伴HBV感染性肝癌调控枢纽基因筛选与初步鉴定

慢性乙型肝炎病毒(Hepatitis B virus,HBV)感染引起的原发性肝癌涉及多种基因、转录本和蛋白质的相互作用及调控。从单个基因的角度来看,某个基因的表达量的改变只能对肝癌发生发展的局部作出解释而无法从整体行为进行深入和全面的探索,无法满足高度复杂性的调控研究需要。筛选乙肝相关性肝癌的基因芯片数据获取差异表达基因后,应用加权基因共表达网络分析算法构建基因共表达网络,识别与肝癌发生相关的模块,利用可视化筛选枢纽基因,并针对枢纽基因进行基因本体富集分析和初步验证。富集分析和文献挖掘一致发现,某些枢纽基因确实与多种癌症的发生与发展存在显著的关联。权重基因共表达网络分析方法被证明是一个高效的系统生物学方法,应用该方法发现了新的HBV相关性肝癌枢纽基因。经实验验证,发现枢纽基因SHARPIN促进细胞迁移。该研究对肝癌发生的调控机制以及发现HBV慢性感染导致肝癌的新型诊断标志物和(或)药物作用靶点提供了新的视野。     

应用GSEA和WGCNA方法分析细菌性败血症宿主关键差异基因及意义

【目的】采用生物信息学方法分析公共数据库来源的细菌性败血症患者全血转录组学表达谱,探讨细菌败血症相关的宿主关键差异基因及意义。【方法】基于GEO数据库中GSE80496和GSE72829全血转录组基因数据集,采用GEO2R、基因集富集分析(GSEA)联用加权基因共表达网络分析(WGCNA)筛选细菌性败血症患者相比健康人群显著改变的差异基因,通过R软件对交集基因进行GO功能分析和KEGG富集分析。同时,通过String 11.0和Cytoscape分析枢纽基因,验证枢纽基因在数据集GSE72809(Health组52例,Definedsepsis组52例)全血标本中的表达情况,并探讨婴儿性别、月(胎)龄、出生体重、是否接触抗生素等因素与靶基因表达谱间的关系。【结果】分析GSE80496和GSE72829数据集分别筛选得到932个基因和319个基因,联合WGCNA枢纽模块交集得到与细菌性败血症发病相关的10个枢纽基因(MMP9、ITGAM、CSTD、GAPDH、PGLYRP1、FOLR3、OSCAR、TLR5、IL1RN和TIMP1);GSEA分析获得关键通路(氨基酸糖类-核糖代谢、PPAR信号通路、聚糖生物合成通路、自噬调控通路、补体、凝血因子级联反应、尼古丁和烟酰胺代谢、不饱和脂肪酸生物合成和阿尔兹海默症通路)及生物学过程(类固醇激素分泌、腺苷酸环化酶的激活、细胞外基质降解和金属离子运输)。【结论】本项研究通过GEO2R、GSEA联用WGCNA分析,筛选出与细菌性败血症发病相关的2个枢纽模块、10个枢纽基因以及一些关键信号通路和生物学过程,可为后续深入研究细菌性败血症致病机制奠定理论依据。     

肾透明细胞癌Caki-1细胞系差异表达基因的生物信息学分析

目的比较肾透明细胞癌Caki-1细胞系与正常肾上皮细胞系ASE-5063中的差异表达基因（DEGs）,寻找潜在的肾透明细胞癌特异性分子标志物。 方法利用GEO数据库自带的GEO2R在线分析工具分析基因芯片GSE78179,将筛选出的DEGs分别导入Metascape、STRING以及Cytoscape进行综合分析并筛选出核心基因。最后使用FunRich等软件对筛选出的核心基因进行GO和KEGG富集分析。 结果共筛选出562个DEGs,其中上调基因345个,下调基因217个。进一步使用MCODE筛选出36个关键基因,GO功能分析发现这些基因与细胞粘附分子活性、趋化因子活性、细胞通讯和信号转导等密切相关;KEGG通路富集结果则表明差异基因主要集中在趋化因子信号通路、TNF信号通路以及NF-κB信号通路等多种与肿瘤相关的通路上。 结论运用生物信息学方法筛选出肾透明细胞癌Caki-1细胞系中DEGs,其中数个核心基因广泛参与多种肿瘤的病理进程,但尚未在肾透明细胞癌有相关研究报道,提示其可能是治疗肾透明细胞癌的潜在靶点。     

