转录组分析鉴定腰椎椎间盘退行性变中潜在免疫相关生物标志物

目的 通过转录组分析鉴定腰椎椎间盘退行性变（IDD）中潜在的免疫相关生物标志物，寻找IDD免疫相关的关键靶点。方法 从基因表达汇编数据库（GEO）的GSE67567数据集中获得基因表达谱，并筛选差异表达mRNA、长链非编码RNA（lncRNA）和微RNA（miRNA）。使用加权基因共表达网络分析（WGCNA）和基因集富集分析（GSEA）筛选免疫相关模块，并使用Cytoscape构建lncRNA-miRNA-mRNA竞争性内源RNA（ceRNA）网络，通过免疫浸润分析确定相关的免疫细胞，使用Pearson相关分析筛选免疫相关的关键靶点。通过GSEA筛选关键的生物过程和途径。使用GEO的GSE124272数据集验证关键靶基因的表达。结果 WGCNA结果表明，蓝绿色模块与免疫力有关。免疫浸润分析显示，CD4幼稚T细胞可能是关键的免疫细胞。Pearson相关分析表明，4个mRNA（UHMK1、ZFP36L2、ZCCHC3、ZBTB20）和1个lncRNA（LINC00641）可能是IDD免疫相关的关键靶点，建立了一个免疫调节相关的ceRNA网络。GSEA结果显示，LINC00641可能通过TGF-β信号通路调节IDD。验证结果表明，这些关键基因在GSE124272数据集中存在差异表达。结论 mRNA（UHMK1、ZFP36L2、ZCCHC3、ZBTB20）和lncRNA（LINC00641）是IDD免疫相关的关键靶点。     

腹主动脉瘤特异性ceRNA调控网络的筛选与构建

背景与目的 腹主动脉瘤（AAA）是一种在老年患者中发病率及病死率均较高的疾病。目前基于内源性竞争RNA（ceRNA）网络在AAA发病机制中的作用不明。本研究通过筛选和构建AAA特异性的长链非编码RNA（lncRNA）-微小RNA（miRNA）-mRNA的ceRNA网络,以期揭示ceRNA网络在AAA形成与发展中的作用。方法 首先通过基因表达数据库（GEO）筛选数据集,寻找AAA与正常腹主动脉组织的差异lncRNA及差异mRNA;利用Lasso回归从差异RNA中筛选疾病特征性基因;运用软件Starbase寻找与差异lncRNA和特异性mRNA共同结合的miRNA,利用Cytoscape软件构建lncRNA-miRNA-mRNA的ceRNA网络,使用Cytoscape中的cytohubba模块进一步筛选核心ceRNA网络。结果 筛选获得GSE7084和GSE57691两个数据集,获得差异mRNA 114个,差异lncRNA 13个;利用Lasso回归从差异mRNA中获得17个特异性的mRNA;结果显示,lncRNA HCP5-miR27-FOSB被认为可能是导致AAA发生发展最为关键的ceRNA网络。结论 所构建的ceRNA网络可能参与AAA的形成及发展,但该结果需开展后续研究进一步探明和验证。     

甲状腺乳头状癌潜在关键基因的生物信息学分析

背景与目的 甲状腺癌是近年来发病率增长最快的疾病,甲状腺乳头状癌（PTC）是甲状腺癌最多见的一种亚型。目前,亟需寻找与PTC相关的生物标志物分子,以提高预后诊断和提供高效的治疗靶标。方法 检索并分析GEO数据库PTC相关的的微阵列数据集（GSE60542,GSE33630和GSE3467）,通过GEO数据库的GEO2R工具筛选PTC和正常甲状腺组织的差异表达基因。对差异基因行全基因组的富集分析,这些差异基因蛋白质之间的相互作用通过线上数据库工具分析,通过Cytoscape软件进行可视化处理。通过Cbioportal分析工具评估关键基因对PTC的预后价值并进一步行实验验证。结果 共鉴定62个上调和40个下调差异基因,挑选出10个具有高度连通性的关键基因,其中,KIT降低与PTC的预后不良相关（P<0.01）。通过qRT-PCR检测52例PTC组织和癌旁正常组织中KIT的表达,结果显示,KIT在癌组织中表达较癌旁正常组织显著降低（P<0.001）,KIT的表达与临床分期（P=0.008）和淋巴结转移明显相关（P=0.023）。结论 KIT在PTC组织中表达较正常甲状腺组织降低,其可能是PTC患者预后不良的关键基因,并有望成为PTC的治疗靶标和分子生物学标志物。     

