肾移植术后BK病毒相关性肾病核心基因及免疫微环境的生物信息学分析

目的通过生物信息学方法分析肾移植术后BK病毒相关性肾病(BKVAN)的核心基因及其与浸润的免疫细胞相关性。 方法从美国国立生物技术信息中心基因表达综合数据库下载BKVAN相关数据集GSE75693和GSE72925,BK病毒(BKV)血症相关数据集GSE47199。合并GSE75693和GSE72925后筛选差异表达基因(DEGs),然后进行基因本体生物过程(GOBP)以及京都基因与基因组百科全书(KEGG)通路分析,并通过蛋白-蛋白相互作用(PPI)网络进一步筛选核心基因。使用CIBERSORT进行免疫浸润分析,然后计算差异的免疫细胞和核心基因的相关性。最后,在GSE47199数据集筛选BKV血症和BKVAN共同的核心基因,使用基因集富集分析(GSEA)鉴定共同的核心基因分别在BKVAN和BKV血症中的生物过程。所有统计分析及可视化均基于R语言(4.0.2)。P<0.05为差异有统计学意义。 结果在合并数据中共筛选出175个上调及70个下调DEGs。在PPI网络中,通过5种方法交集得到9个核心基因,核心基因主要富集在免疫细胞活化与功能相关的进程;在KEGG分析中,核心基因主要富集在病毒蛋白与细胞因子和细胞因子受体间相互作用、细胞因子-细胞因子受体间相互作用以及趋化因子信号通路等。免疫浸润分析表明PTPRC、CCL5、TYROBP、CXCL10、CD2和CXCL9与BKVAN中浸润的免疫细胞相关。CD2是BKVAN和BKV血症的共同核心基因。 结论通过生物信息学方法筛选出BKVAN的核心基因,其中PTPRC、CCL5、TYROBP、CXCL10、CD2和CXCL9与BKVAN中浸润的免疫细胞相关,CD2是BKVAN和BKV血症的共同核心基因,这些标志物为肾移植术后BKVAN的诊治提供依据。     

基于GEO数据库的胰腺癌循环肿瘤细胞代谢通路及关键基因研究

目的探讨胰腺癌循环肿瘤细胞（CTCs）中起关键作用的代谢通路及关键基因。方法 从GEO数据库筛选胰腺癌CTCs相关数据集,利用R studio软件筛选差异基因,与KEGG数据库的代谢相关基因比对,寻找代谢相关差异基因。对差异基因进行KEGG通路富集,使用STRING、Cytoscape进行蛋白相互作用网络分析和可视化。最后,对比两数据集富集的代谢通路,获得关键通路及基因,并利用TCGA和TIMER数据库分析关键基因与临床特征和免疫浸润的关系。结果 分别从数据集GSE118556和GSE18670筛选出834个和1119个差异基因。前者基因主要富集到半胱氨酸和蛋氨酸代谢、一碳代谢和辅酶因子合成等代谢通路,后者基因主要富集到一碳代谢、嘌呤代谢和甘油磷脂代谢通路。其中转酮醇酶（TKT）在两个数据集的一碳代谢中均显著上调。TKT与总体生存期、肿瘤分期、组织学分级相关（P<0.05）。同样编码转酮醇酶同工酶的TKTL1和TKTL2与免疫浸润相关（P<0.05）。结论 通过对胰腺癌CTCs数据集的生物信息学分析,发现一碳代谢和TKT可能在CTCs的形成和维持中起关键作用,为进一步研究胰...     

肝细胞癌发生发展关键基因及其功能的生物信息学分析

背景与目的:肝细胞癌(HCC)是常见的原发性肝癌,其预后较差.基因的激活与失活可促进HCC的发生发展.本研究基于生物信息学HCC发生发展的关键基因及功能并进行临床样本表达验证.方法:从公共基因GEO数据库中筛选HCC及癌旁组织基因芯片,通过GE02R在线工具及Venn图筛选出差异表达基因(DEGs),对筛选出来的DEG...     