基于TCGA数据库分析甲状腺癌基因表达谱

为分析甲状腺癌基因表达谱,筛选疾病相关的基因标志物。基于肿瘤基因组图谱(TCGA)数据库中的甲状腺癌基因表达数据,运用R/Bioconductor统计平台进行数据处理与统计学分析。分别应用edgeR算法和limma算法选取肿瘤组织与对照组间倍数改变 > 2,P< 0.05的基因作为差异基因;进一步运用Medcalc统计软件进行受试者工作特征曲线(ROC)分析,鉴定出有诊断标志物潜在应用价值的基因标志物。通过两种运算方法筛选出甲状腺癌组织中存在着1 945个差异基因(上调基因1 033个,下调基因912个);根据差异倍数进一步鉴定出11个基因在肿瘤组织中表达上调,且对鉴别肿瘤组与对照组有较好的应用价值。本研究分析了TCGA中的甲状腺癌表达谱数据,鉴定出了与疾病诊断显著相关的差异表达基因,能够为探索疾病发生发展机制及寻找新型分子标志物提供依据。     

基于生物数据挖掘分析PTX3在非小细胞肺癌中的表达及其与预后的关系

摘要 目的：探讨正五聚素蛋白 3（PTX3）在非小细胞肺癌（NSCLC）中的表达及预后意义。方法：运用 Oncomine、GEPIA分析PTX3在NSCLC组织中的表达情况，通过GEPIA分析PTX3表达与NSCLC患者生存期的相关性，利用CCLE分析 PTX3在 NSCLC细胞系中的表达水平，从CCLE下载NSCLC相关基因芯片并用 R语言筛选 PTX3共表达基因，利用基因本体（GO）和KEGG信号通路分析对 PTX3 相关共表达基因进行功能注释。结果：Oncomine和GEPIA 数据库中分析显示 PTX3 基因在NSCLC组织中显著低表达（P＜0.05）；利用GEPIA数据库生存分析功能发现，PTX3高表达与NSCLC预后呈负相关（P＜0.05）；在CCLE数据库里利用 R 软件共筛选出 105个NSCLC中与PTX3共表达的基因，GO功能富集分析表明，PTX3相关性蛋白主要定位于黏着斑、细胞-基质黏着连接及细胞间连接等，主要参与细胞外基质、细胞外结缔组织、细胞-基质粘附及上皮细胞发育等生物过程。KEGG分析显示PTX3共表达基因主要参与紧密连接、调节肌动蛋白骨架及JAK-STAT信号通路等。结论：PTX3基因在NSCLC组织中低表达，PTX3表达与NSCLC患者预后相关，可能作为NSCLC患者预后评估的分子标志物之一。     

胶质母细胞瘤预后相关基因的数据挖掘分析

胶质母细胞瘤（glioblastoma, GBM）是恶性程度最高的颅内恶性肿瘤，目前临床上缺乏有效治疗药物，复发率高且预后差，开发新的抗GBM药物是目前临床上亟待解决的问题。为了筛选与GBM预后密切相关的基因，为寻找新的药物靶点提供线索，采用GEO2R工具从GEO数据库中的269个肿瘤组织和61个正常组织中初步筛选出差异表达基因，然后利用Cluster Profiler数据库进行基因功能富集分析，STRING及Cytoscape进一步筛选出37个差异表达基因，采用GEPIA交互分析对这37个基因在GBM肿瘤组织中的表达进行验证。为了进一步探索这些差异表达基因与患者预后的关系，研究中利用GEPIA工具对TCGA数据库中与患者预后相关的数据进行深入挖掘，最终发现PTTG1、RRM2、E2F7与患者中位生存期呈显著性负相关。研究筛选出的与患者预后密切相关的基因不仅可以为评估患者预后提供参考，同时也为开发新的抗GBM药物提供了潜在的靶点。     

