Department of Molecular Medicine and Biotechnology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India.
Department of Biostatistics and Health, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India.
Curr Diabetes Rev. 2024;21(2):e100622205872. doi: 10.2174/1573399819666220610191935.
Renal disease in T2DM could arise independently of hyperglycemia, aka non diabetic kidney disease. Its prevalence ranges from 33% to 72.5% among T2DM patients. Specific molecular signatures that distinguish Diabetic Nephropathy from NDKD (FSGS) in T2DM might provide new targets for CKD management.
Five original GEO microarray DN and FSGS datasets were evaluated (GSE111154, GSE96804, GSE125779, GSE129973 and GSE121233). Each of the three groups (DN, FSGS, and Controls) had equal renal transcriptome data (n=32) included in the analysis to eliminate bias. The DEGs were identified using TAC4.0. Pathway analysis was performed on the discovered genes aligned to official gene symbols using Reactome, followed by functional gene enrichment analysis using Funrich, Enrichr..STRING and Network analyst investigated PPI, followed by Webgestalt's pathway erichment. Finally, using the Targetscan 7.0 and DIANA tools, filtered differential microRNAs downregulated in DN were evaluated for target identification.
Between the three groups, DN, FSGS, and Control, a total of 194 DEGs with foldchange, >2 & <-2 and P-value 0.01 were found in the renal transcriptome. In comparison to control, 45 genes were elevated, particularly in DN, whereas 43 were upregulated specifically in FSGS. DN datasets were compared to FSGS in a separate analysis. FABP4, EBF1, ADIRF, and ART4 were shown to be among the substantially up-regulated genes unique to DN in both analyses. The transcriptional regulation of white adipocytes was discovered by pathway analysis.
The molecular markers revealed might be employed as specific targets in the aetiology of DN, as well as in T2DM patients' therapeutic care.
2 型糖尿病患者的肾脏疾病可能独立于高血糖症（即非糖尿病肾病）发生。在 2 型糖尿病患者中，其患病率范围为 33%至 72.5%。在 2 型糖尿病中，区分糖尿病肾病与 FSGS 的特定分子特征可能为 CKD 管理提供新的靶点。
评估了五个原始 GEO 微阵列 DN 和 FSGS 数据集（GSE111154、GSE96804、GSE125779、GSE129973 和 GSE121233）。为了消除偏差，分析中纳入了每个三组（DN、FSGS 和对照组）具有相同的肾脏转录组数据（n=32）。使用 TAC4.0 鉴定差异表达基因（DEGs）。使用 Reactome 对发现的基因进行基于官方基因符号的途径分析，然后使用 Funrich、Enrichr 进行功能基因富集分析。STRING 和 Network analyst 调查 PPI，然后使用 Webgestalt 的途径富集。最后，使用 Targetscan 7.0 和 DIANA 工具，评估 DN 中下调的差异微小 RNA 的靶标识别。
在三组中，DN、FSGS 和对照组的肾脏转录组中共发现了 194 个具有>2 倍和<2 倍变化倍数、P 值<0.01 的差异表达基因。与对照组相比，45 个基因上调，尤其是在 DN 中，而 43 个基因仅在 FSGS 中上调。在单独的分析中，将 DN 数据集与 FSGS 进行了比较。在这两种分析中，FABP4、EBF1、ADIRF 和 ART4 被证明是 DN 中特有的显著上调基因之一。通过途径分析发现了白色脂肪细胞的转录调控。
揭示的分子标记可能被用作 DN 病因学以及 2 型糖尿病患者治疗护理的特定靶点。
