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An integrated machine learning framework for developing and validating diagnostic models and drug predictions based

Na An1, Zhongwen Lu2, Yang Li3

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This study identifies key immune biomarkers for ulcerative colitis (UC) and potential drug targets. Findings offer insights into UC pathogenesis and therapeutic development for this inflammatory bowel disease.

Keywords:
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Area of Science:

  • Immunology
  • Genomics
  • Computational Biology

Background:

  • Ulcerative colitis (UC) is a chronic inflammatory bowel disease with autoimmune components.
  • Identifying specific immune-related biomarkers is crucial for understanding UC pathogenesis and developing targeted therapies.

Purpose of the Study:

  • To identify immune-related biomarkers for ulcerative colitis (UC).
  • To explore potential therapeutic targets for UC treatment.
  • To build a diagnostic model for UC using machine learning.

Main Methods:

  • Downloaded gene expression profiles from GEO database (GSE87466, GSE87473, GSE92415).
  • Identified differentially expressed genes (DEGs) and utilized Weighted Gene Co-expression Network Analysis (WGCNA).
  • Integrated DEGs, WGCNA key genes, and immune-related genes (IRGs) from ImmPort; applied machine learning for diagnostic model construction and q-PCR for validation.

Main Results:

  • Identified immune-related differentially expressed genes associated with UC.
  • Single-cell analysis revealed key genes linked to macrophages, epithelial cells, and fibroblasts.
  • Enrichment analysis (GO, KEGG, GSEA) highlighted UC-associated biological processes and pathways.
  • Machine learning models demonstrated diagnostic potential; thalidomide and troglitazone emerged as potential therapeutic candidates.

Conclusions:

  • The study successfully identified immune-related biomarkers and potential therapeutic targets for UC.
  • Findings provide a deeper understanding of UC pathogenesis and offer avenues for novel drug development.
  • The developed diagnostic models and identified drug candidates warrant further clinical investigation.