Department of Laboratory Medicine, Blood Transfusion Department, Hunan Second People's Hospital (Hunan Brain Hospital), Changsha, Hunan, China.
Department of Nephrology, The First Hospital of Changsha (The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University), Changsha, Hunan, China.
Ren Fail. 2024 Dec;46(2):2409331. doi: 10.1080/0886022X.2024.2409331. Epub 2024 Oct 8.
This study aimed to identify the potential biomarkers associated with pyroptosis in diabetic kidney disease (DKD).
Three datasets from the Gene Expression Omnibus (GEO) were downloaded and merged into an integrated dataset. Differentially expressed genes (DEGs) were filtered and intersected with pyroptosis-related genes (PRGs). Pyroptosis-related DEGs (PRDEGs) were obtained and analyzed using functional enrichment analysis. Random forest, Least Absolute Shrinkage and Selection Operator, and logistic regression analyses were used to select the features of PRDEGs. These feature genes were used to build a diagnostic prediction model, identify the subtypes of the disease, and analyze their interactions with transcription factors (TFs)/miRNAs/drugs and small molecules. We conducted a comparative analysis of immune cell infiltration at different risk levels of pyroptosis. qRT-PCR was used to validate the expression of the feature genes.
A total of 25 PRDEGs were obtained. These genes were coenriched in biological processes and pathways, such as the regulation of inflammatory responses. Five key genes ( were identified and verified using qRT-PCR. The diagnostic model based on key genes has a good diagnostic prediction ability. Five key genes interacted with TFs and miRNAs in 67 and 80 pairs, respectively, and interacted with 113 types of drugs or molecules. Immune infiltration of samples with different pyroptosis risk levels showed significant differences. Thus, and are potential DKD biomarkers.
Genes that regulate pyroptosis can be used as predictors of DKD. Early diagnosis of DKD can aid in its effective treatment.
本研究旨在鉴定与糖尿病肾病（DKD）中细胞焦亡相关的潜在生物标志物。
从基因表达综合数据库（GEO）下载了三个数据集，并将其合并为一个综合数据集。筛选差异表达基因（DEGs）并与细胞焦亡相关基因（PRGs）进行交集。获得细胞焦亡相关 DEGs（PRDEGs），并进行功能富集分析。使用随机森林、最小绝对收缩和选择算子以及逻辑回归分析来选择 PRDEGs 的特征。这些特征基因用于构建诊断预测模型，识别疾病的亚型，并分析它们与转录因子（TFs）/miRNAs/药物和小分子的相互作用。我们对不同细胞焦亡风险水平下的免疫细胞浸润进行了比较分析。使用 qRT-PCR 验证特征基因的表达。
共获得 25 个 PRDEGs。这些基因在生物过程和途径中存在共同富集，如炎症反应的调节。通过 qRT-PCR 鉴定并验证了 5 个关键基因（ 。基于关键基因的诊断模型具有良好的诊断预测能力。这 5 个关键基因分别与 67 对 TFs 和 80 对 miRNAs 相互作用，并与 113 种药物或分子相互作用。不同细胞焦亡风险水平的样本的免疫浸润存在显著差异。因此， 和 是潜在的 DKD 生物标志物。
调节细胞焦亡的基因可作为 DKD 的预测指标。对 DKD 的早期诊断有助于其有效治疗。
