使用线性微分方程构建初步的乳腺癌转移相关基因网络模型

随着基因芯片的技术的推广,越来越多的表达数据需要被处理和分析．利用这些表达数据提取基因调控矩阵从而构建基因网络是一个重要的问题．通过线性微分方程模型可以初步构建基因网络,了解网络结构,提取最显著的信息．然而由于分子生物学的条件限制或者数据来源的限制,导致实验数据不充分,使方程组无解．本文使用三次样条方法,对26例临床、病理资料完备的具有淋巴结转移的乳腺癌基因表达数据进行插值处理,使表达数据满秩,从而使用最小二乘法解出加权矩阵,构建初步的表达基因调控网络．通过对构建的基因网络的初步分析表明:乳腺癌转移的形成是由多基因异常引起多条传导通路异常,致使细胞恶性转化的结果,这与生物学上公认的看法是相一致的．因此,利用此线性模型方法对基因表达谱进行分析兵有一定可行性,在认识乳腺癌转移机制,乳腺癌诊断和治疗方面具有一定的理论和应用价值．     

采用系统生物学方法分析乳腺癌转移中Runx2对细胞外基质重塑的调节机制

摘要细胞外基质(extracellular matrix,ECM)重塑是癌细胞迁移的关键步骤.本研究基于乳腺癌组织的基因表达谱数据,采用系统生物学方法推测乳腺癌转移中Runx2对细胞外基质重塑的调节机制.采用相关性分析程序分析49例乳腺原发癌和15例淋巴结转移癌组织的基因表达谱数据,筛选与Runx2呈相关性表达的基因,结果得到与ECM重塑相关的候选基因52个,包括ECM成分11个,ECM降解酶及其抑制剂8个,细胞信号分子33个.利用转录调节因子结合序列数据库搜索候选基因启动子区的Runx2结合模序,筛选其中Runx2转录调控的ECM重塑相关基因,并判断可能调节Runx2的上游信号分子;文献检索实验证实的与Runx2有相互调节关系的基因,并基于Runx2上游调控信号分子和下游转录调节基因的分析,构建得到以Runx2为中心的ECM重塑的生物学调控网络.WNT和TGF/BMPs是启动Runx2表达的主要信号通路,Runx2通过转录调节ECM组分、ECM降解酶及其抑制剂和信号分子调节ECM重塑,促进癌细胞完成转移的生物学过程.     

乳腺癌致病microRNA调控网络的识别与生物信息学分析

目的:构建并解析乳腺癌致病microRNA(miRNA)调控网络,探究其在乳腺癌发生发展中的调控机制。方法:整合TCGA、ENCODE、Fantom等公共数据库资源,得到miRNA、转录因子和基因候选调控关系数据,结合差异表达、变异系数与PCA,构建乳腺癌miRNA调控网络,解析调控网络的度中心性与聚类系数,使用DAVID进行功能富集分析,构建Cox回归模型作生存曲线。结果:共识别miRNA调控网络262个,其中包含5个显著差异表达miRNA,8个转录因子和130个基因。通过功能富集分析发现这些miRNA靶基因显著参与细胞周期、细胞分化、细胞生长、转移等转录后调控的肿瘤生物进程,并与FoxO信号通路、p53信号通路、基因监测通路等信号通路高度相关。通过分析生存曲线发现hsa-mir-144与hsa-mir-133a-2显著与乳腺癌患者生存相关。结论:识别的乳腺癌致病miRNA调控网络中miRNA之间有相互作用,且网络整体功能不仅受hub网络影响,也受元件自身特性影响,这些miRNA靶基因显著富集于肿瘤相关生物学进程与信号通路中。     

肝细胞癌相关差异基因的生物信息学及预后分析

肝细胞癌是全球癌症相关死亡的主要原因,目前对肝细胞癌的发病机制研究尚不完善,探索肝细胞癌发生、发展相关的分子标志物及其预后具有重要意义。从GEO数据库获得肝细胞癌组织和非癌组织的基因表达阵列数据GSE84402,利用GEO2R筛选差异表达基因;采用DAVID数据库对差异基因进行GO富集分析和KEGG通路分析;通过STRING数据库和Cytoscape软件构建差异表达基因对应的蛋白质相互作用网络,并从网络中筛选出核心基因(hub genes);结合KM plotter数据库的临床信息对hub genes进行预后分析。结果显示:共得到1 307个差异表达基因,其中上调基因741个,下调基因566个,这些差异表达基因主要涉及细胞分裂、细胞周期、DNA复制及物质代谢等生物学过程及生物通路。通过GO、KEGG及蛋白质相互作用网络筛选出BUB1、BUB1B、CCNA2、CCNB1、CCNB2、CDC20、CDK1、MAD2L1、PLK1等9个hub genes,进一步分析发现hub genes均与细胞周期的调控相关,表明细胞周期的调控失常在肝细胞癌的发生、发展过程中具有重要作用。生存分析显示9个hub genes在肝细胞癌患者中均为表达上调的基因,且与患者预后不良相关,这为寻找肝细胞癌患者预后相关生物标志物的研究提供了线索。     

