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,...     

HER-2阴性乳腺癌新靶向基因的生物信息学分析

利用生物信息学方法研究HER-2阴性乳腺癌的潜在靶向基因、信号传导通路及分子机制。从公众数据库(gene expression omnibus,GEO)下载HER-2阴性乳腺癌患者和正常女性上皮细胞基因芯片数据,运用RMA算法进行数据预处理,运用R语言LIMMA的包选出差异表达基因。将差异表达基因上传到DAVID在线网站进行富集分析,运用KEGG信号传导通路进行信号传导通路分析,最后用STRING进行蛋白相互作用网络分析(控制差异表达倍数大于2倍,p值小于0.01),筛出差异表达基因72个(上调基因10个,下调基因62个)。差异基因富集分析结果表明富集程度高的差异基因主要与转录调节、细胞凋亡及细胞外刺激反应等密切相关,信号传导通路分析结果表明差异基因主要与MAPK信号转导通路等密切相关,蛋白共表达网络分析结果表明关键蛋白质为FOS、JUN、KLF6、ATF3、HIST2H2AA4和HIST2H2BE等。HER-2阴性的乳腺癌患者早期差异表达基因表现为下调,HIST2H2AA4和HIST2H2BE可成为其潜在靶向基因,并可作为MAPK信号传导通路中ERK1/2中FOS基因的下游基因,验证需进一步实验。     

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。     

KLF基因家族在乳腺癌中的表达及临床意义

为了研究KLF基因家族成员在乳腺癌发生中的分子机制及其与患者预后的关系,我们利用GEPIA对KLF家族进行Kaplan-Meier生存分析,筛选出与患者预后显著相关家族成员;在Oncomine数据库中分析前面筛选出的与乳腺癌预后有关的KLF家族成员;采用c Bio Portal筛选出与患者预后有关的KLF家族成员共表达基因;STRING构建蛋白互作网络,Cytoscape软件中的MCODE插件识别网络模块,再用STRING对网络模块进行聚类分析。Kaplan-Meier生存分析显示KLF家族成员中KLF13和KLF15与乳腺癌预后显著相关(p0.05);Oncomine分析显示只有KLF13在乳腺癌组织表达显著高于正常组织(p0.01);c Bio Portal中筛选出KLF13共表达基因274个;Cytoscape软件中的MCODE插件识别出网络模块3个,STRING进行GO分析发现,网络模块中基因主要参与线粒体内的翻译和呼吸链氧化磷酸化生成ATP等生物过程,KEGG分析发现网络模块中涉及基因主要线粒体蛋白质翻译场所核糖体及其氧化磷酸化等信号通路有关。以上表明KLF13可能通过线粒体信号通路来影响乳腺癌的发生,可作为一个候选的乳腺癌临床预后标志物和潜在的治疗靶点。     

NHP2调控肝癌细胞衰老机制的生物信息学分析

为了探讨核糖核蛋白NHP2在肝细胞癌（hepatocellular carcinoma, HCC）中的表达情况,了解其与疾病进展与预后的关系,通过Quick Go、GEPIA 2在线数据库筛选HCC细胞衰老的差异表达基因并确定了研究对象基因NHP2;进一步使用STRING、TIMER 2.0、UALCAN数据库等生物信息学方法分析NHP2在泛癌中的差异表达以及在肝癌和其他泛癌中病理进展过程中的相关性,并预测预后生存关系;使用miRNet数据库分析其靶向miRNAs和lncRNAs,应用Cytoscape v3.8.0绘制可能的CeRNAs调控网络图。结果显示,在线数据库检索到细胞衰老相关生物学过程相关基因113个,HCC差异表达基因2 206个,共有19个差异表达基因参与了肝癌细胞衰老的生物学过程。其中,NHP2在包括HCC的多种癌症中显著高表达,NHP2高表达的肝癌人群预后较差,具有统计学差异。NHP2基因编码的互作蛋白有10个,主要参与了1个信号通路（KEGG信号通路）、6个分子功能（molecular function,MF）、11个细胞组分（cellular component...     

