PDF(Pubmed)
Abstract：
UNASSIGNED: The purpose of this study was to identify potential biomarkers in the tubulointerstitium of focal segmental glomerulosclerosis (FSGS) and comprehensively analyze its mRNA-miRNA-lncRNA/circRNA network.
UNASSIGNED: The expression data (GSE108112 and GSE200818) were downloaded from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/). Identification and enrichment analysis of differentially expressed genes (DEGs) were performed. the PPI networks of the DEGs were constructed and classified using the Cytoscape molecular complex detection (MCODE) plugin. Weighted gene coexpression network analysis (WGCNA) was used to identify critical gene modules. Least absolute shrinkage and selection operator regression analysis were used to screen for key biomarkers of the tubulointerstitium in FSGS, and the receiver operating characteristic curve was used to determine their diagnostic accuracy. The screening results were verified by quantitative real-time-PCR (qRT-PCR) and Western blot. The transcription factors (TFs) affecting the hub genes were identified by Cytoscape iRegulon. The mRNA-miRNA-lncRNA/circRNA network for identifying potential biomarkers was based on the starBase database.
UNASSIGNED: A total of 535 DEGs were identified. MCODE obtained eight modules. The green module of WGCNA had the greatest association with the tubulointerstitium in FSGS. PPARG coactivator 1 alpha (PPARGC1A) was screened as a potential tubulointerstitial biomarker for FSGS and verified by qRT-PCR and Western blot. The TFs FOXO4 and FOXO1 had a regulatory effect on PPARGC1A. The ceRNA network yielded 17 miRNAs, 32 lncRNAs, and 50 circRNAs.
UNASSIGNED: PPARGC1A may be a potential biomarker in the tubulointerstitium of FSGS. The ceRNA network contributes to the comprehensive elucidation of the mechanisms of tubulointerstitial lesions in FSGS.
UNASSIGNED: The expression data (GSE108112 and GSE200818) were downloaded from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/). Identification and enrichment analysis of differentially expressed genes (DEGs) were performed. the PPI networks of the DEGs were constructed and classified using the Cytoscape molecular complex detection (MCODE) plugin. Weighted gene coexpression network analysis (WGCNA) was used to identify critical gene modules. Least absolute shrinkage and selection operator regression analysis were used to screen for key biomarkers of the tubulointerstitium in FSGS, and the receiver operating characteristic curve was used to determine their diagnostic accuracy. The screening results were verified by quantitative real-time-PCR (qRT-PCR) and Western blot. The transcription factors (TFs) affecting the hub genes were identified by Cytoscape iRegulon. The mRNA-miRNA-lncRNA/circRNA network for identifying potential biomarkers was based on the starBase database.
UNASSIGNED: A total of 535 DEGs were identified. MCODE obtained eight modules. The green module of WGCNA had the greatest association with the tubulointerstitium in FSGS. PPARG coactivator 1 alpha (PPARGC1A) was screened as a potential tubulointerstitial biomarker for FSGS and verified by qRT-PCR and Western blot. The TFs FOXO4 and FOXO1 had a regulatory effect on PPARGC1A. The ceRNA network yielded 17 miRNAs, 32 lncRNAs, and 50 circRNAs.
UNASSIGNED: PPARGC1A may be a potential biomarker in the tubulointerstitium of FSGS. The ceRNA network contributes to the comprehensive elucidation of the mechanisms of tubulointerstitial lesions in FSGS.
摘要：
■这项研究的目的是鉴定局灶节段肾小球硬化（FSGS）肾小管间质中的潜在生物标志物，并全面分析其mRNA-miRNA-lncRNA/circRNA网络。
■从基因表达综合数据库下载表达数据(GSE108112和GSE200818)(https://www.ncbi.nlm.nih.gov/geo/)。进行差异表达基因(DEGs)的鉴定和富集分析。使用Cytoscape分子复合物检测（MCODE）插件构建和分类DEGs的PPI网络。使用加权基因共表达网络分析(WGCNA)来鉴定关键基因模块。最小绝对收缩和选择算子回归分析用于筛选FSGS肾小管间质的关键生物标志物，并使用受试者工作特性曲线来确定其诊断准确性。通过定量实时PCR(qRT-PCR)和Westernblot验证筛选结果。通过CytoscapeiRegion鉴定了影响hub基因的转录因子（TF）。用于识别潜在生物标志物的mRNA-miRNA-lncRNA/circRNA网络基于starBase数据库。
■总共鉴定了535个DEG。MCODE获得了八个模块。WGCNA的绿色模块与FSGS中的肾小管间质具有最大的关联。PPARG共激活因子1α（PPARGC1A）被筛选为FSGS的潜在肾小管间质生物标志物，并通过qRT-PCR和Westernblot进行验证。TFsFOXO4和FOXO1对PPARGC1A有调节作用。ceRNA网络产生了17个miRNA，32个lncRNAs,和50个circRNAs。
■PPARGC1A可能是FSGS肾小管间质中的潜在生物标志物。ceRNA网络有助于全面阐明FSGS肾小管间质病变的机制。
■从基因表达综合数据库下载表达数据(GSE108112和GSE200818)(https://www.ncbi.nlm.nih.gov/geo/)。进行差异表达基因(DEGs)的鉴定和富集分析。使用Cytoscape分子复合物检测（MCODE）插件构建和分类DEGs的PPI网络。使用加权基因共表达网络分析(WGCNA)来鉴定关键基因模块。最小绝对收缩和选择算子回归分析用于筛选FSGS肾小管间质的关键生物标志物，并使用受试者工作特性曲线来确定其诊断准确性。通过定量实时PCR(qRT-PCR)和Westernblot验证筛选结果。通过CytoscapeiRegion鉴定了影响hub基因的转录因子（TF）。用于识别潜在生物标志物的mRNA-miRNA-lncRNA/circRNA网络基于starBase数据库。
■总共鉴定了535个DEG。MCODE获得了八个模块。WGCNA的绿色模块与FSGS中的肾小管间质具有最大的关联。PPARG共激活因子1α（PPARGC1A）被筛选为FSGS的潜在肾小管间质生物标志物，并通过qRT-PCR和Westernblot进行验证。TFsFOXO4和FOXO1对PPARGC1A有调节作用。ceRNA网络产生了17个miRNA，32个lncRNAs,和50个circRNAs。
■PPARGC1A可能是FSGS肾小管间质中的潜在生物标志物。ceRNA网络有助于全面阐明FSGS肾小管间质病变的机制。
