文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期,新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

运用生物信息学方法鉴定糖尿病肾病的关键基因。

Identification of key genes for diabetic kidney disease using biological informatics methods.

机构信息

Department of Nephrology, The First Hospital of Jilin University, Changchun, Jilin 130021, P.R. China.

出版信息

Mol Med Rep. 2017 Dec;16(6):7931-7938. doi: 10.3892/mmr.2017.7666. Epub 2017 Sep 29.


DOI:10.3892/mmr.2017.7666
PMID:28990106
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5779875/
Abstract

Diabetic kidney disease (DKD) is a common complication of diabetes, which is characterized by albuminuria, impaired glomerular filtration rate or a combination of the two. The aim of the present study was to identify the potential key genes involved in DKD progression and to subsequently investigate the underlying mechanism involved in DKD development. The array data of GSE30528 including 9 DKD and 13 control samples was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) in DKD glomerular and tubular kidney biopsy tissues were compared with normal tissues, and were analyzed using the limma package. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for DEGs using the GO Function software in Bioconductor. The protein‑protein interaction (PPI) network was then constructed using Cytoscape software. A total of 426 genes (115 up‑ and 311 downregulated) were differentially expressed between the DKD and normal tissue samples. The PPI network was constructed with 184 nodes and 335 edges. Vascular endothelial growth factor A (VEGFA), α‑actinin‑4 (ACTN4), proto‑oncogene, Src family tyrosine kinase (FYN), collagen, type 1, α2 (COL1A2) and insulin‑like growth factor 1 (IGF1) were hub proteins. Major histocompatibility complex, class II, DP α1 (HLA‑DPA1) was the common gene enriched in the rheumatoid arthritis and systemic lupus erythematosus pathways, and the immune response was a GO term enriched in module A. VEGFA, ACTN4, FYN, COL1A2, IGF1 and HLA‑DPA1 may be potential key genes associated with the progression of DKD, and immune mechanisms may serve a part in DKD development.

摘要

糖尿病肾病(DKD)是糖尿病的一种常见并发症,其特征为蛋白尿、肾小球滤过率受损或两者兼有。本研究旨在鉴定与 DKD 进展相关的潜在关键基因,并随后研究 DKD 发病机制中涉及的潜在机制。从基因表达综合数据库中下载了包括 9 个 DKD 和 13 个对照样本的 GSE30528 芯片数据集。将 DKD 肾小球和肾小管肾活检组织中的差异表达基因(DEGs)与正常组织进行比较,并使用 limma 包进行分析。使用 Bioconductor 中的 GO 功能软件对 DEGs 进行基因本体论(GO)注释和京都基因与基因组百科全书(KEGG)通路富集分析。然后使用 Cytoscape 软件构建蛋白质-蛋白质相互作用(PPI)网络。在 DKD 和正常组织样本之间共鉴定到 426 个差异表达基因(115 个上调和 311 个下调)。构建的 PPI 网络包含 184 个节点和 335 条边。血管内皮生长因子 A(VEGFA)、α-辅肌动蛋白-4(ACTN4)、原癌基因,Src 家族酪氨酸激酶(FYN)、胶原,类型 1,α2(COL1A2)和胰岛素样生长因子 1(IGF1)是核心蛋白。主要组织相容性复合体,Ⅱ类,DP α1(HLA-DPA1)是类风湿关节炎和系统性红斑狼疮通路中富集的共同基因,免疫反应是模块 A 中富集的 GO 术语。VEGFA、ACTN4、FYN、COL1A2、IGF1 和 HLA-DPA1 可能是与 DKD 进展相关的潜在关键基因,免疫机制可能在 DKD 发病机制中发挥作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8390/5779875/33f90cf27f5c/MMR-16-06-7931-g02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8390/5779875/baae4cb7a3fb/MMR-16-06-7931-g00.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8390/5779875/048e4278a69e/MMR-16-06-7931-g01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8390/5779875/33f90cf27f5c/MMR-16-06-7931-g02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8390/5779875/baae4cb7a3fb/MMR-16-06-7931-g00.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8390/5779875/048e4278a69e/MMR-16-06-7931-g01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8390/5779875/33f90cf27f5c/MMR-16-06-7931-g02.jpg

相似文献

[1]
Identification of key genes for diabetic kidney disease using biological informatics methods.