CISH技术在乳腺癌Her-2基因检测中的应用

原癌基因Her-2是人类表皮生长因子受体家族的第二个成员,该家族中的受体均位于细胞膜上,在许多组织中都能发现。乳腺癌居女性癌症发病率之首,研究显示Her-2在20％-30％的乳腺癌中过度表达,与乳腺癌的侵袭转移及预后密切相关。目前随着靶向药物曲妥珠单抗（赫赛汀）的发展,乳腺癌复发率及死亡率大大降低,曲妥珠单抗（赫赛汀）靶向治疗的关键是Her-2基因是否扩增,常用的Her-2基因检测方法是免疫组化（IHC）法和荧光原位杂交（FISH）法,但其结果之间一致性较差。     

乳腺癌中CELF6的表达水平及其与预后的关系

目的:研究CELF6在乳腺癌组织与正常组织中的表达差异以及其在乳腺癌中的预后意义。方法:采用GEPIA分析乳腺癌组织与正常乳腺组织中CELF6的表达差异,免疫组化检测乳腺癌组织CELF6蛋白的表达。KM-plotter在线分析TCGA数据库中CELF6的表达差异与乳腺癌患者生存预后的关系,CCK8实验分析不同CELF6表达水平对乳腺癌细胞生长增殖的影响。结果:GEPIA在线软件分析结果显示乳腺癌CELF6表达较与正常组织显著降低(P0.001)。免疫组化检测结果显示乳腺癌患者的乳腺癌组织CELF6的阳性表达显著低于癌旁的正常组织。KM-plotter在线分析的生存曲线显示CELF6高表达的乳腺癌患者生存预后显著优于低表达者(P0.001)。CCK8实验显示敲低乳腺癌细胞MDA-MB-231中CELF6的表达,细胞增殖速度显著变快,而过表达CELF6的MDA-MB-231细胞的增殖速度减慢。结论:CELF6可能作为潜在的抑癌基因,在乳腺癌组织中低表达,与乳腺癌患者预后不良有关。     

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...     

基于已知疾病基因构建共表达网络识别胃癌进展及预后相关非编码基因

本研究旨在总结整理已有胃癌研究的基础上,进一步挖掘出非编码基因在胃癌的进展及预后中起的关键作用。通过胃癌患者编码及非编码基因的表达数据,结合已知胃癌致病基因,进行编码基因与非编码基因的共表达计算,识别出由miRNA介导的并且与已知胃癌基因互作的lncRNA,挖掘出三者(mRNA-miRNA-lncRNA)相互作用的模块,进而对模块进行筛选,并对疾病相关的显著模块的基因进行生存分析。除已知的胃癌相关基因外,研究也使用了差异表达的胃癌基因,这些基因显著的富集在细胞增殖、细胞粘附、肌肉收缩、血管重塑、细胞分裂、染色体分离等生物过程,这些生物过程都与胃的基础功能及胃癌发生发展密切相关。分值最高的三元组模块内核心基因BGN在胃癌患者中显著高表达,而且和胃癌患者的预后显著相关;同时也发现该模块内的miRNA has-miRNA-153-5p和has-miRNA-5001-5p均为已证实的胃癌相关基因;模块内的mRNA和miRNA的表达异常可能是由于与他们显著相关的lncRNA的表达异常导致的。本研究为胃癌已知致病基因的表达异常研究找到了新突破口,潜在的胃癌相关的非编码基因的发现为胃癌预防与治疗提供了新的靶点,为未来的临床应用提供了依据。     

乳腺癌靶向治疗的分子基础

近年来,乳腺癌靶向治疗的研究取得了显著进展。分子靶向药物通过作用于乳腺癌细胞相关细胞的分子信号传导途径,特异性针对异常环节进行干预,控制细胞基因的表达,从而抑制或杀死肿瘤细胞,以此达到治疗乳腺癌的目的。本文将对乳腺癌靶向治疗的分子基础研究进展做一综述。     

