Establishment of artificial neural network model based on mitochondria-associated genes in Crohn's disease
- VernacularTitle:基于克罗恩病线粒体相关基因的人工神经网络模型的构建
- Author:
Fengming DU
1
;
Xiaohua CAO
;
Ruichen LIU
;
Chaoyang HU
;
Yan SUN
Author Information
- Publication Type:Research Article
- Keywords: Crohn's disease; mitochondria; artificial neural network; machine learning; differential analysis; immune cell infiltration
- From: Journal of Clinical Medicine in Practice 2024;28(23):8-15
- CountryChina
- Language:Chinese
-
Abstract:
Objective To screen mitochondria-related genes in Crohn's disease (CD) based on the Gene Expression Omnibus (GEO) database, construct an artificial neural network diagnostic model and evaluate its performance. Methods The CD-related datasets GSE186582 and GSE102133 were downloaded from the GEO database for differential expression genes (DEGs) screening. The intersection of DEGs and mitochondrial genes from the MitoCarta 3.0 database was obtained. Least absolute shrinkage and selection operator regression and random forest algorithms were used to identify CD-specific genes and construct an artificial neural network diagnostic model. The model was further validated by the validation set GSE95095, and the diagnostic performance was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve. The immune cell infiltration in CD was assessed by the CIBERSORT algorithm, and the relationship between biomarkers and infiltrated immune cells was investigated. Results A total of 551 DEGs were obtained, including 275 upregulated and 276 downregulated genes. There were 20 mitochondria-related genes associated with CD. A total of 9 mitochondria-related feature genes (
SOD2 ,MTHFD2 ,BPHL ,PXMP2 ,RMND1 ,AGXT2 ,MAOA ,HMGCS2 ,MAOB ) were screened by two machine learning algorithms. An artificial neural network diagnostic model was constructed by the selected feature genes. The values of AUC of the model in the training and validation groups were 0.956 and 0.736 respectively. Immune cell infiltration analysis showed that the feature genes were associated with resting memory CD4+ T cells, activated memory CD4+ T cells, activated dendritic cells, neutrophils, and CD8+ T cells. Conclusion The artificial neural network diagnostic model for CD based on 9 mitochondrial genes has good predictive performance.