Mol Med Rep. 2017-9-29

[2]
Screening and Identification of Hub Genes in the Development of Early Diabetic Kidney Disease Based on Weighted Gene Co-Expression Network Analysis.

Front Endocrinol (Lausanne). 2022-6-3

[3]
Bioinformatics prediction and experimental verification of key biomarkers for diabetic kidney disease based on transcriptome sequencing in mice.

PeerJ. 2022

[4]
Correlation Between Serum 25-Hydroxyvitamin D Levels in Albuminuria Progression of Diabetic Kidney Disease and Underlying Mechanisms By Bioinformatics Analysis.

Front Endocrinol (Lausanne). 2022

[5]
Microarray analysis reveals gene and microRNA signatures in diabetic kidney disease.

Mol Med Rep. 2017-11-28

[6]
Single-cell RNA and transcriptome sequencing profiles identify immune-associated key genes in the development of diabetic kidney disease.

Front Immunol. 2023

[7]
Bioinformatics analysis of fibroblasts exposed to TGF‑β at the early proliferation phase of wound repair.

Mol Med Rep. 2017-9-26

[8]
Diabetic kidney disease-predisposing proinflammatory and profibrotic genes identified by weighted gene co-expression network analysis (WGCNA).

J Cell Biochem. 2022-2

[9]
Gene microarray analysis of expression profiles in Suberoyllanilide hyroxamic acid-treated Dendritic cells.

Biochem Biophys Res Commun. 2018-11-28

[10]
Identification of candidate genes for necrotizing enterocolitis based on microarray data.

Gene. 2018-3-29

引用本文的文献

[1]
Fyn Kinase: A Potential Target in Glucolipid Metabolism and Diabetes Mellitus.

Curr Issues Mol Biol. 2025-8-5

[2]
Identification of Potential Drug Targets for Immunoglobulin A Nephropathy: A Mendelian Randomization Study.

Biomedicines. 2025-2-25

[3]
Integrated multiomic analyses: An approach to improve understanding of diabetic kidney disease.

Diabet Med. 2025-2

[4]
Identifying C1QB, ITGAM, and ITGB2 as potential diagnostic candidate genes for diabetic nephropathy using bioinformatics analysis.

PeerJ. 2023

[5]
Identification of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study mega-analysis.

BMC Med. 2023-1-11

[6]
Epigallocatechin Gallate Induces the Demethylation of Actinin Alpha 4 to Inhibit Diabetic Nephropathy Renal Fibrosis via the NF-KB Signaling Pathway In Vitro.

Dose Response. 2022-6-10

[7]
Integrated Analysis of Multiple Microarray Studies to Identify Core Gene-Expression Signatures Involved in Tubulointerstitial Injury in Diabetic Nephropathy.

Biomed Res Int. 2022

本文引用的文献

[1]
limma powers differential expression analyses for RNA-sequencing and microarray studies.

Nucleic Acids Res. 2015-4-20

[2]
Inhibition of Src kinase blocks high glucose-induced EGFR transactivation and collagen synthesis in mesangial cells and prevents diabetic nephropathy in mice.

Diabetes. 2013-8-13

[3]
Transcriptome analysis of human diabetic kidney disease.

Diabetes. 2011-7-13

[4]
GO-function: deriving biologically relevant functions from statistically significant functions.

Brief Bioinform. 2011-6-24

[5]
Cytoscape: software for visualization and analysis of biological networks.

Methods Mol Biol. 2011

[6]
The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored.

Nucleic Acids Res. 2011-1

[7]
HLA DPA1, DPB1 alleles and haplotypes contribute to the risk associated with type 1 diabetes: analysis of the type 1 diabetes genetics consortium families.

Diabetes. 2010-4-27

[8]
Association between mannose-binding lectin, high-sensitivity C-reactive protein and the progression of diabetic nephropathy in type 1 diabetes.

Diabetologia. 2010-4-16

[9]
Standards of medical care in diabetes--2010.

Diabetes Care. 2010-1

[10]
MicroRNAs and their role in progressive kidney diseases.

Clin J Am Soc Nephrol. 2009-7

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索