Yu Ling'an, Tao Haiying
Department of Nephrology, Taizhou First People's Hospital, Huangyan Hospital, Wenzhou Medical University, No. 218 Hengjie Road, Dongcheng Street, Huangyan District, Taizhou, 318020, Zhejiang, China.
Department of Endocrinology, Taizhou First People's Hospital, Huangyan Hospital, Wenzhou Medical University, No. 218 Hengjie Road, Dongcheng Street, Huangyan District, Taizhou, 318020, Zhejiang, China.
Biochem Genet. 2023 Feb;61(1):221-237. doi: 10.1007/s10528-022-10250-3. Epub 2022 Jul 14.
The purpose of this study is to explore the progression-related genes of diabetic nephropathy (DN) through weighted gene co-expression network analysis (WGCNA). The gene expression dataset GSE14202 was downloaded from the GEO database for differential expression analysis. WGCNA v1.69 was used to perform co-expression analysis on differentially expressed genes. 25 modular genes were selected through WGCNA. The motif enrichment analysis was performed on 25 genes, and 34 motifs were obtained, of which 8 transcription factors (TFs) were differentially expressed. GENIE3 was applied to analyze the expression correlation of 8 differentially expressed TFs and 25 genes. Combined with the predicted TF-target gene relationship, 69 interactions between 8 TFs and 18 genes were obtained. The functional enrichment analysis of 18 genes showed that 7 key genes were obviously enriched in adaptive immune response and were clearly up-regulated in advanced DN patients. The expression of C1S, LAIR1, CD84, SIT1, SASH3, and CD180 in glomerular samples from DN patients was significantly up-regulated in compared with normal samples, and the expression of these genes was negatively correlated with GFR. We observed that in the in vitro cell model of DN, the relative expression levels of 5 key genes (except SASH3) were obviously elevated in the high-glucose group. Five key genes were identified to be related to the progression of DN. The findings of this study may provide new ideas and therapeutic targets for exploring the pathogenesis of DN.
本研究旨在通过加权基因共表达网络分析（WGCNA）探索糖尿病肾病（DN）的进展相关基因。从GEO数据库下载基因表达数据集GSE14202进行差异表达分析。使用WGCNA v1.69对差异表达基因进行共表达分析。通过WGCNA选择了25个模块基因。对25个基因进行基序富集分析，获得34个基序，其中8个转录因子（TFs）差异表达。应用GENIE3分析8个差异表达TFs与25个基因的表达相关性。结合预测的TF-靶基因关系，获得了8个TFs与18个基因之间的69个相互作用。对18个基因的功能富集分析表明，7个关键基因在适应性免疫反应中明显富集，且在晚期DN患者中明显上调。与正常样本相比，DN患者肾小球样本中C1S、LAIR1、CD84、SIT1、SASH3和CD180的表达显著上调，且这些基因的表达与肾小球滤过率（GFR）呈负相关。我们观察到，在DN的体外细胞模型中，高糖组中5个关键基因（除SASH3外）的相对表达水平明显升高。确定了5个关键基因与DN的进展相关。本研究结果可能为探索DN的发病机制提供新思路和治疗靶点。