基于共表达网络挖掘不同前列腺特异抗原水平下的前列腺癌发展相关基因

目的采用权重基因共表达网络分析方法(WGCNA)挖掘不同前列腺特异抗原(PSA)水平下的前列腺癌发展枢纽基因。方法数据来自NCBI的GEO数据库中下载不同PSA水平下的前列腺癌全基因组表达数据集,经过数据预处理后,用WGCNA构建基因共表达网络,识别不同PSA水平下的前列腺癌发展模块与枢纽基因。结果筛选出的差异基因聚集成一个模块,并且找到10个枢纽基因。结论结合文献发现,SNAI2、TRIM29、LAMB3、CYP3A5和SLC14A1这5个基因很可能影响不同PSA水平下前列腺癌的发展。     

以LPAR3为中心的2型糖尿病相关基因功能富集及蛋白互作分析

本研究目的是查找与2型糖尿病相关的潜在基因及其参与的生物过程、信号通路和蛋白互作网络。利用GEO数据库中GSE20966数据集,采用GENE-E平台,筛选出与LPAR3共表达的基因,结合生物信息学工具GOC、DAVID、COREMINE、Gene Mania对共表达基因进行基因功能富集分析、文本挖掘及蛋白互作分析。我们筛选出LPAR3在2型糖尿病患者胰腺β细胞中表达值显著低于正常对照组,其共表达基因603个,主要涉及代谢过程、信号调控等生物过程和Hippo信号通路。共表达相关系数高的8个基因与2型糖尿病相关,且均与肿瘤相关,涉及代谢过程、信号转导、发病机理、细胞增殖、细胞粘附等生物过程。LPAR3与20个已报道的2型糖尿病基因存在互作关系。本研究发现LPAR3及其共表达基因可能与2型糖尿病相关,并可能增加患者罹患各系统肿瘤的风险。     

基于生物信息学和分子对接筛选结直肠腺癌的潜在生物标志物及其靶向药物

[目的]获取结直肠癌(Colon adenocarcinoma, COAD)的潜在生物标志物及其靶向药物。[方法]用R和Cytoscape 3.8.2软件分析临床数据库中56例COAD患者和45例正常样本的转录组数据,获取生物标志物及其共表达基因并分析其生物功能。通过miRWalk和miRTarBase数据库得到与COAD生存预后相关的miRNAs并分析它们的功能。利用分子对接筛选与所获生物标志物具有较好结合能力的靶向中药小分子化合物。[结果]共筛选得到179个差异表达基因,包括49个高表达基因和130个低表达基因。STRING和Cytoscape 3.8.2所得30个Hubba基因中仅CLCA1符合Cox模型(C-index:0.708(0.632-1),P<0.001)。CLCA1及其共表达基因主要参与氯离子跨膜转运过程,与肿瘤的发生发展密切相关(P<0.01)。mRNA-miRNA网络中与CLCA1密切相关的10个miRNA主要参与肿瘤细胞的增殖,凋亡和血管的形成。且甘草次酸、小檗碱和雷公藤红素被证实与CLCA1结合较牢。[结论]CLCA1是COAD的潜在生物标志物,...     

CIAPIN1基因与肿瘤

细胞因子诱导的凋亡抑制因子1(cytokine induced apoptosis inhibitor1,CIAPIN1)是最新发现的一个细胞因子依赖性抗凋亡分子,并已经被证实是独立于Bcl家族、胱天蛋白酶(caspase)家族等之外的Ras信号转导通路中的另一个调节分子。CIAPIN1广泛分布于胎儿和成人的正常组织中,特别在分化型组织和活性代谢组织中具有很高的表达水平,但是在某些癌症发生时表达受到抑制。通过基因转染、RNA干扰等技术手段研究CIAPIN1与肿瘤发生、发展的关联,揭示了CIAPIN1表达水平的改变与肿瘤进展具有相关性,CIAPIN1有望成为一个新的肿瘤治疗靶分子。     

Screening and identification of hub genes in pancreatic cancer by integrated bioinformatics analysis

