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  4. Vol 4, Issue 6, 2023

Analysis of genes and immune cell infiltration related to the diagnosis of ulcerative colitis based on machine learning

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DOI: 10.23977/medsc.2023.040617 | Downloads: 8 | Views: 329

Author(s)

Yan He 1, Bao Xin 1

Affiliation(s)

1 School of Public Health, Shaanxi University of Traditional Chinese Medicine, Xianyang, Shaanxi, China

Corresponding Author

Bao Xin

ABSTRACT

At present, the diagnosis of ulcerative colitis mainly relies on endoscopic methods, and the diagnostic results are often difficult to distinguish from Crohn's disease. This study aims to mine gene expression data at the molecular level to determine the factors related to the diagnosis of ulcerative colitis. Characteristic genes and immune infiltration analysis provide new directions for the diagnosis and treatment of ulcerative colitis. We downloaded the ulcerative colitis gene expression data sets GSE38713 and GSE87466 from the GEO database as training sets for differentially expressed gene analysis. We used three machine learning methods: random forest, XGB, and LASSO regression to analyze the differentially expressed genes. The integrated analysis results determined that Characteristic genes related to the diagnosis of ulcerative colitis and validated in the GSE47908 data set. Immune infiltration analysis was performed on normal samples and ulcerative colitis samples using the CIBERSOR algorithm, and the correlation between signature genes and immune cell infiltration levels was evaluated. It was finally determined that the differential genes related to the diagnosis of ulcerative colitis are: PDZK1IP1, SERPINA1, and TRIM29, which showed good diagnostic ability (AUC>0.8) in both the training set and the validation set. Moreover, PDZK1IP1, SERPINA1, and TRIM29 are positively correlated with dendritic cell resting, monocytes, macrophages, dendritic cell activation, and regulatory T cells, and are positively correlated with T cell CD4+ memory cell activation, natural killer cells, and M1 giant cells. Phagocytes were negatively correlated. In summary, PDZK1IP1, SERPINA1, and TRIM29 may be involved in the occurrence and development of ulcerative colitis through a variety of immune cells, and can be used as diagnostic biomarkers for ulcerative colitis.

KEYWORDS

Ulcerative colitis, Machine learning, Diagnosis, Immune infiltration

CITE THIS PAPER

Yan He, Bao Xin, Analysis of genes and immune cell infiltration related to the diagnosis of ulcerative colitis based on machine learning. MEDS Clinical Medicine (2023) Vol. 4: 118-127. DOI: http://dx.doi.org/10.23977/medsc.2023.040617.

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