创伤性脊髓损伤后环状非编码RNA-微小RNA潜在调控网络信息挖掘

目的通过高通量测序获取创伤性脊髓损伤(traumatic spinal cord injury,TSCI)后环状非编码RNA(circular RNA,circRNA)与微小RNA(microRNA,miRNA)表达谱,预测潜在circRNA-miRNA调控网络。方法取48只雄性C57BL/6小鼠(体质量18~22 g)随机均分为两组(n=24),TSCI组采用Allen’s打击器械制备TSCI模型,假手术组(Sham组)仅切开椎板不损伤脊髓。术后3 d,两组取材行HE染色,观察脊髓组织结构;提取组织总RNA建库,高通量测序鉴定circRNA和miRNA差异表达谱,基因本体分析(gene ontology,GO)注释差异表达的circRNA宿主基因功能,筛选显著差异表达的miRNA,通过TargetScan和miRanda预测circRNAmiRNA靶向结合,筛选关键circRNA,构建潜在调控网络。结果HE染色示Sham组小鼠脊髓结构完整无破裂,TSCI组脊髓结构有明显损伤破裂。测序共鉴定出17440个circRNA、1228个miRNA。差异表达的circRNA宿主基因主要富集在细胞质,生物过程中差异基因主要富集在转录的正调控和蛋白磷酸化过程。差异表达最显著的miRNA为mmu-miR-21-5p,筛选出可与其靶向结合的circRNA6730,以circRNA6730为核心构建潜在circRNAmiRNA调控网络。结论通过表达谱分析和功能注释分析,显著差异表达的circRNA和miRNA有潜在的临床标志物价值,以circRNA6730为核心包含mmu-miR-21-5p的靶向互作网络可能在TSCI的发生发展过程中起重要调控作用,有助于阐明TSCI的病理生理进程机制,为临床诊疗提供新思路。     

miR-542-3p对骨肉瘤细胞侵袭能力的影响及其靶基因预测

目的 明确miR-542-3p对骨肉瘤细胞系HOS和SAOS2细胞侵袭能力的影响,通过生物信息学建立miR-542-3p靶基因调控网络,并探讨其潜在作用靶点。方法 使用miR-542-3p mimics转染HOS和SAOS2细胞,同时设立正常组（未转染）及阴性对照组（转染miR-neg mimics）,Transwell小室试验检测转染后细胞的侵袭能力。通过美国国立生物信息中心（NCBI）基因表达共享数据库（GEO）联合GEO2R平台,获取并分析miRNA-542-3p与miR-neg转染的U2OS细胞的基因表达谱数据及其差异表达基因,进而使用miRNA预测数据库及Cytoscape软件,预测并建立miR-542-3p靶基因调控网络。实时荧光定量PCR验证miR-542-3p转染HOS和SAOS2细胞后,部分预测目标靶基因的mRNA表达水平变化。结果 miR-542-3p干预组HOS和SAOS2细胞的侵袭能力较正常组和阴性对照组明显降低（P均＜0.05）。数据库差异基因分析表明,miR-542-3p与miR-neg转染的U2OS细胞的表达差异基因共有417个,其中185个基因表达下调。miRNA预测数据库预测的266个靶基因中,有14个与差异基因分析中得到的表达下调基因重合。实时荧光定量PCR分析得出,ILK、TBPL1、ETS1这3个预测靶基因在miR-542-3p干预组中的表达水平显著低于阴性对照组,差异均有统计学意义（P均＜0.05）。结论 miR-542-3p通过下调ILK、TBPL1、ETS1显著抑制骨肉瘤细胞的侵袭能力。     

