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Screening of hub inflammatory bowel disease biomarkers and identification of immune-related functions based on basement membrane genes
BACKGROUND: Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is a chronic, inflammatory, and autoimmune disease, but its specific etiology and pathogenesis are still unclear. This study aimed to better discover the causative basement membrane (BM) ge...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362583/ https://www.ncbi.nlm.nih.gov/pubmed/37481583 http://dx.doi.org/10.1186/s40001-023-01193-5 |
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author | Lin, Penghang Hua, Jin Teng, Zuhong Lin, Chunlin Liu, Songyi He, Ruofan Chen, Hui Yao, Hengxin Ye, Jianxin Zhu, Guangwei |
author_facet | Lin, Penghang Hua, Jin Teng, Zuhong Lin, Chunlin Liu, Songyi He, Ruofan Chen, Hui Yao, Hengxin Ye, Jianxin Zhu, Guangwei |
author_sort | Lin, Penghang |
collection | PubMed |
description | BACKGROUND: Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is a chronic, inflammatory, and autoimmune disease, but its specific etiology and pathogenesis are still unclear. This study aimed to better discover the causative basement membrane (BM) genes of their subtypes and their associations. METHODS: The differential expression of BM genes between CD and UC was analyzed and validated by downloading relevant datasets from the GEO database. We divided the samples into 3 groups for comparative analysis. Construction of PPI networks, enrichment of differential gene functions, screening of Lasso regression models, validation of ROC curves, nomogram for disease prediction and other analytical methods were used. The immune cell infiltration was further explored by ssGSEA analysis, the immune correlates of hub BM genes were found, and finally, the hub central genes were screened by machine learning. RESULTS: We obtained 6 candidate hub BM genes related to cellular immune infiltration in the CD and UC groups, respectively, and further screened the central hub genes ADAMTS17 and ADAMTS9 through machine learning. And in the ROC curve models, AUC > 0.7, indicating that this characteristic gene has a more accurate predictive effect on IBD. We also found that the pathogenicity-related BM genes of the CD and UC groups were mainly concentrated in the ADAMTS family (ADAMTS17 and ADAMTS9). Addition there are some differences between the two subtypes, and the central different hub BM genes are SPARC, POSTN, and ADAMTS2. CONCLUSIONS: In the current study, we provided a nomogram model of CD and UC composed of BM genes, identified central hub genes, and clarified the similarities and differences between CD and UC. This will have potential value for preclinical, clinical, and translational guidance and differential research in IBD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40001-023-01193-5. |
format | Online Article Text |
id | pubmed-10362583 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-103625832023-07-23 Screening of hub inflammatory bowel disease biomarkers and identification of immune-related functions based on basement membrane genes Lin, Penghang Hua, Jin Teng, Zuhong Lin, Chunlin Liu, Songyi He, Ruofan Chen, Hui Yao, Hengxin Ye, Jianxin Zhu, Guangwei Eur J Med Res Research BACKGROUND: Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is a chronic, inflammatory, and autoimmune disease, but its specific etiology and pathogenesis are still unclear. This study aimed to better discover the causative basement membrane (BM) genes of their subtypes and their associations. METHODS: The differential expression of BM genes between CD and UC was analyzed and validated by downloading relevant datasets from the GEO database. We divided the samples into 3 groups for comparative analysis. Construction of PPI networks, enrichment of differential gene functions, screening of Lasso regression models, validation of ROC curves, nomogram for disease prediction and other analytical methods were used. The immune cell infiltration was further explored by ssGSEA analysis, the immune correlates of hub BM genes were found, and finally, the hub central genes were screened by machine learning. RESULTS: We obtained 6 candidate hub BM genes related to cellular immune infiltration in the CD and UC groups, respectively, and further screened the central hub genes ADAMTS17 and ADAMTS9 through machine learning. And in the ROC curve models, AUC > 0.7, indicating that this characteristic gene has a more accurate predictive effect on IBD. We also found that the pathogenicity-related BM genes of the CD and UC groups were mainly concentrated in the ADAMTS family (ADAMTS17 and ADAMTS9). Addition there are some differences between the two subtypes, and the central different hub BM genes are SPARC, POSTN, and ADAMTS2. CONCLUSIONS: In the current study, we provided a nomogram model of CD and UC composed of BM genes, identified central hub genes, and clarified the similarities and differences between CD and UC. This will have potential value for preclinical, clinical, and translational guidance and differential research in IBD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40001-023-01193-5. BioMed Central 2023-07-22 /pmc/articles/PMC10362583/ /pubmed/37481583 http://dx.doi.org/10.1186/s40001-023-01193-5 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 Lin, Penghang Hua, Jin Teng, Zuhong Lin, Chunlin Liu, Songyi He, Ruofan Chen, Hui Yao, Hengxin Ye, Jianxin Zhu, Guangwei Screening of hub inflammatory bowel disease biomarkers and identification of immune-related functions based on basement membrane genes |
title | Screening of hub inflammatory bowel disease biomarkers and identification of immune-related functions based on basement membrane genes |
title_full | Screening of hub inflammatory bowel disease biomarkers and identification of immune-related functions based on basement membrane genes |
title_fullStr | Screening of hub inflammatory bowel disease biomarkers and identification of immune-related functions based on basement membrane genes |
title_full_unstemmed | Screening of hub inflammatory bowel disease biomarkers and identification of immune-related functions based on basement membrane genes |
title_short | Screening of hub inflammatory bowel disease biomarkers and identification of immune-related functions based on basement membrane genes |
title_sort | screening of hub inflammatory bowel disease biomarkers and identification of immune-related functions based on basement membrane genes |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362583/ https://www.ncbi.nlm.nih.gov/pubmed/37481583 http://dx.doi.org/10.1186/s40001-023-01193-5 |
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