Peroxiredoxin Ⅱ在退变椎间盘髓核中的表达及临床意义

目的:检测Pcroximdoxin Ⅱ在腰椎间盘髓核组织中的表达,分析其在椎间盘退变中的临床意义.方法:用蛋白免疫印迹(Western blot)的方法检测Peroxiredoxin Ⅱ在正常、突出及脱出腰椎间盘髓核中的表达情况.结果:Peroxiredoxin Ⅱ在退变椎间盘髓核中表达丰富,而在正常椎问盘髓核中表达微弱,两者比较差异显著(P<0.05),在突出和脱出椎间盘髓核中表达无显著性差异(P>0.05).结论:Peroxiredoxin Ⅱ在正常及退变腰椎间盘髓核组织中差异表达.     

Bcl2、Fas在人颈椎间盘软骨终板、纤维环及髓核中的表达及意义

目的 检测正常和突出的人颈椎间盘软骨终板、纤维环及髓核中Bcl2、Fas的表达情况,探讨其在各组中不同表达的意义。方法 先将各正常及突出颈椎间盘在解剖显微镜下分出软骨终板、纤维环、髓核并进行苏木精-伊红（HE）染色行形态学观察,再用免疫组化（SABC法）检测Bcl2、Fas在各组中的表达情况,并进行计算机显像系统图像分析。结果 Bcl2的表达在正常颈椎间盘的软骨终板、纤维环、髓核中均高于突出颈椎间盘的各相应组;Fas的表达在正常颈椎间盘的软骨终板、纤维环、髓核中均低于突出颈椎间盘的各相应组;在异常颈椎间盘的软骨终板中Bcl2的表达低于其在纤维环和髓核中的表达（P〈0.01）;Fas的表达高于其在纤维环和髓核中的表达（P〈0.01）;Bcl2及Fas的表达在纤维环和髓核中的差别无统计学差异（P〉0.05）。结论 细胞凋亡是椎间盘退变的重要原因,且凋亡可能始于软骨终板。     

Peroxiredoxin Ⅱ在退变椎间盘髓核中的表达及其临床意义

目的:检测PeroxiredoxinⅡ在腰椎间盘髓核组织中的表达,分析其在椎间盘退变中的临床意义。方法:用蛋白免疫印迹(Western blot)的方法检测PeroxiredoxinⅡ在正常、突出及脱出腰椎间盘髓核中的表达情况。结果:PeroxiredoxinⅡ在退变椎间盘髓核中表达丰富,而在正常椎间盘髓核中表达微弱,两者比较差异显著(P<0.05),在突出和脱出椎间盘髓核中表达无显著性差异(P>0.05)。结论:PeroxiredoxinⅡ在正常及退变腰椎间盘髓核组织中差异表达。     

PeroxiredoxinⅡ在退变椎间盘髓核中的表达及其临床意义

目的：检测PeroxiredoxinⅡ在腰椎间盘髓核组织中的表达,分析其在椎间盘退变中的临床意义。方法：用蛋白免疫印迹（Western blot）的方法检测PeroxiredoxinⅡ在正常、突出及脱出腰椎间盘髓核中的表达情况。结果：PeroxiredoxinⅡ在退变椎间盘髓核中表达丰富,而在正常椎间盘髓核中表达微弱,两者比较差异显著（P〈0.05）,在突出和脱出椎间盘髓核中表达无显著性差异（P〉0.05）。结论：PeroxiredoxinⅡ在正常及退变腰椎间盘髓核组织中差异表达。     

缺氧诱导因子-1α和血管内皮生长因子在突出腰椎间盘中的表达及意义

目的探讨缺氧诱导因子-1α（hypoxia—induciblefactor-1α,HIF-1α）和血管内皮生长因子（vascular endothelial growth factor,VEGF）在突出腰椎间盘组织中的表达及意义。方法采用链霉亲和素-过氧化物酶复合物（SABC）免疫组化方法,测定40例腰椎间盘突出症患者椎间盘组织中HIF-1α和VEGF的表达情况。结果退变椎间盘组织中HIF-1α和VEGF呈高表达,HIF-1α和VEGF在髓核的表达显著高于纤维环;纤维环破裂型显著高于纤维环完整型;各组中HIF-1α和VEGF的表达均高度相关。结论HIF-1α和VEGF共同参与了椎间盘退变;HIF-1α可能通过上调VEGF的表达来促进椎间盘组织中新生血管的形成,进而延缓椎间盘退变的发生。     