成骨分化内源性竞争性lncRNA-miRNA-mRNA网络与核心基因的研究

目的 揭示成骨分化中内源性竞争性长链非编码核糖核酸lncRNA（long noncoding RNA,lncRNA）与下游潜在的微小核糖核酸(micro-ribonucleic acid,microRNA,miRNA),及信使核糖核酸（messenger RNA,mRNA）的表达关系,构建内源性竞争性lncRNA-miRNA-mRNA网络。方法 选取NCBI基因表达综合数据库基因芯片GSE89330、GSE72429、GSE74837,应用GEO2R获得差异基因(differentially expressed genes,DEGs)、差异lncRNA（differentially expressed lncRNA,DElncRNAs）和差异miRNA （differentially expressed miRNA,DEmiRNAs）。通过DAVID数据库(Database for Annotation,Visualization and Integrated Discovery)进行DEGs功能富集分析（GO analysis）和KEGG分析（Kyoto Encyclopedia of Genes and Genomes analysis）。利用miRWalk在线工具、DIANA在线分析工具lncBASE 2.0预测DEGs的上游潜在靶点和DEmiRNAs的lncRNA潜在靶点,互相比对,利用Cytoscape构建lncRNA-miRNA-mRNA互作网络。应用STRING(Search Tool for the Retrieval of Interacting Genes)、Cytoscape和MCODE(Molecular Complex Detection)软件建立蛋白相互作用网络（PPI network）,计算DEGs 的各个连接度并分析和筛选网络集簇模块,并进行关键基因（hub gene）筛选。结果 共获得186个DEGs,包含81个下调基因和105个上调基因;89个DEmiRNA,包括25个下调miRNA和64个上调miRNA;441个DElncRNA,包括205个下调lncRNA和236个上调lncRNA。最终筛选出84个DEGS和7个DEmiRNA及11个DElncRNAs构建lncRNA-miRNA-mRNA互作网络。对186个DEGs GO分析发现其功能主要富集在炎症反应和血管生成中,其分子功能主要在生长因子活化中。通过PPI网络分析,筛选出两个网络集簇模块,并得到10个关键基因（IL6、CXCL12、CXCL8、CCL2、HGF、LEP、VCAM1、CXCL1、SAA1、FOS）。结论 通过lncRNA-miRNA-mRNA互作网络,预测了新的潜在内源性竞争性lncRNA与下游miRNA-mRNA存在联系。     

基于生物信息学的肝内胆管癌差异表达基因谱中关键基因的筛选及分析

背景与目的：肝内胆管癌（ICC）是指来源于肝内胆管上皮的一种恶性肿瘤,其发病隐匿,恶性程度高。ICC早期无明显临床表现,大多数患者确诊时往往已失去手术机会,因此预后极差。寻找ICC的早期诊断和治疗靶标具有重要意义,因此,本研究通过生物信息学方法对影响ICC发生发展的关键基因进行筛选。方法：从GEO数据库下载2个ICC转录组数据集（GSE107943、GSE119336）。用R语言的edge R包筛选出差异表达基因,然后对这些差异表达基因进行GO和KEGG通路富集分析。通过STRING数据库建立差异表达基因的蛋白质-蛋白质相互作用（PPI）网络,使用Cytoscape中的MCODE插件发掘出关键调控基因。分析关键调控基因在肿瘤组织中的表达,采用UALCAN、GEPIA数据库进行验证。通过UCSC XENA数据库分析关键调控基因在泛癌中的表达。利用TCGA数据库分析关键调控基因共表达基因。采用UALCAN、GEPIA数据库分析关键调控基因与患者预后、肿瘤分级、分期、淋巴转移的关系。使用R语言GSVA包计算关键调控基因表达与免疫浸润相关性。绘制ROC曲线评价关键调控基因对ICC的预测能力。采...     

