Analysis of Key Genes and Immune Infiltration Mechanism of Scleroderma Based on Artificial Neural Network Model and Prediction of Targeted Traditional Chinese Medicine
10.11842/wst.20231227005
- VernacularTitle:基于人工神经网络模型分析硬皮病关键基因和免疫浸润机制及靶向中药预测
- Author:
Zhiwei ZUO
1
;
Mengdie YANG
;
Bingzeng SHANG
;
Chang LIU
;
Kelei GUO
;
Hua BIAN
Author Information
1. 河南中医药大学骨伤学院 郑州 450008
- Keywords:
Scleroderma;
Artificial neural network;
Immune cell infiltration;
Machine learning;
Traditional Chinese medicine prediction
- From:
World Science and Technology-Modernization of Traditional Chinese Medicine
2024;26(8):2055-2068
- CountryChina
- Language:Chinese
-
Abstract:
Objective to establish a combined diagnosis model of scleroderma related genes based on gene expression comprehensive database(GEO)and artificial neural network(ANN)and to evaluate its effect and to predict and analyze targeted traditional Chinese medicine.Methods two scleroderma chips GSE23741 and GSE95065 were obtained from the GEO database as the training group data set.Random forest and lasso regression algorithms were used to screen the key genes of scleroderma and construct the ANN model for the diagnosis of scleroderma.The validation data sets GSE76807,GSE32413 and GSE59785 were used to verify the model,and the area under curve(AUC)analysis was used to evaluate the clinical application value of ANN model.The relative expression of key gene mRNA was verified by RT-qPCR experiment.The CIBERSORT algorithm was used to estimate the bioinformatics association between scleroderma and the screened biomarkers.Finally,the key genes were used to screen the targeted traditional Chinese medicine.Results A total of 167 differential genes were obtained.Furthermore,the five most relevant key genes(SERPINE2,SFRP4,SUGCT,FBLN5,NRXN2)were screened by machine learning,and the artificial neural network diagnosis model was constructed.The model was used to draw the subject operating characteristic(ROC)curves diagnosed by the training group and the verification group,and the AUC value of the training group was 1.000.The AUC of verification group were 0.770,0.795 and 0.872 respectively.The result of RT-qPCR experiment is consistent with that of machine learning algorithm.Immune cell infiltration analysis showed that the relative content of memory CD4+T cells was significantly increased in scleroderma group,while the relative content of γ δ T cells in normal group was significantly increased.Key genes are associated with macrophage M1,T cells,memory activated CD4+T cells,resting mast cells,CD8+T cells and so on.According to the key genes,12 traditional Chinese medicines were screened.Most of the four qi and five flavors belong to warm,cold,flat,sweet,pungent and bitter,and most of them belong to the meridians of liver,spleen and lung.Conclusion the artificial neural network diagnosis model of key genes of scleroderma is constructed,which can be used in clinical diagnosis of scleroderma,and the potential targeted traditional Chinese medicine for the treatment of scleroderma is predicted,which provides a new perspective for exploring the pathogenesis of scleroderma.
