泛素
下调和上调
STAT1
免疫系统
信号转导
基因
细胞生物学
糖尿病肾病
泛素连接酶
癌症研究
炎症
基因表达
化学
生物
车站3
STAT蛋白
磷酸化
泛素结合酶
F盒蛋白
蛋白质水解
分子生物学
平方毫米
激活剂(遗传学)
基因表达调控
激酶
免疫学
作者
Song Xue,Yingkui Si,Xue Han
标识
DOI:10.1177/10799907251396047
摘要
This study aims to screen liquid-liquid phase separation (LLPS)-related genes modulating ubiquitination for diagnosing patients with diabetic nephropathy (DN). After performing differential gene expression analysis, ssGSEA, and immune infiltration analysis, LLPS-related genes modulating ubiquitination, key immune cells, and immune-related pathways were acquired. The correlations between LLPS-related genes and ubiquitination, key immune-related pathways were explored, respectively. The diagnostic genes were identified by four machine learning algorithms, followed by evaluation to predict the performance of the machine learning models on ubiquitination using receiver operating characteristic curves and a confusion matrix. In vitro experiments were applied to explore the function of signal transducer and activator of transcription 1 (STAT1) in DN. Totally, 513 differentially expressed genes were screened between the DN and control groups. The activity of the ubiquitin-mediated proteolysis pathway exhibited a significant difference between the DN and control samples. The fraction of a total of 10 immune cells exhibited a significant difference between the lower and higher ubiquitination groups. Totally, nine LLPS-related genes modulating ubiquitination were acquired, and these genes modulating ubiquitination were enriched in ubiquitin pathways and significantly positively correlated with the complement, IFN-α, and IFN-γ pathways. Two diagnostic genes were obtained, namely dual specificity tyrosine phosphorylation regulated kinase 2 (DYRK2) and STAT1. Notably, downregulation of STAT1 effectively alleviated IFN-γ, IFN-α, IL-1β, and TNF-α levels in D-glucose-treated HK-2 cells, as well as the protein level of FK2. LLPS-related STAT1 modulating ubiquitination accelerates the inflammatory response in DN.
科研通智能强力驱动
Strongly Powered by AbleSci AI