基于基因表达汇编数据库的外周血中脊柱椎间盘退行性变诊断标志物的生物信息学分析

目的 通过生物信息学分析椎间盘退行性变（IDD）相关的差异表达基因（DEG）,寻找疾病的新型诊断标志物。方法 通过基因表达汇编（GEO）数据库GSE124272、GSE150408数据集下载IDD相关的外周血样本芯片数据,筛选出IDD组和正常组之间的DEG。使用DAVID在线数据库对DEG进行基因本体（GO）功能富集和京都基因与基因组百科全书（KEGG）信号通路富集,然后利用STRING在线数据库和Cytoscape软件构建蛋白质-蛋白质相互作用（PPI）网络并获取关键基因,并利用GSE23130数据集中的纤维环样本芯片数据进行验证。利用GSE124272、GSE150408数据集中的数据,采用受试者工作特征（ROC）曲线评估外周血中关键基因的诊断效能。结果 联合分析后筛选出597个DEG,包含363个上调基因和234个下调基因。GO功能富集分析发现DEG主要参与细胞黏附、细胞凋亡、趋化作用和细胞迁移等功能,KEGG分析发现DEG主要参与细胞外基质受体相互作用和癌症中的信号通路。PPI网络分析筛选出17个关键基因,经验证获得RBMX、EEF1A1、SSR1和POLR2C这4个基因,ROC曲线分析显示这4个基因对IDD诊断效能显著,曲线下面积分别为0.763、0.741、0.710、0.702。结论 RBMX、EEF1A1、SSR1和POLR2C或可成为IDD的新型诊断标志物,为该病进一步的功能研究提供理论依据。     

探讨Circ_0000253在骨肉瘤中的诊断价值

目的:探讨circ_0000253作为骨肉瘤（osteosarcoma,OS）诊断和预后生物标志物的价值。方法:GEO数据库筛选OS中差异表达的circRNAs,预测和circRNA存在结合位点的微小RNA（MicroRNA,miRNA）进一步构建ceRNA网络,GO和KEGG分析筛选靶基因。qRT-PCR检测112例...     

基于公共数据库的甲状腺癌预后风险基因筛选

背景与目的 甲状腺癌的发病率呈逐年增长趋势,尽管其总体预后较好,但仍有部分患者因复发或转移而死亡。本研究旨在基于公共数据库应用生物信息学方法筛选甲状腺癌的预后风险基因。方法 从癌症RNA测序关系（CRN）数据库下载甲状腺癌的蛋白编码基因RNA-seq数据,筛选甲状腺癌中差异表达的蛋白编码基因。通过DAVID数据库对差异表达的蛋白编码基因进行功能富集分析。用STRING数据库和Cytoscape软件构建差异表达蛋白编码基因之间的相互作用网络,分别用Cytoscape软件中的cytoHubba插件与ClueGO插件筛选核心基因,并对核心基因进行功能预测。用UALCAN数据库验证核心基因在甲状腺癌中的表达水平,通过GEPIA数据库对核心基因进行生存分析,分析核心基因的表达水平对甲状腺癌的生存时间有无影响。结果 筛选共得到913个差异表达的蛋白编码基因。这些基因主要富集于调控小分子GTP酶介导的信号转导、Z膜、结合肌动蛋白和细胞色素P450介导的药物代谢。构建互作网络后,筛选出10个核心基因,分别为TP53、ESR1、FOS、SYP、PPARG、ACTB、GRIA1、NRXN1、HDAC3和KIT,其中TP53得分最高,为62,它们均在甲状腺癌组织中表达下调;预测显示核心基因TP53、ESR1、PPARG可能参与了基因沉默的负性调控,TP53、FOS可能参与了RNA聚合酶II对pri-miRNA的转录调控过程。UALCAN数据库验证结果显示,除TP53外,其余核心基因均在甲状腺癌组织中表达下调（均P<0.05）,与CRN数据库中的表达结果一致。生存分析结果显示,KIT的高表达与甲状腺癌患者的无病生存期明显相关（P=0.012）,而对其总体生存期无明显影响（P=0.85）。结论 本研究筛选的蛋白编码基因KIT在甲状腺癌组织中呈低表达,其高表达与甲状腺癌的无病生存期密切相关,推测其可能成为甲状腺癌的预后风险标志物或治疗靶点。     