Pancreatic cancer (Pa) is a malignant tumor of the digestive tract with high degree of malignancy, this study aimed to obtain the hub genes in the tumorigenesis of Pa. Microarray datasets GSE15471, GSE16515, and GSE62452 were downloaded from Gene Expression Omnibus (GEO) database, GEO2R was conducted to screen the differentially expressed genes (DEGs), and functional enrichment analyses were carried out by Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Genes (STRING), and the hub genes were identified by Cytoscape. Totally 205 DEGs were identified, consisting of 51 downregulated genes and 154 upregulated genes enriched in Gene Ontology terms including extracellular matrix (ECM) organization, collagen binding, cell adhesion, and pathways associated with ECM-receptor interaction, focal adhesion, and protein digestion. Two modules in the PPI were chosen and biological process analyses showed that the module genes were mainly enriched in ECM and cell adhesion. Twenty-four hub genes were confirmed, the survival analyses from the cBioPortal online platform revealed that topoisomerase (DNA) II α (TOP2A), periostin (POSTN), plasminogen activator, urokinase (PLAU), and versican (VCAN) may be involved in the carcinogenesis and progression of Pa, and the receiver-operating characteristic curves indicated their diagnostic value for Pa. Among them, TOP2A, POSTN, and PLAU have been previously reported as biomarkers for Pa, and far too little attention has been paid to VCAN. Analysis from R2 online platform showed that Pa patients with high VCAN expression were more sensitive to gemcitabine than those with low level, suggesting that VCAN may be an indicator to guide the use of the chemotherapeutic drug. In vitro experiments also showed that the sensitivity of the VCAN siRNA group to gemcitabine was lower than that of the control group. In conclusion, this study discerned hub genes and pathways related to the development of Pa, and VCAN was identified as a novel biomarker for the diagnose and therapy of Pa.     

Gene expression analysis in LLC-PK1 renal tubular cells by atrial natriuretic peptide (ANP): correlation of homologous human genes with renal response

We used human DNA microarray to explore the differential gene expression profiling of atrial natriuretic peptide (ANP)-stimulated renal tubular epithelial kidney cells (LLC-PK1) in order to understand the biological effect of ANP on renal kidney cell's response. Gene expression profiling revealed 807 differentially expressed genes, consisting of 483 up-regulated and 324 down-regulated genes. The bioinformatics tool was used to gain a better understanding of differentially expressed genes in porcine genome homologous with human genome and to search the gene ontology and category classification, such as cellular component, molecular function and biological process. Four up-regulated genes of ATP1B1, H3F3A, ITGB1 and RHO that were typically validated by real-time quantitative PCR (RT-qPCR) analysis serve important roles in the alleviation of renal hypertrophy as well as other related effects. Therefore, the human array can be used for gene expression analysis in pig kidney cells and we believe that our findings of differentially expressed genes served as genetic markers and biological functions can lead to a better understanding of ANP action on the renal protective system and may be used for further therapeutic application.     

Integrative analysis of miRNA–mRNA network in high altitude retinopathy by bioinformatics analysis

High-altitude retinopathy (HAR) is an ocular manifestation of acute oxygen deficiency at high altitudes. Although the pathophysiology of HAR has been revealed by many studies in recent years, the molecular mechanism is not yet clear. Our study aimed to systematically identify the genes and microRNA (miRNA) and explore the potential biomarkers associated with HAR by integrated bioinformatics analysis. The mRNA and miRNA expression profiles were obtained from the Gene Expression Omnibus database. We performed Gene Ontology functional annotations and Kyoto Encyclopedia of Genes and Genomes pathway analysis. Potential target gene analysis and miRNA–mRNA network analysis were also conducted. Quantitative RT-PCR (qRT-PCR) was used to validate the results of the bioinformatics analysis. Through a series of bioinformatics analyses and experiments, we selected 16 differentially expressed miRNAs (DE-miRNAs) and 157 differentially expressed genes related to acute mountain sickness (AMS) and constructed a miRNA–mRNA network containing 240 relationship pairs. The hub genes were filtered from the protein-protein interaction network: IL7R, FOS, IL10, FCGR2A, DDX3X, CDK1, BCL11B and HNRNPH1, which were all down-regulated in the AMS group. Then, nine up-regulated DE-miRNAs and eight hub genes were verified by qRT-PCR in our hypoxia-induced HAR cell model. The expression of miR-3177-3p, miR-369-3p, miR-603, miR-495, miR-4791, miR-424-5p, FOS, IL10 and IL7R was consistent with our bioinformatics results. In conclusion, FOS, IL10, IL-7R and 7 DE-miRNAs may participate in the development of HAR. Our findings will contribute to the identification of biomarkers and promote the effective prevention and treatment of HAR in the future.     

Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis

Renal cell carcinoma (RCC) is the most common type of renal tumor, and the clear cell renal cell carcinoma (ccRCC) is the most frequent subtype. In this study, our aim is to identify potential biomarkers that could effectively predict the prognosis and progression of ccRCC. First, we used The Cancer Genome Atlas (TCGA) RNA-sequencing (RNA-seq) data of ccRCC to identify 2370 differentially expressed genes (DEGs). Second, the DEGs were used to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Moreover, we identified the yellow module, which was strongly related to the histologic grade and pathological stage of ccRCC. Then, the functional annotation of the yellow module and single-samples gene-set enrichment analysis of DEGs were performed and mainly enriched in cell cycle. Subsequently, 18 candidate hub genes were screened through WGCNA and protein–protein interaction (PPI) network analysis. After verification of TCGA’s ccRCC data set, Gene Expression Omnibus (GEO) data set (GSE73731) and tissue validation, we finally identified 15 hub genes that can actually predict the progression of ccRCC. In addition, by using survival analysis, we found that patients of ccRCC with high expression of each hub gene were more likely to have poor prognosis than those with low expression. The receiver operating characteristic curve showed that each hub gene could effectively distinguish between localized and advanced ccRCC. In summary, our study indicates that 15 hub genes have great predictive value for the prognosis and progression of ccRCC, and may contribute to the exploration of the pathogenesis of ccRCC.     

生物信息学分析筛选结直肠癌靶基因及评估预后价值

为寻找与结直肠癌发展和预后相关的潜在关键基因及信号通路。从美国国立信息中心NCBI的GEO数据库获得结直肠癌基因表达数据集GSE106582,通过PCA对样本进行分组,利用GEO2R进行综合分析,筛选结直肠癌与癌旁对照组的差异表达基因;通过DAVID在线工具对差异表达基因进行GO本体分析和KEGG通路富集分析,初步分析差异表达基因的生物学作用;基于STRING数据库对差异表达基因进行蛋白质相互作用网络分析,利用Cytoscape软件进行可视化并筛选关键基因;用生存分析和ROC曲线诊断对关键基因进行鉴定并通过数据集GSE21510进行验证。共鉴定出199个差异表达基因,其中53个为上调基因,146个为下调基因;上调的差异表达基因主要富集在与胶原蛋白分解代谢过程、细胞外基质分解、细胞外基质受体相互作用和PI3K/AKT信号通路等生物学过程;下调的差异表达基因主要富集在碳酸氢盐运输、一碳代谢过程、矿物质吸收、药物代谢-细胞色素P450和氮代谢通路等生物学过程;MCODE分析、生存分析和ROC诊断共发现3个基因分别为BGN、COL1A2和TIMP1可能与结直肠癌的发生发展有关,它们在肿瘤组织中的异常高表达与患者较差的生存期呈正相关,GSE21510的验证结果与GSE106582的分析结果相同。本研究采用生物信息学方法对CRC基因芯片数据进行挖掘,从基因水平探讨CRC潜在的发病机制、肿瘤标志物的及患者预后分子的筛选,以及可能的药物治疗靶点提供了一定的参考价值和理论基础。     

Ossification of the posterior longitudinal ligament related genes identification using microarray gene expression profiling and bioinformatics analysis