Sox9治疗兔椎间盘退变的效果及机制研究

目的:探究Sox9用于治疗椎间盘退变的效果及调控机制。方法:将Ad-sox9和Ad-GFP各20μL分别转染至椎间盘退变兔的髓核组织中,转染后3、7、30、60天取材,采用免疫组化、免疫荧光和MRI等研究方法检测椎间盘髓核组织中II型胶原、蛋白多糖的表达情况,并分析对椎间盘退变的改善情况。结果:免疫组化染色显示sox9组中椎间盘髓核组织中II型胶原、蛋白多糖的表达明显升高,MRI显示sox9组椎间盘T2像信号有明显改善(P<0.05)。结论:体内转染腺病毒介导的sox9基因能够增加椎间盘内II型胶原和蛋白多糖的表达,并抑制椎间盘的退变进程。     

人椎间盘髓核来源的诱导多能干细胞重编程及功能的研究

椎间盘退变始发于髓核组织,获得足够有功能的髓核细胞是研究及治疗椎间盘退变的关键.而人诱导多能干细胞(induced pluripotent stem cell,iPSC)不仅为建立疾病模型以研究疾病发生发展机制开辟了道路,还在再生医学领域展现出了广阔的应用前景.我们首先从椎间盘退变患者微创手术获得的髓核组织内分离髓核细胞,将携带OCT3/4、SOX2、KLF4和c-MYC的仙台病毒(Sendai virus,Se V)转染髓核细胞,重编程获得iPSC.通过检测多能细胞特异性标志、体内成瘤实验、甲基化及核型分析对所获得的iPSC进行鉴定.并以皮肤成纤维细胞来源iPSC作为对照,在二维和三维水凝胶中对iPSC进行定向分化,检测髓核细胞相关蛋白和基因的表达,比较分析2种iPSC向髓核细胞的分化效率.结果显示,iPSC能表达多能细胞特异性标志,具有正常的二倍体核型,畸胎瘤实验显示三个胚层的出现.诱导分化后的iPSC表达髓核相关基因和蛋白,在水凝胶中诱导培养后,iPSC表达更多的髓核相关基因和蛋白.髓核来源的iPSC与成纤维细胞来源的iPSC相比,可表达更多的髓核相关基因和蛋白.本研究首次将患者退变髓核细胞重编程成iPSC,并在水凝胶内将其诱导分化为髓核样细胞,为椎间盘退变个体化细胞治疗奠定基础.     

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...     

不同月龄大鼠椎间盘退变与多效生长因子表达的关系

目的观察不同月龄大鼠椎间盘的形态学变化并检测椎间盘中多效生长因子（pleiotrophin,PTN）的表达,探讨PTN与椎间盘退变的关系。方法取Wistar大鼠50只,以1,3,6,12,18个月龄不同分为5组,每组10只。采用苏木精-伊红染色观察椎间盘的形态学变化。采用SABC免疫组织化学方法,检测椎间盘中PTN的表达情况;结果（1）随着月龄的增加,椎间盘组织结构紊乱的程度逐渐增加,髓核内基质降解、正中出现空腔,胶原纤维增生、粗大、排列紊乱、并可见纤维断裂或缺失。（2）随着大鼠月龄的增加（1-12月龄）,椎间盘细胞中PTN的表达有逐渐减低的趋势,但至18月龄,PTN表达又有所增加;6和12月龄组椎间盘细胞中PTN的表达显著低于1月龄组,而18月龄组PTN的表达显著高于12月龄组。同月龄组椎间盘细胞中,PTN在终板的表达高于髓核和纤维环,髓核和纤维环中PTN的表达未见明显差异。结论大鼠椎间盘结构随月龄增加发生退行性变,PTN参与了大鼠椎间盘的退变,并可能通过促进椎间盘组织中新生血管的形成,延缓椎间盘的退变。     

