基于生物信息学方法对扩张型心肌病患者中衰老相关基因的综合分析OACSTPCD

Objective To comprehensively analyze aging-related genes in patients with dilated cardiomyopathy(DCM)using a bioinformatics approach.Methods Senescent cell associated genes were extracted from the CellAge database,and datasets related to DCM including GSE19303,GSE17800 and GSE42955 were from the GEO database.Differentially expressed genes of GSE19303 and GSE17800 were identified using R language.The differentially expressed senescence-related genes(DESRGs)selected from senescence associated genes and differentially expressed genes were analyzed by GO,KEGG and PPI networks.Diagnostic genes were further screened by gene expression and ROC curves to construct miRNA-gene networks of diagnostic genes.Results A total of 10 DESRGs were validated,namely LSAMP,RMI2,EMILIN1,CERCAM,MAP3K7CL,CNN1,MMP3,UCHL1,PLK4,APEX2.GO analysis showed that the biological processes of the above differential genes mainly focused on biological processes and pathways including collagen metabolism,positive regulation of cellular component biogenesis,negative regulation of MAPK cascade.Except for MAP3K7CL,MMP3,UCHL1,APEX2,the differences in the expression levels of the other 6 DESRGs between the DCM group and control group in the GSE17800 and GSE42955 datasets were statistically significant(P<0.05).ROC analysis showed that the AUCs of all DESRGs genes in GSE17800 were greater than 0.65,with RMI2(AUC=0.916,95%CI=0.832-0.977)having the highest diagnostic value,followed by PLK4(AUC=0.850,95%CI=0.683-0.961).In the GSE42955 validation set,ROC analysis revealed that the AUCs of all DESRGs genes except CERCAM were greater than 0.65,with RMI2(AUC=0.917,95%CI=0.781-0.986)having the highest diagnostic value,followed by PLK4(AUC=0.833,95%CI=0.591-0.964).Setting AUC of>0.8 as the criterion for selecting diagnostic genes,the overlapping genes in the training and validation sets were RMI2 and PLK4,with high diagnostic value.Conclusion DESRGs such as LSAMP,RMI2,EMILIN1,CERCAM,MAP3K7CL,CNN1,UCHL1,PLK4,APEX2 may be involved in the occurrence and development of DCM.RMI2 and PLK4 are potential specific biomarkers for DCM.