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Exploration and verification a 13-gene diagnostic framework for ulcerative colitis across multiple platforms via

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  • 1Department of Gastroenterology, The First Affiliated Hospital of Wan-Nan Medical College, Wuhu, 241001, China.

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|July 1, 2024
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Summary

A new diagnostic model for ulcerative colitis (UC) was developed using gene expression profiles and machine learning. Key genes like LCN2, ASS1, and IRAK3 show potential for diagnosing UC and guiding future therapies.

Keywords:
Diagnostic modelGene set enrichment analysisImmunocytes infiltrationMachine learning algorithmsUlcerative colitis

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Area of Science:

  • Genomics
  • Immunology
  • Computational Biology

Background:

  • Ulcerative colitis (UC) is a chronic inflammatory bowel disease with complex causes and diverse symptoms.
  • Accurate diagnostic methods are crucial for effective UC management.
  • Existing diagnostic tools may not fully capture the disease's heterogeneity.

Purpose of the Study:

  • To develop a robust diagnostic model for UC using gene expression data.
  • To identify key genes that distinguish UC patients from healthy individuals.
  • To explore the potential of identified genes as biomarkers and therapeutic targets.

Main Methods:

  • Analysis of gene expression profiles from multiple cohorts (335 UC patients, 129 healthy controls).
  • Gene Set Enrichment Analysis (GSEA) to identify relevant pathways and genes.
  • Machine learning algorithms, including LASSO regression, to build and validate diagnostic models.
  • Evaluation of immune cell infiltration using single-sample GSEA.

Main Results:

  • A LASSO regression model achieved an average AUC of 0.942, demonstrating high diagnostic accuracy.
  • The model identified 13 key genes, notably LCN2, ASS1, and IRAK3, as significant differentiators.
  • LCN2 expression was significantly higher in active UC patients compared to non-active patients and controls.
  • Positive correlations were found between LCN2, IRAK3, and activated dendritic cells in UC patients.

Conclusions:

  • A potent LASSO-based diagnostic model for UC has been established through gene expression analysis and machine learning.
  • Genes such as LCN2, ASS1, and IRAK3 show promise as diagnostic markers for UC.
  • These identified genes may also serve as potential therapeutic targets for future UC interventions.