北京生物醫(yī)學(xué)工程

基于GEO數(shù)據(jù)庫挖掘與膜性腎病密切相關(guān)的關(guān)鍵基因

Identification of key genes associated with membranous nephropathy based on GEO database

作者：高燕李葉段雪婷王倩張海松李銘

單位：河北大學(xué)附屬醫(yī)院腎內(nèi)科（河北保定  071000） 河北大學(xué)臨床醫(yī)學(xué)院（河北保定  071000） 河北省慢性腎臟病骨骼代謝生理學(xué)重點(diǎn)實(shí)驗(yàn)室（河北保定  071000） 河北大學(xué)基礎(chǔ)醫(yī)學(xué)院（河北保定  071000） 通信作者：李銘。E-mail: [email protected]

關(guān)鍵詞： GEO數(shù)據(jù)庫；膜性腎病；生物信息學(xué)分析；差異表達(dá)基因；信號(hào)通路

分類號(hào)：R318

出版年·卷·期（頁碼）：2022·41·2（118-124）

摘要：

目的膜性腎病是造成成年人腎病綜合征的最常見因素之一，約1/3的患者會(huì)發(fā)展為終末期腎病。近年來，雖然與膜性腎病有關(guān)的自身抗原陸續(xù)被鑒定出來，但是對(duì)于其發(fā)病機(jī)制還是知之甚少。本研究依托GEO數(shù)據(jù)庫的GSE108113數(shù)據(jù)集，從中提取膜性腎病患者的轉(zhuǎn)錄組相關(guān)數(shù)據(jù)，并通過生物信息學(xué)分析尋找與膜性腎病密切相關(guān)的關(guān)鍵基因和相關(guān)信號(hào)通路。方法首先通過R軟件分析獲取在正常人腎小球組織和膜性腎病病人腎小球組織之間差異表達(dá)的基因，最終獲得36個(gè)上調(diào)差異表達(dá)基因和126個(gè)下調(diào)差異表達(dá)基因。然后對(duì)差異表達(dá)基因進(jìn)行GO注釋和KEGG通路分析，最后通過Cytoscape軟件里的MCODE插件進(jìn)行聚類分析篩選核心基因。結(jié)果通過生物信息學(xué)分析后最終獲得以CYP3A5，CYP4A11，CYP2B6為代表的13個(gè)核心基因在正常腎小球與膜性腎病腎小球中差異表達(dá)，并且這些差異表達(dá)基因顯著富集在藥物代謝和視黃醇代謝通路。結(jié)論本研究揭示了與膜性腎病密切相關(guān)的13個(gè)關(guān)鍵基因和藥物代謝等信號(hào)通路，為揭示膜性腎病的發(fā)病機(jī)制提供研究基礎(chǔ)。

Objective Membranous nephropathy (MN) is a major cause of nephrotic syndrome in adults. Approximately one third of the MN patients will progress to end-stage renal disease. Although, the autoantigen related with MN has been identified in recent years, the underlying pathological mechanism of MN is still unclear. Methods In the present study, we obtained the transcriptomes data of MN patients from GSE108113 in GEO database, in order to identify core genes in relate with the formation of MN through bioinformatical analysis. We first got 36 up-regulated differentially expressed genes (DEGs) and 126 down-regulated DEGs, after gene expression profiling analysis by R software compared the healthy glomeruli with the glomeruli of MN patients. Then the gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analysis were carried out and the core genes were selected by the MCODE application of Cytoscape. Results The bioinformatical results illustrated that DEGs was significantly enriched in drug metabolism and retinol metabolism. Moreover, MCODE application picked up the 13 core genes represented by CYP3A5, CYP4A11, CYP2B6 differently expressed between the healthy and MN glomeruli. Conclusions Our research found 13 core genes and drug metabolism pathway which are connected with the development of MN, and will be benefit to uncover the pathological mechanism of MN.

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