细胞凋亡和代谢基因在转移倾向结肠癌中的研究

目的:应用基因微阵列技术初步筛选与不同转移倾向结肠癌相关的细胞凋亡和代谢相关基因,研究转移相关基因功能.方法:取结肠癌肝转移和无转移结肠癌组织,采用人全基因组表达谱芯片获得两组织的基因表达谱,分析比较两者之间细胞凋亡和代谢基因的差异表达情况;利用基因数据库检索结肠癌相关基因,分析基因功能.结果:应用含有16450个克隆(其中3869个未知)的cDNA微阵列分析发现,细胞凋亡或肿瘤相关基因中,2倍以上(Ratio值小于0.5或大于2.0)差异基因共216个,上调基因85个,下调基因129个.表达差异5倍以上(Ratio值小于0.2或大于5.0)共32个,上调基因10个,下调基因22个.在细胞代谢相关基因中,2倍以上(Ratio值小于0.5或大于2.O)差异基因共205个,上调基因86个,下调基因119个.表达差异5倍以上(Ratio值小于0.2或大于5.0)共15个,上调基因10个,下调基因5个.利用基因数据库检索分析发现5个基因与结肠癌转移关系密切.结论:结肠癌的发生和转移是多基因参与的,本实验应用基因微阵列技术发现细胞凋亡和代谢相关基因中发现5个基因与结肠癌转移关系密切.     

凋亡抑制基因livin与survivin在乳腺癌中的表达差异

目的探讨凋亡抑制基因livin在乳腺癌发生、发展中的作用及其与survivin基因的表达和乳腺癌生物学行为之间的关系。方法采用逆转录聚合酶链反应(RT-PCR)检测44例乳腺癌组织、40例癌旁正常组织及4个乳腺癌细胞系中livinmRNA和survivin mRNA的表达,并用免疫组化(IHC)EnVision法检测上述组织和细胞中livin和survivin蛋白的表达。结果livin mRNA和survivin mRNA在乳腺癌组织中的阳性表达率分别为72.7%(32/44)和61.4%(27/44),在癌旁正常组织中的阳性率分别为7.50%(3/40)和5.00%(2/40),二者在癌组织中的表达均显著高于在正常组织中的表达(P<0.01)。livin和survivin蛋白表达情况与mRNA结果相似(P<0.01)。livin和survivin在乳腺癌组织中的表达无显著相关性(P>0.05)。4个乳腺癌细胞系中均有survivin mRNA和蛋白的表达,而MCF-7及MDA-MB-435细胞系中呈阴性表达。survivin基因在伴有淋巴结转移的乳腺癌组织中的表达明显高于无淋巴结转移的乳腺癌组织(P=0.0047),livin在雌激素受体(ER)阴性或者Her2/neu阳性表达的乳腺癌中的阳性率有升高的趋势,但并无显著性差异(P>0.05)。结论livin和survivin基因在人乳腺癌组织中表达上调,提示其可能在乳腺癌发生、发展中起重要促进作用,sur-vivin和淋巴结转移的密切关系表明它的高表达可能反映患者较差的预后。livin和survivin基因一样可能成为乳腺癌治疗中的一个靶基因。     

基于基因表达谱分析的乙肝阳性转移性肝细胞癌的分子机制分析

本研究旨在筛选与乙肝阳性转移性肝细胞癌相关的基因并揭示其潜在的分子机制。利用GEO数据库中GSE364数据集,筛选在肝内扩散转移组和门静脉癌栓转移组都差异表达的基因,DAVID对差异表达基因进行GO与信号通路富集分析,并用STRING和Cytoscape构建蛋白互作网络,随后用mi Rwalk 2.0筛选可能参与肝细胞癌转移的miRNAs,构建miRNA-枢纽基因调控网络。之后使用Smoami R DB 2.0和c Bio Portal分析枢纽基因突变与circRNA和肝细胞癌预后的关系。我们获得在肝内扩散转移组和门静脉癌栓转移组都差异表达的基因701个,富集分析发现这些基因主要涉及血管生成和血管内皮生长因子信号转导等信号通路。从构建的蛋白互作网络中获得参与蛋白互作模式1的15个枢纽基因,GO分析发现其主要参与RNA加工、代谢、剪接等生物过程。构建的miRNA-枢纽基因调控网络中有4个miRNA参与两个枢纽基因的调控,此外肝细胞癌中SRSF1基因有突变并可转录为hsa_circ_0044757,SNRNP200基因突变与患者预后相关。本研究发现的差异表达基因和枢纽基因,有助于我们认识乙肝相关性肝细胞癌转移的分子机制,并可作为新的用于诊断和预后判断的分子标志物。     