Ossification of the posterior longitudinal ligament (OPLL) is a kind of disease with physical barriers and neurological disorders. The objective of this study was to explore the differentially expressed genes (DEGs) in OPLL patient ligament cells and identify the target sites for the prevention and treatment of OPLL in clinic. Gene expression data GSE5464 was downloaded from Gene Expression Omnibus; then DEGs were screened by limma package in R language, and changed functions and pathways of OPLL cells compared to normal cells were identified by DAVID (The Database for Annotation, Visualization and Integrated Discovery); finally, an interaction network of DEGs was constructed by string. A total of 1536 DEGs were screened, with 31 down-regulated and 1505 up-regulated genes. Response to wounding function and Toll-like receptor signaling pathway may involve in the development of OPLL. Genes, such as PDGFB, PRDX2 may involve in OPLL through response to wounding function. Toll-like receptor signaling pathway enriched genes such as TLR1, TLR5, and TLR7 may involve in spine cord injury in OPLL. PIK3R1 was the hub gene in the network of DEGs with the highest degree; INSR was one of the most closely related genes of it. OPLL related genes screened by microarray gene expression profiling and bioinformatics analysis may be helpful for elucidating the mechanism of OPLL.     

Identification of biomarkers correlated with hypertrophic cardiomyopathy with co-expression analysis

Hypertrophic cardiomyopathy (HCM) is reported to be the most common genetic heart disease. To identify key module and candidate biomarkers correlated with clinical prognosis of patients with HCM, we carried out this study with co-expression analysis. To construct a co-expression network of hub genes correlated with HCM, the Weighted Gene Co-expression Network Analysis (WGCNA) was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction network analysis of central genes was performed to recognize the interactions of central genes. Gene set enrichment analyses were carried out to discover the possible mechanisms involved in the pathways promoted by hub genes. To validate the hub genes, quantitative real-time polymerase chain reaction (RT-PCR) was performed. Based on the results of topological overlap measure based clustering, 2,351 differentially expressed genes (DEGs) were identified. Those genes were included in six different modules. Of these modules, the yellow and the blue modules showed a pivotal correlation with HCM. DEGs were enriched in immune system procedure associated GO terms and KEGG pathways. We identified nine hub genes (TYROBP, STAT3, CSF1R, ITGAM, SYK, ITGB2, LILRB2, LYN, and HCK) affected the immune system significantly. Among the genes we validated with RT-PCR, TYROBP, CSF1R, and SYK showed significant increasing expression levels in model HCM rats. In conclusion, we identified two modules and nine hub genes, which were prominently associated with HCM. We found that immune system may play a crucial role in the HCM. Accordingly, those genes and pathways might become therapeutic targets with clinical usefulness in the future.     

DNA methylation-based diagnostic and prognostic biomarkers of nonsmoking lung adenocarcinoma patients

Currently, there are few studies on patients with nonsmoking lung adenocarcinoma, and the pathogenesis is still unclear. The role of DNA methylation in the pathogenesis of cancer is gradually being recognized. The purpose of this study was to determine the abnormal methylation genes and pathways involved in nonsmoking lung adenocarcinoma patients. Gene expression microarray data (GSE10072, GSE43458) and gene methylation microarray data (GSE62948) were downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes were obtained through GEO2R. Next, we analyzed the function and enrichment of the selected genes using Database for Annotation, Visualization, and Integrated Discovery. The protein-protein interaction (PPI) networks were constructed using the Search Tool for the Retrieval of Interacting Genes database and visualized in Cytoscape. Finally, we performed module analysis of the PPI network using Molecular Complex Detection. And we obtained 10 hub genes by Cytoscape Centiscape. We analyzed the independent prognostic value of each hub gene in nonsmoking nonsmall cell lung cancer patients through Kaplan-Meier plotter. Seven hub genes (CXCL12, CDH1, CASP3, CREB1, COL1A1, ERBB2, and ENO2) were closely related to the overall survival time. This study provides an effective bioinformatics basis for further understanding the pathogenesis and prognosis of nonsmoking lung adenocarcinoma patients. Hub genes with prognostic value could be selected as effective biomarkers for timely diagnosis and prognostic of nonsmoking lung adenocarcinoma patients.