几种椎间盘退变动物模型的建立和应用

椎间盘退变是一种年龄相关的退行性疾病,是引起下腰痛的主要因素,严重影响病人的生活质量,并显著增加家庭的经济负担。目前,缺少椎间盘退变的有效干预和治疗手段,部分原因是其发病机制尚未阐明。椎间盘退变动物模型的构建对于阐明该疾病的病理机制至关重要。椎间盘退变是一个复杂的过程,受机械应力、结构损伤、生物化学与基因表达等多种因素的影响。本文总结了应用异常机械应力、结构损伤、生物化学或化学诱导和基因敲除等方式构建的椎间盘退变动物模型。生物力学是维持椎间盘稳态的重要因素,异常的机械应力会导致椎间盘退变。同时,椎间盘退变常伴随结构性损伤,椎间盘结构破坏也会导致椎间盘发生退变。此外,生物化学或化学诱导和关键基因敲除也会导致椎间盘退变。本文按照造成异常机械应力的因素将机械应力模型分为加压模型和失稳模型;按照椎间盘结构将结构损伤模型分为髓核与纤维环损伤模型和软骨终板损伤模型。总结了生物化学或化学诱导模型以及新型的基因敲除模型。讨论了不同类型椎间盘退变动物模型的可能应用和局限性。     

Hub genes identification and association of key pathways with hypoxia in cancer cells: A bioinformatics analysis

Three human cancer cell lines (A549, HCT116, and HeLa) were used to investigate the molecular mechanisms and potential prognostic biomarkers associated with hypoxia. We obtained gene expression data from Gene Expression Omnibus (GEO) datasets GSE11704, GSE147384, and GSE38061, which included 5 hypoxic and 8 control samples. Using the GEO2R tool and Venn diagram software, we identified common differentially expressed genes (cDEGs). The cDEGs were then subjected to Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis by employing DAVID. The hub genes were identified from critical PPI subnetworks through CytoHuba plugin and these genes' prognostic significance and expression were verified using Kaplan-Meier analysis and Gene Expression Profiling Interactive Analysis (GEPIA), respectively. The research showed 676 common DEGs (cDEGs), with 207 upregulated and 469 downregulated genes. The STRING analysis showed 673 nodes and 1446 edges in the PPI network. We identified 4 significant modules and 19 downregulated hub genes. GO analysis revealed all of them were majorly involved in ribosomal large subunit assembly and biogenesis, rRNA processing, ribosome biogenesis, translation, RNA & protein binding frequently at the sites of nucleolus and nucleoplasm while 11 were significantly associated with a better prognosis of hypoxic tumors. Our research sheds light on the molecular mechanisms that underpin hypoxia in human cancer cell lines and identifies potential prognostic biomarkers for hypoxic tumors.     

急性髓系白血病mRNA与非编码RNA差异表达谱及CeRNA调控网络分析

利用GEO数据库(gene expression omnibus database)通过生物信息学分析方法探讨急性髓系白血病(acute myelogenous leukemia,AML)的发病机制。检索GEO数据库中AML相关芯片数据集GSE142698、GSE142699和GSE96535。利用GEO2R分析得到差异mRNAs、miRNAs以及差异lncRNAs。利用在线生物信息学分析工具DAVID对差异mRNAs进行GO富集分析和KEGG通路分析。利用miRWalk数据库预测AML相关miRNAs的靶向mRNAs,利用Spongescan数据库预测AML相关miRNAs的靶向lncRNAs,构建lncRNA-miRNA-mRNA竞争性内源RNA （competing endogenous RNA,ceRNA）调控网络。共筛选出29个显著差异mRNAs、70个显著差异miRNAs和20 005个显著差异lncRNAs。GO富集分析和KEGG通路分析显示,差异表达基因主要涉及蛋白磷酸化、细胞分裂、细胞增殖的负调控、基因表达的正向调节、周期蛋白依赖的丝氨酸/苏氨酸激酶活性的调节等生物过程以及细胞周期、细胞衰老、癌症通路、PI3K-Akt通路等信号通路。将miRWalk数据库预测的靶向mRNAs与差异mRNAs取交集,Spongescan数据库预测的靶向lncRNAs与差异lncRNAs取交集,分别确定了25个mRNAs、6个lncRNAs参与AML相关ceRNA调控网络的构建。结果表明,lncRNAs可能作为关键的ceRNA,通过调控miRNA和相关靶基因参与AML的发生与发展,研究结果为AML诊断和治疗的分子生物学研究提供了新的依据。     

Elevated hsa‐miR‐590‐3p expression down‐regulates HMGB2 expression and contributes to the severity of IgA nephropathy