通过生物信息学分析发现胃癌的靶点基因和生物学特性

目的从公共数据库获取数据，筛选差异表达基因，旨在发现胃癌潜在的靶点基因并揭示其生物学特征。 方法基因表达谱（GSE29272、GSE54129、GSE13911、GSE79973、GSE19826）从GEO数据库获得；差异表达基因通过GEO2R筛选出，韦恩图绘制出5个基因表达谱的交集，从而得出共同差异表达基因；使用DAVID数据库进行共同差异表达基因的KEGG通路分析和GO富集分析；共同差异表达基因通过STRING数据库获取其蛋白质-蛋白质互作（PPI）网络图并用Cytoscape软件进行可视化，同时通过Cytoscape软件中的插件CytoHubba筛选胃癌靶点基因；靶点基因在GEPIA数据库和UALCAN数据库中进一步验证其表达及生存分析；CMap数据库预测其潜在的靶向小分子化合物。 结果韦恩图筛选出105个共同差异表达基因，其中包括57个下调基因和48个上调基因；经DAVID数据库中的KEGG通路分析和GO富集分析显示，这些上调基因主要与细胞外基质组织、细胞黏附、局灶性黏附、PI3K-Akt信号传导途径、细胞外基质-受体相互作用相关。通过Cytoscape软件筛选出8个靶点基因：BGN、SPARC、COL5A2、COL5A1、COL1A2、COL4A1、COL6A3和COL11A1；在GEPIA数据库和UALCAN数据库验证后，确认了这8个关键基因与胃癌发生发展有关。生存分析显示，COL4A1（P=0.029，HR=1.4）和COL5A2（P=0.009 5，HR=1.5）的高表达与生存能力降低有关。CMap数据库分析显示吡咯酰胺和芳香维甲酸最有可能逆转胃癌的状态。 结论BGN、SPARC、COL5A2、COL5A1、COL1A2、COL4A1、COL6A3和COL11A1可能被用作改善胃癌诊断和免疫疗法生物标志物的潜在靶标，吡咯酰胺和芳香维甲酸最有可能成为治疗胃癌的小分子化合物，这些分析结果为胃癌的病因研究提供了新的方向，也为深入探究其发病机制提供了理论基础。     

胃癌枢纽基因的筛选和预后分析

目的:运用生物信息学方法探讨胃癌的预测指标和治疗靶点,并分析其与预后的关系。方法:从基因表达综合(GEO)数据库下载3个微阵列数据集(GSE13911、GSE33651、GSE79973),运用GEO2R筛选出胃癌样本与正常组织样本间的差异表达基因,通过基因本体论(GO)富集分析和京都基因与基因组百科全书(KEGG)分析对差异基因进行功能和通路注释,同时使用STRING和Cytoscape构建蛋白互作网络(PPI),筛选出枢纽基因,结合Kaplan-Meier plotter数据库对筛选出的枢纽基因进行预后分析。结果:共筛选出135个差异表达基因,其中68个上调,67个下调。GO分析结果表明差异表达基因主要参与信号转导、钙离子结合、细胞外外泌体等生物学过程。KEGG分析显示差异基因主要富集的通路包括PI3K/Akt信号通路、ECM受体相互作用、黏着斑。经PPI分析筛选得出COL1A1、COL1A2、COL4A1、FN1、THBS1、CD44、COL2A1、COL4A2、CXCL8、COL5A1为枢纽基因,生存分析显示除THBS1的上调,其余基因的差异表达均影响胃癌患者的总体生存率。结论:所筛选的枢纽基因的异常表达可能参与胃癌的发生发展过程,与胃癌患者的预后密切相关,可以作为潜在的预测指标和治疗靶点为胃癌的进一步研究提供依据。     