基于多目标评价方法的乳腺癌易感基因排序及其网络表达研究

目的利用已有的研究结果和数据,采用多目标评价方法建立乳腺癌易感基因评价模型,对与已知乳腺癌基因关系密切的其它基因进行分析和排序,并给出结果的网络表达模式。方法通过分析已有的文献,并利用有关的基因数据库和已有文献中的数据,提炼出乳腺癌易感基因的多目标评价体系,构建基于加权和法的乳腺癌易感基因评价模型,并利用Cytoscape软件进行评价结果计算和评价结果的网络模式表达。结果利用多目标模型所得到的评价结果,与已有的研究结果一致。其中,乳腺癌易感基因TopBP1排名第二,已知乳腺癌候选易感基因HMMR排名第六。结论文章提出的多目标评价模型能够准确评价被选基因与乳腺癌易感性之间的关系,所提出的评价方法与相关软件结合使用,将成为癌症易感基因研究方面有效的分析方法和途径。     

Biological and Clinical Significance of MAD2L1 and BUB1, Genes Frequently Appearing in Expression Signatures for Breast Cancer Prognosis

To investigate the biologic relevance and clinical implication of genes involved in multiple gene expression signatures for breast cancer prognosis, we identified 16 published gene expression signatures, and selected two genes, MAD2L1 and BUB1. These genes appeared in 5 signatures and were involved in cell-cycle regulation. We analyzed the expression of these genes in relation to tumor features and disease outcomes. In vitro experiments were also performed in two breast cancer cell lines, MDA-MB-231 and MDA-MB-468, to assess cell proliferation, migration and invasion after knocking down the expression of these genes. High expression of these genes was found to be associated with aggressive tumors and poor disease-free survival of 203 breast cancer patients in our study, and the association with survival was confirmed in an online database consisting of 914 patients. In vitro experiments demonstrated that lowering the expression of these genes by siRNAs reduced tumor cell growth and inhibited cell migration and invasion. Our investigation suggests that MAD2L1 and BUB1 may play important roles in breast cancer progression, and measuring the expression of these genes may assist the prediction of breast cancer prognosis.     

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。     

GroupRank: Rank Candidate Genes in PPI Network by Differentially Expressed Gene Groups

Many cell activities are organized as a network, and genes are clustered into co-expressed groups if they have the same or closely related biological function or they are co-regulated. In this study, based on an assumption that a strong candidate disease gene is more likely close to gene groups in which all members coordinately differentially express than individual genes with differential expression, we developed a novel disease gene prioritization method GroupRank by integrating gene co-expression and differential expression information generated from microarray data as well as PPI network. A candidate gene is ranked high using GroupRank if it is differentially expressed in disease and control or is close to differentially co-expressed groups in PPI network. We tested our method on data sets of lung, kidney, leukemia and breast cancer. The results revealed GroupRank could efficiently prioritize disease genes with significantly improved AUC value in comparison to the previous method with no consideration of co-exprssed gene groups in PPI network. Moreover, the functional analyses of the major contributing gene group in gene prioritization of kidney cancer verified that our algorithm GroupRank not only ranks disease genes efficiently but also could help us identify and understand possible mechanisms in important physiological and pathological processes of disease.     

GOBO: gene expression-based outcome for breast cancer online

Microarray-based gene expression analysis holds promise of improving prognostication and treatment decisions for breast cancer patients. However, the heterogeneity of breast cancer emphasizes the need for validation of prognostic gene signatures in larger sample sets stratified into relevant subgroups. Here, we describe a multifunctional user-friendly online tool, GOBO (http://co.bmc.lu.se/gobo), allowing a range of different analyses to be performed in an 1881-sample breast tumor data set, and a 51-sample breast cancer cell line set, both generated on Affymetrix U133A microarrays. GOBO supports a wide range of applications including: 1) rapid assessment of gene expression levels in subgroups of breast tumors and cell lines, 2) identification of co-expressed genes for creation of potential metagenes, 3) association with outcome for gene expression levels of single genes, sets of genes, or gene signatures in multiple subgroups of the 1881-sample breast cancer data set. The design and implementation of GOBO facilitate easy incorporation of additional query functions and applications, as well as additional data sets irrespective of tumor type and array platform.     