Peripheral blood mononuclear cells (PBMCs) play important roles in the pathogenesis of IgA nephropathy (IgAN). Our study aimed to provide a deep understanding of IgAN and focused on the dysregulation of hsa‐miR‐590‐3p and its target gene HMGB2 in PBMCs. Three gene expression profile datasets (GSE14795, GSE73953 and GSE25590) were downloaded from the GEO database. The DEGs (differentially expressed genes)‐miRNA network that was associated with IgAN was constructed by Cytoscape, and HMGB2 and hsa‐miR‐590‐3p were selected for further exploration. The dual‐luciferase reporter system was utilized to verify their interaction. Then, the expression levels of HMGB2 and hsa‐miR‐590‐3p in PBMCs were detected by qPCR in another cohort, and the correlation of their expression levels with the clinical pathological manifestations and serum Gd‐IgA1(galactose‐deficient IgA1) levels was also investigated. HMGB2 was identified as the target gene of hsa‐miR‐590‐3p. Furtherly, the elderly patients had higher HMGB2 expression levels than the expression levels of the younger patients. As the serum creatinine, serum BUN levels increased, the expression of HMGB2 decreased; Besides, the HMGB2 expression was positively correlated with serum complement 3(C3) levels, and it also had a negative correlation with the diastolic blood pressure, but not reach statistical significance. What is more, both hsa‐miR‐590‐3p and HMGB2 expression had a slight correlation tendency with serum Gd‐IgA1 levels in the whole population. In conclusion, HMGB2, the target gene of hsa‐miR‐590‐3p, was identified to correlate with the severity of IgAN, and this provides more clues for the pathogenesis of IgAN.     

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。     

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.     

Nucleus pulposus related lncRNA and mRNA expression profiles in intervertebral disc degeneration

In the present study, we aimed to have a comprehensive understanding of nucleus pulposus related long noncoding RNA (lncRNA) and mRNA expression profiles in intervertebral disc degeneration (IDD). In total, 2418 mRNAs and 528 lncRNAs were found to be differentially expressed in the IDD group compared with the Control group. Combining microarray datasets and sequencing data, 5 overlapping DEMs and 7 overlapping DELs were identified. NF-κB signaling pathway, PI3K-Akt signaling pathway and Wnt/β-catenin signaling pathway were strongly linked with enriched GO terms and KEGG pathways. The ceRNA network suggested that lnc-TMEM44-AS1-hsa-miR-206-HDAC4 may be one crucial axis in IDD. PPI network analysis was constructed with 309 nodes and 129 edges. And the highest connectivity degrees were ALB, APOB and CCL2. This study suggested that specific lncRNAs and ceRNA axes may be crucial in the development of IDD. It provides a new perspective for delaying IDD process and enhancing intervertebral disc repair.     

Epigenetic alternations of microRNAs and DNA methylation contribute to gestational diabetes mellitus

This study aimed to identify epigenetic alternations of microRNAs and DNA methylation for gestational diabetes mellitus (GDM) diagnosis and treatment using in silico approach. Data of mRNA and miRNA expression microarray ({"type":"entrez-geo","attrs":{"text":"GSE103552","term_id":"103552"}}GSE103552 and {"type":"entrez-geo","attrs":{"text":"GSE104297","term_id":"104297"}}GSE104297) and DNA methylation data set ({"type":"entrez-geo","attrs":{"text":"GSE106099","term_id":"106099"}}GSE106099) were obtained from the GEO database. Differentially expressed genes (DEGs), differentially expressed miRNAs (DEMs) and differentially methylated genes (DMGs) were obtained by limma package. Functional and enrichment analyses were performed with the DAVID database. The protein‐protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. Simultaneously, a connectivity map (CMap) analysis was performed to screen potential therapeutic agents for GDM. In GDM, 184 low miRNA‐targeting up‐regulated genes and 234 high miRNA‐targeting down‐regulated genes as well as 364 hypomethylation–high‐expressed genes and 541 hypermethylation–low‐expressed genes were obtained. They were mainly enriched in terms of axon guidance, purine metabolism, focal adhesion and proteasome, respectively. In addition, 115 genes (67 up‐regulated and 48 down‐regulated) were regulated by both aberrant alternations of miRNAs and DNA methylation. Ten chemicals were identified as putative therapeutic agents for GDM and four hub genes (IGF1R, ATG7, DICER1 and RANBP2) were found in PPI and may be associated with GDM. Overall, this study identified a series of differentially expressed genes that are associated with epigenetic alternations of miRNA and DNA methylation in GDM. Ten chemicals and four hub genes may be further explored as potential drugs and targets for GDM diagnosis and treatment, respectively.