Mechanism of ferroptosis in hypertensive nephropathy

BackgroundIn recent years, it has been demonstrated that ferroptosis can be involved in a variety of kidney injury processes, but the role played by ferroptosis in hypertensive kidney injury is still unclear. The aim was to explore the mechanism of ferroptosis playing a role in hypertensive kidney disease and related signalling pathways.Methods{"type":"entrez-geo","attrs":{"text":"GSE37455","term_id":"37455"}}GSE37455 microarray data was downloaded from the Gene Expression Omnibus (GEO) database and preprocessed. Batch correction and differential analysis were performed on the normal population and the hypertensive nephropathy samples using the “sva” and “limma” packages in R software. Ferroptosis-related genes were obtained from the FerrDb database and normalized and processed using UniProt. Ferroptosis-related differentially expressed genes were obtained using Venny 2.1. and imported into the Search Tool for the Retrieval of Interacting Genes (STRING) to obtain protein-protein interactions (PPIs). The data were imported into Cytoscape 3.7.2 for processing to identify the core differential genes of ferroptosis based on nodes. Gene set enrichment analysis (GSEA) was performed on the core differential genes of ferroptosis to infer the pathway of ferroptosis action in hypertensive nephropathy.ResultsThe R software processing yielded 37 differential genes, including 13 upregulated genes and 24 downregulated genes. 202 ferroptosis-related genes were obtained by screening, and 3 ferroptosis-related differentially expressed genes were obtained after taking the intersection. The ferroptosis-related core differentially expressed gene albumin (ALB) was obtained by PPI network analysis and Cytoscape processing. GSEA analysis revealed that ferroptosis may act in hypertensive nephropathy through pathways such as drug metabolism-cytochrome P450, branched-chain amino acid (BCAA) metabolism, retinol metabolism, and biological processes (BPs) such as organic and amino acid metabolism and humoral immunity.ConclusionsFerroptosis may act in the development of hypertensive nephropathy through pathways such as BCAA metabolism and retinol metabolism and BPs such as organic and amino acid metabolism and humoral immunity.     

Mining Potential Drug Targets for Osteoporosis Based on CeRNA Network

Objective

To identify key pathological hub genes, micro RNAs (miRNAs), and circular RNAs (circRNAs) of osteoporosis (OP) and construct their ceRNA network in an effort to explore the potential biomarkers and drug targets for OP therapy.

Methods

GSE7158, GSE201543, and GSE161361 microarray datasets were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by comparing OP patients with healthy controls and hub genes were screened by machine learning algorithms. Target miRNAs and circRNAs were predicted by FunRich and circbank, then ceRNA network were constructed by Cytoscape. Pathways affecting OP were identified by functional enrichment analysis. The hub genes were verified by receiver operating characteristic (ROC) curve and real time quantitative PCR (RT-qPCR). Potential drug molecules related to OP were predicted by DSigDB database and molecular docking was analyzed by autodock vina software.

Results

A total of 179 DEGs were identified. By combining three machine learning algorithms, BAG2, MME, SLC14A1, and TRIM44 were identified as hub genes. Three OP-associated target miRNAs and 362 target circRNAs were predicted to establish ceRNA network. The ROC curves showed that these four hub genes had good diagnostic performance and their differential expression was statistically significant in OP animal model. Benzo[a]pyrene was predicted which could successfully bind to protein receptors related to the hub genes and it was served as the potential drug molecules.

Conclusion

An mRNA-miRNA-circRNA network is reported, which provides new ideas for exploring the pathogenesis of OP. Benzo[a]pyrene, as potential drug molecules for OP, may provide guidance for the clinical treatment.     

Re-Exploring Biomarkers and Therapeutic Targets in Primary Melanoma Patients: Insights from Network-Based Analysis of Microarray Data

ObjectiveTo identify novel biomarkers and therapeutic targets for primary melanoma using network-based microarray data analysis.MethodsEligible microarray datasets from the Gene Expression Omnibus (GEO) database were used to identify differentially expressed genes (DEGs). The protein-protein interaction (PPI) network, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to identify hub genes and pathways that might affect the survival of melanoma patients. Immunohistochemistry results obtained from the Human Protein Atlas (HPA) database confirmed the protein expression levels of hub genes. The Cancer Genome Atlas (TCGA) database was used to further verify the gene expression levels and conduct survival analysis.ResultsThree microarray datasets (GSE3189, GSE15605, and GSE46517) containing 122 melanoma and 30 normal skin tissue samples were included. A total of 262 common differentially expressed genes (cDEGs) were identified based on three statistical approaches (Fisher's method, the random effects model (REM), and vote counting) with strict criteria. Of these, two upregulated genes, centromere protein F (CENPF) and pituitary tumor-transforming gene 1 (PTTG1), were selected as hub genes. HPA and TCGA database analyses confirmed that CENPF and PTTG1 were overexpressed in melanoma. Survival analysis showed that high expression levels of CENPF were significantly correlated with decreased overall survival (OS) (P=0.028).ConclusionThe expression level of CENPF was significantly upregulated in melanoma and correlated with decreased OS. Thus, CENPF may represent a novel biomarker and therapeutic target for melanoma patients.     

