医学信息2025，Vol.38Issue(4)：10-15,6.DOI:10.3969/j.issn.1006-1959.2025.04.002
基于生物信息学和机器学习的克罗恩病关键基因筛选和免疫浸润分析
Key Gene Screening and Immune Infiltration Analysis of Crohn's Disease Based on Bioinformatics and Machine Learning
卜凡靖1
作者信息
- 1. 滨州市第二人民医院消化内科,山东 滨州 256800
- 折叠
摘要
Abstract
Objective To screen the key genes of Crohn's disease(CD)by bioinformatics and machine learning algorithms,and to analyze the immune infiltration.Methods The transcriptional sequencing data of sigmoid colon tissue containing CD and healthy controls(Hcon)was downloaded for differential analysis,and weighted gene co-expression network analysis(WGCNA)was used to filter for CD-related differentially expressed genes(DEGs).The key genes for CD were identified using machine learning methods such as the least absolute shrinkage and selection operator(LASSO)and random forest(RF),and immune infiltration analysis was performed.Results A total of 54 CD-related DEGs were obtained,and machine learning algorithms identified the potential biomarker for CD,CCAAT/enhancer-binding protein delta(CEBPD).In CD samples,the proportion of resting dendritic cells was lower than in Hcon samples.CEBPD was positively correlated with neutrophils and negatively correlated with resting CD4 memory T cells.Conclusion CEBPB is a key gene in the pathogenesis of CD,and dendritic cells,neutrophils,and CD4 memory T cells are closely related to the development of CD,which may be a key in treating CD.关键词
克罗恩病/免疫浸润/机器学习/生物信息学Key words
Crohn's disease/Immune infiltration/Machine learning/Bioinformatics分类
医药卫生引用本文复制引用
卜凡靖..基于生物信息学和机器学习的克罗恩病关键基因筛选和免疫浸润分析[J].医学信息,2025,38(4):10-15,6.
