An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes
Ulcerative colitis (UC) is a long-lasting inflammatory bowel disease that causes inflammation in the intestines and triggers autoimmune responses. This study aims to identify immune-related biomarkers for ulcerative colitis (UC) and explore potential therapeutic targets. First, we downloaded the exp...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1571529/full |
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| author | Na An Zhongwen Lu Yang Li Bing Yang Shaozhen Ji Xu Dong Zhaoliang Ding |
| author_facet | Na An Zhongwen Lu Yang Li Bing Yang Shaozhen Ji Xu Dong Zhaoliang Ding |
| author_sort | Na An |
| collection | DOAJ |
| description | Ulcerative colitis (UC) is a long-lasting inflammatory bowel disease that causes inflammation in the intestines and triggers autoimmune responses. This study aims to identify immune-related biomarkers for ulcerative colitis (UC) and explore potential therapeutic targets. First, we downloaded the expression profiles of datasets GSE87466, GSE87473, and GSE92415 from the GEO database. Next, we identified differentially expressed genes (DEGs) that are associated with UC. Using the WGCNA algorithm, we screened key module genes in UC and retrieved immune-related genes (IRGs) from the ImmPort database. We identified immune-related differentially expressed genes by intersecting the results from WGCNA, DEGs, and IRGs. To build a diagnostic model for UC, we applied 113 combinations of 12 machine learning algorithms. This included 10-fold cross-validation on the training set and external validation on the test set. The single-cell results presented the cellular profile of UC and indicated that the key genes were significantly associated with macrophages, epithelial cells, and fibroblasts. The single-cell results presented the cell atlas of UC and suggested that key genes were significantly associated with macrophages, epithelial cells and fibroblasts. Quantitative polymerase chain reaction (q-PCR) was used to verify the expression levels of the core biomarkers screened out by machine learning. We conducted enrichment analysis using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA), which showed biological processes and signaling pathways associated with UC. Immune cell infiltration analysis based on CIBERSORT was also performed. We also screened potential drugs from the DSigDB drug database. To evaluate their effectiveness, we performed molecular docking and dynamics simulations. The results suggested that compounds like thalidomide and troglitazone are promising candidates for new UC drug development. Our findings provide insights into the pathogenesis of UC, its clinical treatment, and potential drug development. |
| format | Article |
| id | doaj-art-4832ff5c265341ba803a8e03f6fbb39e |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-4832ff5c265341ba803a8e03f6fbb39e2025-08-20T03:46:33ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-06-011210.3389/fmed.2025.15715291571529An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genesNa An0Zhongwen Lu1Yang Li2Bing Yang3Shaozhen Ji4Xu Dong5Zhaoliang Ding6Shandong University of Traditional Chinese Medicine, Jinan, ChinaThe Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing, ChinaZibo City Fourth People’s Hospital, Zibo, ChinaRizhao Hospital of Traditional Chinese Medicine, Rizhao, ChinaZibo City Fourth People’s Hospital, Zibo, ChinaShandong University of Traditional Chinese Medicine, Jinan, ChinaRizhao Hospital of Traditional Chinese Medicine, Rizhao, ChinaUlcerative colitis (UC) is a long-lasting inflammatory bowel disease that causes inflammation in the intestines and triggers autoimmune responses. This study aims to identify immune-related biomarkers for ulcerative colitis (UC) and explore potential therapeutic targets. First, we downloaded the expression profiles of datasets GSE87466, GSE87473, and GSE92415 from the GEO database. Next, we identified differentially expressed genes (DEGs) that are associated with UC. Using the WGCNA algorithm, we screened key module genes in UC and retrieved immune-related genes (IRGs) from the ImmPort database. We identified immune-related differentially expressed genes by intersecting the results from WGCNA, DEGs, and IRGs. To build a diagnostic model for UC, we applied 113 combinations of 12 machine learning algorithms. This included 10-fold cross-validation on the training set and external validation on the test set. The single-cell results presented the cellular profile of UC and indicated that the key genes were significantly associated with macrophages, epithelial cells, and fibroblasts. The single-cell results presented the cell atlas of UC and suggested that key genes were significantly associated with macrophages, epithelial cells and fibroblasts. Quantitative polymerase chain reaction (q-PCR) was used to verify the expression levels of the core biomarkers screened out by machine learning. We conducted enrichment analysis using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA), which showed biological processes and signaling pathways associated with UC. Immune cell infiltration analysis based on CIBERSORT was also performed. We also screened potential drugs from the DSigDB drug database. To evaluate their effectiveness, we performed molecular docking and dynamics simulations. The results suggested that compounds like thalidomide and troglitazone are promising candidates for new UC drug development. Our findings provide insights into the pathogenesis of UC, its clinical treatment, and potential drug development.https://www.frontiersin.org/articles/10.3389/fmed.2025.1571529/fullulcerative colitismachine learningimmunitymolecular dockingdynamicssingle cell |
| spellingShingle | Na An Zhongwen Lu Yang Li Bing Yang Shaozhen Ji Xu Dong Zhaoliang Ding An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes Frontiers in Medicine ulcerative colitis machine learning immunity molecular docking dynamics single cell |
| title | An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes |
| title_full | An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes |
| title_fullStr | An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes |
| title_full_unstemmed | An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes |
| title_short | An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes |
| title_sort | integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes |
| topic | ulcerative colitis machine learning immunity molecular docking dynamics single cell |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1571529/full |
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