高低峰值骨量人群生物标志物差异及其在骨质疏松中的诊疗价值

目的 通过生物信息学分析高低峰值骨量人群生物标志物的差异,并验证其在骨质疏松症中的诊疗价值。方法 从GEO数据库获取高低峰值骨量人群基因表达数据集（GSE7158）,利用R语言进行基因差异表达分析,然后进行差异基因GO功能注释和KEGG通路富集分析。利用STRING数据库获取差异基因蛋白互作网络,利用Cytoscape中的CytoHubba插件及R语言筛选得到关键基因及关键基因互作网络。最后,验证关键基因在骨质疏松症中的表达及诊疗价值。结果 共筛选得到高低峰值骨量人群差异表达基因182个,包括73个下调和109个上调基因。KEGG通路分析中破骨细胞分化通路、PI3K-AKT信号通路、糖尿病并发症中的AGE-RAGE信号通路和铁死亡信号通路值得关注。PPI网络分析得到11个关键基因：MCM7、BUB3、RBBP7、GNG2、FSHR、PCNA、CCR5、CDK16、SRSF7、NPM1和CPSF6;进一步验证分析发现CCR5、CDK16、RBBP7和SRSF7和骨质疏松症密切相关。结论 研究发现CCR5、CDK16、RBBP7和SRSF7可能与骨质疏松症的发病密切相关,可能成为早期筛选骨质疏松症高风险人群的生物标志物,对骨质疏松症的防治发挥重要作用,为后续进一步实验研究及临床治疗提供了有效依据。     

Myeloid-derived suppressor cell (MDSC) key genes analysis in rat anti-CD28-induced immune tolerance kidney transplantation

BackgroundIn the field of transplantation, inducing immune tolerance in recipients is of great importance. Blocking co-stimulatory molecule using anti-CD28 antibody could induce tolerance in a rat kidney transplantation model. Myeloid-derived suppressor cells (MDSCs) reveals strong immune suppressive abilities in kidney transplantation. Here we analyzed key genes of MDSCs leading to transplant tolerance in this model.MethodsMicroarray data of rat gene expression profiles under accession number {"type":"entrez-geo","attrs":{"text":"GSE28545","term_id":"28545"}}GSE28545 in the Gene Expression Omnibus (GEO) database were analyzed. Running the LIMMA package in R language, the differentially expressed genes (DEGs) were found. Enrichment analysis of the DEGs was conducted in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database to explore gene ontology (GO) annotation and their Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Their protein-protein interactions (PPIs) were provided by STRING database and was visualized in Cytoscape. Hub genes were carried out by CytoHubba.ResultsThree hundred and thirty-eight DEGs were exported, including 27 upregulated and 311 downregulated genes. The functions and KEGG pathways of the DEGs were assessed and the PPI network was constructed based on the string interactions of the DEGs. The network was visualized in Cytoscape; the entire PPI network consisted of 192 nodes and 469 edges. Zap70, Cdc42, Stat1, Stat4, Ccl5 and Cxcr3 were among the hub genes.ConclusionsThese key genes, corresponding proteins and their functions may provide valuable background for both basic and clinical research and could be the direction of future studies in immune tolerance, especially those examining immunocyte-induced tolerance.     

长链非编码RNA 909靶向调控miR-194-5p/DACH1轴对胰腺癌细胞增殖、迁移和侵袭的影响

背景与目的:长链非编码RNA 909(LINC00909)是一个新发现的长度约为2 kb的长链非编码RNA(lncRNA),在胶质瘤和白血病中被报道发挥癌基因的作用.然而LINC00909在胰腺癌中的作用鲜有报道.本研究旨探讨LINC00909在胰腺癌中的表达及其对患者预后的影响,以及LINC00909与其调控网络对胰...