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。     

Gene expression patterns in isolated keloid fibroblasts

Keloid scars after skin trauma are a significant clinical problem, especially in black populations, in which the incidence of keloids has been estimated at 4-16%. Keloids are abnormal dermal proliferative scars secondary to dysregulated wound healing. Despite several biochemical studies on the role of extracellular matrix proteins and growth factors during keloid formation, we still do not know what molecules and signals induce this change. Fibroblasts are thought to be the major inductive cell for keloid scar formation. The aim of this study was to identify gene expression patterns that characterize keloid fibroblasts; identifying such genetic disequilibrium may shed light on the molecular signaling events responsible for keloid formation. In this study, we performed gene expression analysis of fibroblasts isolated from keloid lesions from three individuals in comparison with the fibroblasts isolated from normal skin using the Affymetrix U133a chip (22,284 genes and expression sequence tags). We found through J5 test score expression analysis that among 22,284 genes, there were 43 genes that were overexpressed and five genes were underexpressed in keloid fibroblasts when compared with dermal fibroblasts from persons without keloids. The overexpression of three genes not previously reported as being up-regulated in keloids (annexin A2, Transgelin, and RPS18) was confirmed by real-time polymerase chain reaction. Certain overexpressed genes were similar to previous biochemical observations on the protein levels of these overexpressed genes during keloid formation. We also report for the first time that a few tumor-related genes are overexpressed in keloid fibroblasts.     

生物信息学方法研究高脂饮食诱导肥胖对雄性SD大鼠的影响

目的应用生物信息学方法探寻高脂饮食诱导性肥胖雄性SD大鼠生理代谢改变及对生育力的影响。方法利用NCBI中的GEO基因芯片公共数据库进行芯片数据搜索,最终选择芯片数据(GSE8700)作为分析对象,使用bioconductor包中R工具的函数及Limma程序包识别差异性表达基因,应用DAVID数据库对差异表达基因进行GO富集分析和KEGG通路分析,选用String在线数据库构建差异表达基因的PPI网络。结果通过对GSE8700进行分析,得到1 014个表达差异基因,其中上调基因544个,下调基因470个;上调差异基因GO条目全部富集于生物过程(BP),主要为氧化还原、轴突生成、对肽类激素反应、对糖皮质激素反应过程;下调差异基因GO富集于生物过程(BP),主要为女性怀孕、对类固醇激素的反应、甘油三酯代谢等过程;富集于细胞组成(CC),主要为细胞外间质,细胞质,血液微球等组成;富集于分子功能(MF),主要为丝氨酸肽链内切酶活性、脂肪酸结合、磷脂质结合等功能;上调差异基因并未富集到任何KEGG通路,而下调差异基因富集到3条通路,分别为PPAR信号通路(过氧化物酶体增殖物激活型受体)、脂肪的消化和吸收通路、胰腺分泌通路,其中重要的节点基因为热休克蛋白90AB1(Hsp90ab1)、细胞外钙敏感受体(Casr)及趋化因子9(Ccl9)等。结论高脂饮食诱导肥胖雄性SD大鼠脂质代谢发生了紊乱,大鼠生殖功能可能受到影响,类固醇激素、肽类激素代谢异常可能是其影响途径。     

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.     

Transcriptional changes in orthotopic liver transplantation and ischemia/reperfusion injury

BackgroundThere are few effective targeting strategies to reduce liver ischemia-reperfusion injury (IRI), which is one of the reasons for the poor prognosis of liver transplant recipients.MethodsA systematic approach combining gene expression with protein interaction (PPI) network was used to screen the characteristic genes and related biological functions of post-transplant. Differentially expressed genes (DEGs) between IRI+ and IRI- were identified. Logistic regression model and receiver operating characteristic (ROC) curve were used to identify potential target genes of IRI. The expression of key genes was verified by qRT-PCR and Western-blot experiments. Finally, the ssGSEA was used to identify the immune cell infiltration in patients with IRI.ResultsThe 283 common DEGs in GSE87487 and GSE151648 were mainly related to apoptosis and IL-17 signaling pathway. Through PPI network and logistic regression analysis, we identified that IL6, CCL2 and CXCL8 may be involved in the ischemia/reperfusion (IR) process. In addition, 32 genes were showed associated with IRI through inflammatory and metabolic pathways. Among the key genes identified, the differential expression of AGBL4, CILP2 and IL4I1 was verified by molecular experiments. Th17 cells of differentially infiltrated immune cells were positively correlated with CILP2 and IL4I1. The difference of Th17 cells between IRI+ and IRI- was verified by flow cytometry.ConclusionThe study showed that AGBL4, CILP2 and IL4I1 were associated with IRI. Th17 cells may be associated with the regulation of IRI by key genes. These genes and related pathways may be targets for improving IRI.     

