Identification of genes and potential therapeutic targets related to aortic valve stenosis based on bioinformatics analysis
10.3760/cma.j.issn.0254-9026.2023.09.007
- VernacularTitle:基于生物信息学分析筛选老年主动脉瓣狭窄相关基因及治疗靶点的研究
- Author:
Xiaohan CHEN
1
;
Qingping PENG
;
Tianpeng LI
;
Biao CHENG
Author Information
1. 电子科技大学 四川省人民医院老年医学科，成都 610072
- Keywords:
Aortic valve stenosis;
Immunity;
Neovascularization;
Artificial intelligence
- From:
Chinese Journal of Geriatrics
2023;42(9):1057-1063
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the immune and angiogenesis-related genes in aortic valve stenosis(AS)and potential therapeutic targets, based on bioinformatics and machine learning analysis.Methods:AS data sets from the Gene Expression Omnibus(GEO), immune-related genes from the ImmPort database, and angiogenesis-related genes from the Genecards database and MsigDB were downloaded and combined to determine differentially expressed immune and angiogenesis-related genes(DEGs).Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)functional enrichment analyses were performed.Protein-protein interaction(PPI)was analyzed by using STRING database.The key biomarkers were identified by two machine learning methods including Least Absolute Shrinkage Selection Operator(LASSO)and Support Vector Machine Recursive Feature Elimination(SVM-RFE), validated in training data set and verification data set by receiver operating characteristic curve(ROC), and analyzed by Gene Set Enrichment Analysis(GSEA).The subtypes of immune infiltrating cells were analyzed by CIBERSORT.Based on starBase, miRDB, miRWalk and hTFtarget databases, the mRNA-miRNA-TF was constructed.Finally, Potential therapeutic targets and drugs were analyzed through the CTD database.Results:A total of 90 DEGs related to AS, immune, and angiogenesis were obtained.Enrichment analysis found that DEIRGs were mainly related to immune regulation and cell cycle regulation, such as "leukocyte migration" , "cell chemotaxis" and "cytokine-cytokine receptor interaction" .84 related proteins and 548 interactions were obtained by PPI analysis.Two key biomarkers SecretograninⅡ(SCG2)and Tenascin-C(TNC)were identified by machine learning, which showed high diagnostic value for AS by ROC.SCG2 and TNC are mainly involved in the immune regulation by Enrichment analysis.The infiltration level of macrophage M0 in AS group was significantly higher than that in control group by CIBERSORT analysis.The correlation between macrophage M0 and macrophage M2 and SCG2 was the highest.879 mRNA-miRNA-TF, 253 potential therapeutic agents and 299 relationships were obtained.Conclusions:The key biomarkers, immune characteristics and potential therapeutic targets obtained from the research play a vital role in exploring the pathophysiological progress and new therapeutic strategies of AS.