Shelterin complex gene: Prognosis and therapeutic vulnerability in cancer

Telomere encompasses a (TTAGGG)n tandem repeats, and its dysfunction has emerged as the epicenter of driving carcinogenesis by promoting genetic instability. Indeed, they play an essential role in stabilizing chromosomes and therefore protecting them from end-to-end fusion and DNA degradation. Telomere length homeostasis is regulated by several key players including shelterin complex genes, telomerase, and various other regulators. Targeting these regulatory players can be a good approach to combat cancer as telomere length is increasingly correlated with cancer initiation and progression. In this review, we have aimed to describe the telomere length regulator's role in prognostic significance and important drug targets in breast cancer. Moreover, we also assessed alteration in telomeric function by various telomere length regulators and compares this to the regulatory mechanisms that can be associated with clinical biomarkers in cancer. Using publicly available software we summarized mutational and CpG island prediction analysis of the TERT gene breast cancer patient database. Studies have reported that the TERT gene has prognostic significance in breast cancer progression however mechanistic approaches are not defined yet. Interestingly, we reported using the UCSC Xena web-based tool, we confirmed a positive correlation of shelterin complex genes TERF1 and TERF2 in recurrent free survival, indicating the critical role of these genes in breast cancer prognosis. Moreover, the epigenetic landscape of DNA damage repair genes in different breast cancer subtypes also being analyzed using the UCSC Xena database. Together, these datasets provide a comprehensive resource for shelterin complex gene profiles and define epigenetic landscapes of DNA damage repair genes which reveals the key role of shelterin complex genes in breast cancer with the potential to identify novel and actionable targets for treatment.     

家族性高胆固醇血症HDL-miRNAs调节血单核细胞功能机制

本研究通过生物信息学方法分析家族性高胆固醇血症患者外周血单核细胞差异表达基因、HDL载体差异表达miRNA及其生物学功能,研究差异HDL-miRNA与单核细胞差异基因的相关性,探讨HDL-miRNA调控外周血单核细胞功能机制,寻找动脉粥样硬化防治新靶点。运用R语言分析GEO数据库共享平台家族性高胆固醇血症外周血单核细胞基因及HDL-miRNA探针芯片得到差异基因及差异miRNA,利用miRwalk2. 0预测miRNA靶基因,并利用STRING进行蛋白互作分析,构建差异miRNA与差异基因之间的调控网络。运用GO及KEGG分析研究基因功能。利用GEO数据(GSE6054)筛选出834个差异表达基因,利用GEO数据(GSE25108)筛选出HDL上差异miRNA28个。交叉匹配得到由19个差异miRNA和56个差异基因组配对的74对miRNA-靶基因。GO富集分析56个差异基因主要富集于肾上腺素受体信号等分子功能。KEGG分析56个差异基因主要富集于造血谱系通路上。家族性高胆固醇血症差异HDL-miRNA与外周血单核细胞差异mRNA具有相关性,HDL-miRNA有通过调控血单核细胞功能的可能性,可能参与高胆固醇血症导致动脉粥样硬化过程。     

Identification of key biomarkers for thyroid cancer by integrative gene expression profiles

Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein–protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.     

基因的单核苷酸多态性与乳腺癌关系的研究进展

乳腺癌是女性发病率最高的恶性肿瘤,具有家族聚集性,其发生发展是一个复杂的过程,涉及多种不同基因的相互作用和相互调控。随着基因技术和分子生物技术的飞速发展,许多基因被揭示在乳腺癌的发生发展中起关键作用,如BRCA、FGFR、ATM、ZNF365、Pokemon基因等。基于流行病学研究,这些易感基因可被分为高外显率易感基因和低外显率易感基因,其多态性对乳腺癌具有重要的影响。对乳腺癌易感基因的研究有助于阐明乳腺癌的发生机制、发展过程,对其早期诊断、预后判断和良恶性鉴别也有着重要的意义,可为乳腺癌的临床生物基因靶向治疗提供新的靶点和理论基础。本文主要对这些基因的单核苷酸多态性与乳腺癌关系的研究进展进行了综述。