瘢痕疙瘩成纤维细胞的基因组学研究

目的 寻找瘢痕疙瘩致病相关基因，探讨瘢痕疙瘩的发生机理。方法 利用含1100个人类肿瘤相关基因的cDNA芯片(cDNA—microarray)对耳垂和胸部瘢痕疙瘩及正常皮肤成纤维细胞进行检测，初步分析瘢痕疙瘩成纤维细胞与正常皮肤成纤维细胞基因总体表达的差异，并筛选出差异基因。结果 在耳垂及胸部瘢痕疙瘩成纤维细胞中，分别有8种和17种特异性表达基因被检出。在正常皮肤中特异性表达的细胞增殖抑制基因Mda-7，在耳垂及胸部瘢痕疙瘩成纤维细胞中均未被表达。结论 多种基因参与了瘢痕疙瘩的形成过程，瘢痕疙瘩成纤维细胞与正常皮肤成纤维细胞之间存在基因表达的差异，增殖因子受体PAR-1和增殖抑制基因Mda-7可能参与瘢痕疙瘩的形成。     

基于生物信息学胰腺腺癌关键基因的筛选及支持向量机诊断模型的构建

背景与目的：胰腺癌是一种常见的消化道恶性肿瘤,其主要病理类型为胰腺腺癌（PAAD）,因早期诊断困难且缺乏有效的治疗措施,故预后极差。因此,寻找PAAD的诊治新靶标具有重要意义。本研究通过生物信息学方法筛选与PAAD诊断和预后相关的关键基因,构建分类PAAD样本和正常样本的支持向量机（SVM）模型,以期为PAAD的诊治及机制研究提供依据。 方法：从基因表达数据库（GEO）中下载3个芯片数据（GSE28735、GSE62165、GSE62452）,应用R语言的Limma包筛选出PAAD组织和正常组织间的差异表达基因（DEGs）。利用STRING数据库对DEGs进行GO和KEGG通路富集分析。再以STRING数据库构建DEGs的蛋白互作网络（PPI）,利用Cytoscape软件进行可视化编辑,并通过MCODE插件进行关键子网络分析。使用R语言的survival包筛选PPI和关键子网络中与预后相关的关键节点,将其上传至Metascape进行功能富集分析。利用R语言caret包中递归式特征消除（RFE）算法筛选关键节点中的最优特征基因,在GEPIA数据库中验证最优特征基因的表达差异,随后通过R语言的e1071包构建最优特征基因的SVM模型,并在3个芯片数据中借助R语言的pROC包对该模型进行验证。在TCGA数据库中,用R语言的survminer包筛选出最优特征基因中与PAAD预后相关的基因作为关键基因。 结果：共筛选出257个DEGs,包括168个上调基因和89个下调基因。GO分析结果表明DEGs主要参与细胞外基质的组成、细胞黏附、丝氨酸肽酶活性等生物学过程。KEGG分析显示,DEGs主要富集于蛋白质的消化和吸收、胰腺的分泌、黏着斑、PI3K-Akt信号通路。生存分析筛选出14个关键节点同时在GSE28735和GSE62452中与预后相关（均P<0.05）,这些基因在肿瘤侵犯和肿瘤发生中发挥一定作用。RFE筛选出8个最优特征基因：LAMA3、FN1、ITGA3、MET、PLAU、CENPF、MMP14、OAS2;GEPIA数据库验证发现这8个最优特征基因在PAAD组织中明显上调（均P<0.01）;这些基因构建的SVM模型在3个芯片数据中ROC曲线的AUC依次为0.898、1.000、0.905。TCGA数据库验证发现LAMA3、ITGA3、MET、PLAU、CENPF及OAS2的上调与PAAD预后不良有关（均P<0.05）。 结论：关键基因LAMA3、ITGA3、MET、PLAU、CENPF及OAS2可能成为PAAD诊治的新靶点;基于8个最优特征基因构建的SVM模型可有效诊断PAAD。     

Identification of new biomarkers in immune microenvironment of testicular germ cell tumour

To seek novel prognostic biomarkers for testicular germ cell tumour (TGCT) and investigate the tumour immune microenvironment, we identified critical differentially expressed genes (DEGs) by overlapping GSE1818 dataset from Gene Expression Omnibus (GEO). Protein–protein interaction (PPI) network was used to investigate key modules and hub genes. Functional enrichment analysis was performed to investigate the underlying molecular functions of the DEGs in TGCT development and progression. The following survival analysis based on The Cancer Genome Atlas (TCGA) TGCT dataset indicated that AKAP4, SPA17 and TNP1 are correlated with TGCT prognosis. Immunohistochemistry and quantitative real-time polymerase chain reaction verified the down-regulation of the 3 hub genes in TGCT. Gene set enrichment analysis was conducted to further explore the role of the 3 hub genes in TGCT respectively. In addition, TGCT samples had high infiltration of CD8+ T cells, M0 and M1 macrophage cells, and resting myeloid dendritic cells in immune microenvironment. We also constructed the microRNA-gene regulatory networks to identify the key upstream microRNAs in TGCT. In conclusion, our findings indicated that AKAP4, SPA17 and TNP1 are promising biomarkers of TGCT. AKAP4 and TNP1 might regulate immune cells infiltration in immune microenvironment.     

