Identifying biomarkers associated with the diagnosis of ulcerative colitis via bioinformatics and machine learning

Yuedan Wang; Jinke Huang; Jiaqi Zhang; Fengyun Wang; Xudong Tang; Yuedan Wang; Jinke Huang; Jiaqi Zhang; Fengyun Wang; Xudong Tang

doi:10.3934/mbe.2023476

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 6: 10741-10756. doi: 10.3934/mbe.2023476

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Identifying biomarkers associated with the diagnosis of ulcerative colitis via bioinformatics and machine learning

1.
Institute of Digestive Diseases, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing 100091, China
2.
China Academy of Chinese Medical Sciences, Beijing 100091, China

Academic Editor: Yi Jiang
^† These two authors contributed equally.

Received: 16 February 2023 Revised: 17 March 2023 Accepted: 27 March 2023 Published: 17 April 2023

Background
Ulcerative colitis (UC) is an idiopathic inflammatory disease with an increasing incidence. This study aimed to identify potential UC biomarkers and associated immune infiltration characteristics.
Methods
Two datasets (GSE87473 and GSE92415) were merged to obtain 193 UC samples and 42 normal samples. Using R, differentially expressed genes (DEGs) between UC and normal samples were filtered out, and their biological functions were investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Promising biomarkers were identified using least absolute shrinkage selector operator regression and support vector machine recursive feature elimination, and their diagnostic efficacy was evaluated through receiver operating characteristic (ROC) curves. Finally, CIBERSORT was used to investigate the immune infiltration characteristics in UC, and the relationship between the identified biomarkers and various immune cells was examined.
Results
We found 102 DEGs, of which 64 were significantly upregulated, and 38 were significantly downregulated. The DEGs were enriched in pathways associated with interleukin-17, cytokine–cytokine receptor interaction and viral protein interactions with cytokines and cytokine receptors, among others. Using machine learning methods and ROC tests, we confirmed DUOX2, DMBT1, CYP2B7P, PITX2 and DEFB1 to be essential diagnostic genes for UC. Immune cell infiltration analysis revealed that all five diagnostic genes were correlated with regulatory T cells, CD8 T cells, activated and resting memory CD4 T cells, activated natural killer cells, neutrophils, activated and resting mast cells, activated and resting dendritic cells and M0, M1 and M2 macrophages.
Conclusions
DUOX2, DMBT1, CYP2B7P, PITX2 and DEFB1 were identified as prospective biomarkers for UC. A new perspective on understanding the progression of UC may be provided by these biomarkers and their relationship with immune cell infiltration.
- ulcerative colitis,
- differentially expressed genes,
- LASSO regression model,
- support vector machine recursive feature elimination,
- bioinformatics
Citation: Yuedan Wang, Jinke Huang, Jiaqi Zhang, Fengyun Wang, Xudong Tang. Identifying biomarkers associated with the diagnosis of ulcerative colitis via bioinformatics and machine learning[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10741-10756. doi: 10.3934/mbe.2023476

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Abstract

Background

Ulcerative colitis (UC) is an idiopathic inflammatory disease with an increasing incidence. This study aimed to identify potential UC biomarkers and associated immune infiltration characteristics.

Methods

Two datasets (GSE87473 and GSE92415) were merged to obtain 193 UC samples and 42 normal samples. Using R, differentially expressed genes (DEGs) between UC and normal samples were filtered out, and their biological functions were investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Promising biomarkers were identified using least absolute shrinkage selector operator regression and support vector machine recursive feature elimination, and their diagnostic efficacy was evaluated through receiver operating characteristic (ROC) curves. Finally, CIBERSORT was used to investigate the immune infiltration characteristics in UC, and the relationship between the identified biomarkers and various immune cells was examined.

Results

We found 102 DEGs, of which 64 were significantly upregulated, and 38 were significantly downregulated. The DEGs were enriched in pathways associated with interleukin-17, cytokine–cytokine receptor interaction and viral protein interactions with cytokines and cytokine receptors, among others. Using machine learning methods and ROC tests, we confirmed DUOX2, DMBT1, CYP2B7P, PITX2 and DEFB1 to be essential diagnostic genes for UC. Immune cell infiltration analysis revealed that all five diagnostic genes were correlated with regulatory T cells, CD8 T cells, activated and resting memory CD4 T cells, activated natural killer cells, neutrophils, activated and resting mast cells, activated and resting dendritic cells and M0, M1 and M2 macrophages.

Conclusions

DUOX2, DMBT1, CYP2B7P, PITX2 and DEFB1 were identified as prospective biomarkers for UC. A new perspective on understanding the progression of UC may be provided by these biomarkers and their relationship with immune cell infiltration.

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