Journal of Shandong University (Health Sciences) ›› 2024, Vol. 62 ›› Issue (7): 21-32.doi: 10.6040/j.issn.1671-7554.0.2024.0114

• 呼吸系统疾病精准诊疗专题 • Previous Articles     Next Articles

Identification of necroptosis-related biomarkers in asthma based on machine learning algorithms

WANG Jing1, LIU Xiaofei2, ZENG Rong3, XU Changjuan2, ZHANG Jintao2, DONG Liang2,3   

  1. 1. Department of Respiratory Ⅰ, Wuzhong Peoples Hospital, Wuzhong 751199, Ningxia, China;
    2. Department of Respiratory, The First Affiliated Hospital of Shandong First Medical University &
    Shandong Provincial Qianfoshan Hospital, Shandong Institute of Respiratory, Jinan 250014, Shandong, China;
    3. Department of Respiratory, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan 250014, Shandong, China
  • Published:2024-09-20

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Abstract: Objective To identificate and validate of potential necroptosis-related genes(NRGs)in asthma through bioinformatics analysis. Methods The Gene Expression Omnibus(GEO)database provided the gene expression profile dataset GSE76262, and R software was used to screen for potential differentially expressed NRGs. Protein-protein interactions(PPI)analysis, gene ontology(GO)enrichment analysis, and Kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analysis were performed on the differentially expressed NRGs. The up-regulated NRGs were analyzed by least absolute shrinkage and selection operator(LASSO)logistic regression and support vector machine-recursive feature elimination(SVM-RFE)algorithms. Common signature genes were identified as potential diagnostic markers, and receiver operator characteristic(ROC)curves were drawn to verify their diagnostic efficacy. The expression and diagnostic efficacy of the screened signature genes were further confirmed by GSE137268. Online tools were used to predict microRNAs(miRNAs)that can target and regulate the expression of signature genes. Results Thirty-three differentially expressed NRGs(13 up-regulated and 20 down-regulated)were identified in 118 asthma patients and 21 healthy controls. The PPI results indicated that 20 differentially expressed NRGs interacting. GO and KEGG enrichment analyses revealed enrichment items related to multiple signaling pathways, lymphocyte activation, apoptosis, and immune regulation. LASSO and SVM-RFE showed that seven up-regulated NRGs could be potential diagnosis genes. The ROC curves showed high diagnostic efficiency with the area under the curve(AUC)higher than 0.7. GSE137268 verified that seven signature genes showed the same expression trend as the training set(AUC>0.65). Hsa-miR-138-5p, hsa-miR-200b-3p and hsa-miR-30e-5p were predicted to regulate the expression of necroptosis genes in asthmatic patients. Conclusion BIRC3, HIF1A, FLOT1, NLRP3, RIPK2, GBE1 and PELI1 can serve as potential biomarkers for asthma. Hsa-miR-138-5p, hsa-miR-200b-3p and hsa-miR-30e-5p are upstream regulators of HIF1A, RIPK2 and PELI1, respectively.

Key words: Necroptosis, Asthma, Bioinformatics analysis, Machine learning, Biomarkers

CLC Number: 

  • R562
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