Zheng Ze-Qun, Cai Di-Hui, Song Yong-Fei
Ningbo Institute of Innovation for Combined Medicine and Engineering, The Affiliated Lihuili Hospital of Ningbo University, Ningbo 315040, Zhejiang Province, China.
Department of Cardiology, Clinical Research Center, Shantou University Medical College, Shantou 515041, Guangdong Province, China.
World J Diabetes. 2024 Oct 15;15(10):2093-2110. doi: 10.4239/wjd.v15.i10.2093.
Diabetic cardiomyopathy (DCM) is a multifaceted cardiovascular disorder in which immune dysregulation plays a pivotal role. The immunological molecular mechanisms underlying DCM are poorly understood.
To examine the immunological molecular mechanisms of DCM and construct diagnostic and prognostic models of DCM based on immune feature genes (IFGs).
Weighted gene co-expression network analysis along with machine learning methods were employed to pinpoint IFGs within bulk RNA sequencing (RNA-seq) datasets. Single-sample gene set enrichment analysis (ssGSEA) facilitated the analysis of immune cell infiltration. Diagnostic and prognostic models for these IFGs were developed and assessed in a validation cohort. Gene expression in the DCM cell model was confirmed through real time-quantitative polymerase chain reaction and western blotting techniques. Additionally, single-cell RNA-seq data provided deeper insights into cellular profiles and interactions.
The overlap between 69 differentially expressed genes in the DCM-associated module and 2483 immune genes yielded 7 differentially expressed immune-related genes. Four IFGs showed good diagnostic and prognostic values in the validation cohort: Proenkephalin (Penk) and retinol binding protein 7 (Rbp7), which were highly expressed, and glucagon receptor and inhibin subunit alpha, which were expressed at low levels in DCM patients (all area under the curves > 0.9). SsGSEA revealed that IFG-related immune cell infiltration primarily involved type 2 T helper cells. High expression of Penk ( < 0.0001) and Rbp7 ( = 0.001) was detected in cardiomyocytes and interstitial cells and further confirmed in a DCM cell model . Intercellular events and communication analysis revealed abnormal cellular phenotype transformation and signaling communication in DCM, especially between mesenchymal cells and macrophages.
The present study identified Penk and Rbp7 as potential DCM biomarkers, and aberrant mesenchymal-immune cell phenotype communication may be an important aspect of DCM pathogenesis.
糖尿病性心肌病(DCM)是一种多方面的心血管疾病,其中免疫失调起着关键作用。DCM潜在的免疫分子机制尚不清楚。
研究DCM的免疫分子机制,并基于免疫特征基因(IFG)构建DCM的诊断和预后模型。
采用加权基因共表达网络分析和机器学习方法,在批量RNA测序(RNA-seq)数据集中确定IFG。单样本基因集富集分析(ssGSEA)有助于分析免疫细胞浸润情况。针对这些IFG开发诊断和预后模型,并在验证队列中进行评估。通过实时定量聚合酶链反应和蛋白质印迹技术确认DCM细胞模型中的基因表达。此外,单细胞RNA-seq数据提供了对细胞图谱和相互作用的更深入见解。
DCM相关模块中的69个差异表达基因与2483个免疫基因重叠,产生了7个差异表达的免疫相关基因。四个IFG在验证队列中显示出良好的诊断和预后价值:脑啡肽原(Penk)和视黄醇结合蛋白7(Rbp7)高表达,而胰高血糖素受体和抑制素α亚基在DCM患者中低表达(所有曲线下面积>0.9)。SsGSEA显示,IFG相关的免疫细胞浸润主要涉及2型辅助性T细胞。在心肌细胞和间质细胞中检测到Penk(<0.0001)和Rbp7(=0.001)的高表达,并在DCM细胞模型中进一步得到证实。细胞间事件和通信分析显示DCM中存在异常的细胞表型转化和信号通信,特别是在间充质细胞和巨噬细胞之间。
本研究确定Penk和Rbp7为潜在的DCM生物标志物,异常的间充质-免疫细胞表型通信可能是DCM发病机制的一个重要方面。