Analysis of differences of gene expressions in keloid and normal skin with the aid of cDNA microarray

Background: Microarray analysis is a popular tool to investigate the function of genes that are responsible for the phenotype of the disease. Keloid is a intricate lesion which is probably modulated by interplay of many genes. We ventured to study the differences of gene expressions between keloids and normal skins with the aid of cDNA microarray in order to explore the molecular mechanism underlying keloid formation. Methods: The PCR products of 8400 human genes were spotted on a chip in array. The DNAs were then fixed on the glass plate by a series of treatments. Total RNAs was isolated from freshly excised human keloids and normal skin, and then was purified to mRNA by Oligotex. Both the mRNA from keloids and normal skin was reversely transcribed to cDNAs with the incorporations of fluorescent dUTP, for preparing the hybridization probes. The mixed probes were then hybridized to the cDNA microarray. After highly stringent washing, the cDNA microarray was scanned for the fluorescent signals to display the differences between two kinds of tissues. Results: Among 8400 human genes, there were 402 genes (4.79%) with different expression levels between the keloids and normal skins in all cases, 250were up-regulated (2.98%) and 152 down-regulated (1.81%). Analyses of collagen, fibronectin, proteoglycan,growth factors and apoptosis related molecule gene expression confirmed that our molecular data obtained by cDNA microarray were consistent with published biochemical and clinical observations of keloids. Conclusions: DNA microarray technology is an effective technique in screening for differences in gene expression between keloid and normal skin. Many genes are involved in the formation of keloids. Further analysis of the obtained genes will help understand the molecular mechanism of keloid formation.     

用生物信息学分析预测绝经后骨质疏松症核心基因与互作miRNA的研究

目的揭示参与绝经后骨质疏松症(postmenopausal osteoporosis,PMOP)生理病理过程的核心基因,并预测可能与之相互作用的微小核糖核酸(micro-ribonucleic acid,miRNA)。方法选取NCBI基因表达综合数据库基因芯片GSE57273,应用GEO2R和Morpheus分析软件获得差异基因(differentially expressed genes,DEGs),并通过DAVID(Database for Annotation,Visualization and Integrated Discovery)进行功能富集分析。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计算DEGs的各个连接度并分析网络集簇模块。由CyTargetLinker预测与核心基因互作的miRNA。结果本研究共获得841个DEGs,其功能主要富集于基因表达过程,细胞大分子生物合成过程等。蛋白相互作用网络共包含523个节点与2 026条连线。本研究列出了前3个集簇模块,同时筛选出10个核心基因:HSP90AA1、EP300、SMARCA2、RANBP2、ASH1L、EIF4E、PTEN、CNOT6L、RPL7、KRAS,并预测出37个miRNA可与其中7个核心基因靶向性相互作用。结论核心基因与其相互作用的miRNA的发现可能有助于了解PMOP的病理机制,或为药物的开发提供治疗靶点。同时,通过对核心基因富集功能的鉴定为PMOP建立新的科学假说提供依据。     

Effects of left ventricular assist device on heart failure patients: A bioinformatics analysis

With the acceleration of demographic aging, heart failure has become a global public health issue. Left ventricular assist device (LVAD) provides a therapeutic option serving as a bridge to transplantation or destination treatment for end-stage heart failure. However, neither the molecular mechanism nor the gene expression profile of LVAD pathophysiology is well understood. Microarray dataset ( GSE21610 ) was retrieved from the online database of the gene expression omnibus (GEO). Differentially expressed genes (DEGs) between microarrays obtained before and after LVAD therapy were analyzed using GEO2R. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis were carried out, followed by protein–protein interaction (PPI) network construction, which was further visualized by the Cytoscape software. Finally, a target gene-microRNA (miRNA) network was built using the NetworkAnalyst to predict potential miRNA interactions. A total of 36 upregulated DEGs and 14 downregulated DEGs were screened out. Five hub genes with the highest degree of connectivity were identified, including CCL2, CX3CR1, CD163, TLR7, and SERPINE1. CCL2 was identified as the most outstanding hub gene which is specially regulated by miR-124, miR-141, and miR-495. Our study indicates that CCL2 is crucial to the LVAD pathophysiology. The identified hub genes may be involved in cardiac inflammatory responses, remodeling, and the chemokine signaling pathway. These DEGs, pathways, hub genes, miRNAs are valuable for further investigations. This study provides a better understanding of the gene expression profile in LVAD pathophysiology.