larger sample size was merged as a training unit. The function of DEGs was comprehended through disease enrichment (DO) and gene set enrichment analysis (GSEA) on DEGs. Promising biomarkers were identified using the support vector machine-recursive feature elimination and lasso regression models. To further clarify the efficacy of potential biomarkers as diagnostic genes, the area under the ROC curve was observed in the validation group. Additionally, using the CIBERSORT approach, immune cell fractions from CD patients were examined and linked with potential biomarkers. RESULTS: Thirty-four DEGs were identified in colon tissue, of which 26 were up-regulated and 8 were down-regulated. In ileal tissues, 50 up-regulated and 50 down-regulated DEGs were observed. Disease enrichment of colon and ileal DEGs primarily focused on immunity, inflammatory bowel disease, and related pathways. CXCL1, S100A8, REG3A, and DEFA6 in colon tissue and LCN2 and NAT8 in ileum tissue demonstrated excellent diagnostic value and could be employed as CD gene biomarkers using machine learning methods in conjunction with external dataset validation. In comparison to controls, antigen processing and presentation, chemokine signaling pathway, cytokine–cytokine receptor interactions, and natural killer cell-mediated cytotoxicity were activated in colonic tissues. Cytokine–cytokine receptor interactions, NOD-like receptor signaling pathways, and toll-like receptor signaling pathways were activated in ileal tissues. NAT8 was found to be associated with CD8 T cells, while CXCL1, S100A8, REG3A, LCN2, and DEFA6 were associated with neutrophils, indicating that immune cell infiltration in CD is closely connected. CONCLUSION: CXCL1, S100A8, REG3A, and DEFA6 in colonic tissue and LCN2 and NAT8 in ileal tissue can be employed as CD biomarkers. Additionally, immune cell infiltration is crucial for CD development. BioMed Central 2023-07-26 /pmc/articles/PMC10369716/ /pubmed/37496049 http://dx.doi.org/10.1186/s40001-023-01200-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Bao, Wenhui
Wang, Lin
Liu, Xiaoxiao
Li, Ming
Predicting diagnostic biomarkers associated with immune infiltration in Crohn's disease based on machine learning and bioinformatics
title Predicting diagnostic biomarkers associated with immune infiltration in Crohn's disease based on machine learning and bioinformatics
title_full Predicting diagnostic biomarkers associated with immune infiltration in Crohn's disease based on machine learning and bioinformatics
title_fullStr Predicting diagnostic biomarkers associated with immune infiltration in Crohn's disease based on machine learning and bioinformatics
title_full_unstemmed Predicting diagnostic biomarkers associated with immune infiltration in Crohn's disease based on machine learning and bioinformatics
title_short Predicting diagnostic biomarkers associated with immune infiltration in Crohn's disease based on machine learning and bioinformatics
title_sort predicting diagnostic biomarkers associated with immune infiltration in crohn's disease based on machine learning and bioinformatics
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369716/
https://www.ncbi.nlm.nih.gov/pubmed/37496049
http://dx.doi.org/10.1186/s40001-023-01200-9
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