摘要
目的利用生物信息学技术探讨糖尿病肾脏疾病(DKD)的潜在生物标志物及发病机制。方法通过基因表达数据库下载GSE30528、GSE96804和GSE1049483个DKD肾小球组织数据集,应用生物信息学技术与机器学习相结合的方法筛选DKD生物标志物并建立DKD基因诊断模型,并应用CIBERSORT算法分析DKD肾小球组织中免疫细胞浸润情况,探讨生物标志物与免疫细胞及模型风险评分与患者肾小球滤过率(eGFR)间的相关性。结果从26个差异表达基因中筛选并建立了由G6PC、CDH10和TPPP3组成的三基因模型,该模型在训练集(AUC=0.984)和验证集(AUC=0.992)均显示出良好的诊断效能。对DKD肾小球组织中浸润免疫细胞的分析表明,γδT细胞、活化的自然杀伤(NK)细胞、M2巨噬细胞、静止的树突状细胞、静止的肥大细胞、激活的肥大细胞和中性粒细胞可能参与DKD过程,且G6PC、CDH10和TPPP3与多种免疫细胞浸润相关。使用模型计算DKD患者的危险评分,评分越高,eGFR水平越低,风险评分与eGFR水平呈显著负相关。结论G6PC、CDH10和TPPP3可作为DKD的良好诊断标志物,与DKD免疫细胞浸润相关,可作为DKD诊断和治疗的靶点。
Objective To explore the potential biomarkers and pathogenesis of diabetic kidney disease(DKD)using bioinformatics techniques.Methods Three DKD glomerular tissue datasets,GSE30528,GSE96804,and GSE104948,were downloaded from the gene expression database.The bioinformatics techniques,combined with machine learning,were applied to screen DKD biomarkers and establish a DKD gene diagnostic model.The CIBERSORT algorithm was applied to analyze immune cell infiltration in DKD glomerular tissue.The correlation between biomarkers and immune cells,model risk scores,and the glomerular filtration rate(eGFR)of patients was investigated.Results A three-gene model consisting of G6PC,CDH10 and TPPP3 was screened and established from 26 differentially expressed genes,which showed good diagnostic efficacy in both the training set(AUC=0.984)and validation set(AUC=0.992).Analysis of infiltrating immune cells in DKD glomerular tissue showed thatγδT cells,activated NK cells,M2 macrophages,resting dendritic cells,resting mast cells,activated mast cells,and neutrophils may be involved in the DKD process,and that G6PC,CDH10 and TPPP3 were associated with multiple immune cell infiltrations.Using the model to calculate the risk score of DKD patients,the higher the score,the lower the eGFR level,and the risk score was significantly negatively correlated with the eGFR level.Conclusions G6PC,CDH10 and TPPP3 can be good diagnostic markers for DKD,correlate with DKD immune cell infiltration,and be used as targets for DKD diagnosis and treatment.
作者
徐华
陈佳
宋玉印
Xu Hua;Chen Jia;Song Yuyin(Department of Laboratory Medicine,Panjin Liaoyou Gem Flower Hospital,Panjin 124010,China;Department of General Practice,Panjin Liaoyou Gem Flower Hospital,Panjin 124010,China)
出处
《国际生物医学工程杂志》
CAS
2022年第5期395-402,423,共9页
International Journal of Biomedical Engineering