乳腺癌转移相关基因的筛选与生物信息学分析

乳腺癌是女性最常见的恶性肿瘤,转移与复发是乳腺癌患者死亡的主要原因. 研究与乳腺癌细胞转移相关的分子靶点对预防乳腺癌术后复发、提高疗效有重要意义. 本研究以3组乳腺癌转移相关的基因表达谱数据(GSE2034, GSE2603, GSE12276)为分析材料,采用GeneSpring软件筛选乳腺癌原发瘤与转移瘤芯片数据的差异表达基因,结合生物信息学工具PATHER、STRING、pSTIING和文献挖掘工具iHOP对差异基因及其相互作用关系进行分析. 结果显示,共筛选出乳腺癌转移共同差异基因147个,其中表达上调93个,表达下调54个. 这些差异基因主要涉及细胞周期与增殖、细胞粘附、细胞迁移、血管形成及信号转导等生物通路和生物过程. 差异基因编码蛋白间的相互作用主要集中在14个蛋白,且在更为复杂的网络图谱中仍可见其中9个基因（CXCR4、MMP1、MMP2、MMP3、CTGF、COL1A1、MEF2C、PTGS2及SPARC）在重要的节点位置. 文献挖掘发现,COL1A1基因可能为新发现的乳腺癌转移候选基因,为乳腺癌转移的发病机制提供新的思路,也为转移性乳腺癌的分子诊断和个体化治疗奠定基础.     

乳腺癌组织中VEGF—C和p38MAPK的表达及与伴淋巴结转移的关系

目的探讨乳腺浸润性导管癌组织中血管内皮生长因子C（VEGF—C）和丝裂原激活蛋白激酶p38（p38MAPK）的表达关系,以及与乳腺浸润性导管癌淋巴结转移等生物学行为的关系。方法采用免疫组织化学sP法检测70例乳腺浸润性导管癌组织及15例癌旁正常组织中VEGF-C和p38MAPK蛋白的表达,并采用Westernblot法检测13例伴有淋巴结转移的乳腺癌及12例无淋巴结转移的乳腺癌的新鲜组织中VEGF—C和p38MAPK蛋白表达。结果VEGF—C和p38MAPK在乳腺浸润性导管癌组织中的表达（阳性率分别为67．0％和61．4％）明显高于癌旁正常组织;VEGF-C和p38MAPK蛋白在伴有淋巴结转移组的乳腺癌组织中的表达均高于无淋巴结转移组（P=0．005,P=0．005）;在乳腺浸润性导管癌组织中VEGF-C和p38MAPK表达存在显著正相关（r=0．383,P=0．001）,并与乳腺浸润性导管癌的TNM分期（P=0．019,P=0．010）有关;VEGF-C和p38MAPK蛋白表达与乳腺浸润性导管癌肿块的大小（P=0．203,P=0．086）和患者的年龄（P=0．0．266,P=0．087）无明显关系。Western blot也证实,VEGF-C和p38MAPK蛋白在有淋巴结转移组中表达高于无淋巴结转移组。结论VEGF-C和p38MAPK的蛋白表达与乳腺浸润性导管癌的淋巴结转移密切相关,其有望成为乳腺癌治疗的新靶点。     

龋病感染牙髓相关基因的筛选与生物信息学分析

目的:综合应用生物信息学技术,从分子水平对龋坏牙髓与正常牙髓的差异基因进行筛选分析,初步探讨其作用机制。方法:从GEO基因表达数据库中下载龋坏牙髓相关芯片数据集,采用MORPHEUS在线筛选差异表达基因,结合DAIVID、STRING等在线分析工具对差异表达基因进行GO功能富集分析及KEGG通路分析,后用Cytoscape软件构建蛋白质相互作用网络。结果:共筛选出375个差异表达基因,其中表达上调253个、下调122个,主要涉及免疫应答、炎症反应、细胞因子应答和生物矿化组织发育等生物过程,以及抗原加工提呈和NF-κB信号等生物通路。通过蛋白质互作网络构建分析发现,MMP9、IL-8、PTPRC、CXCR4等10个基因处于核心节点位置。结论:借助生物信息学方法能得到可靠的相关差异基因信息,能够有效指导进一步的研究。得到的差异基因可以作为龋病诊断的指示因子和机制研究的候选基因。     

三阴性乳腺癌相关miRNA筛选及其靶基因的生物信息学分析

应用生物信息学方法筛选并分析三阴性乳腺癌（triple-negative breast cancer,TNBC）相关miRNA及其靶基因,为TNBC的研究提供潜在的分子靶点。采用GEO2R分析TNBC相关miRNA芯片数据集,筛选差异表达倍数最大的5个上调和5个下调miRNA。miRWalk、TargetScan和miRDB预测靶基因并进行Veen分析取交集。利用DAVID对靶基因进行GO富集分析和KEGG通路分析。利用STRING数据库构建蛋白互作网络,并结合Cytoscape构建miRNA-靶基因调控网络,从而筛选出关键的miRNA及其关键靶基因。利用GEPIA2数据库对靶基因进行生存分析。GEO2R筛选出486个差异miRNA,上调和下调的miRNA分别有298个和188个。对差异倍数最大的5个上调和5个下调miRNA的靶基因进行富集分析显示,靶基因主要参与ErbB信号通路、癌症中转录调控紊乱和cGMP-PKG信号通路等。miRNA-靶基因调控网络显示,表达上调的关键miRNA为miR-611,其关键靶基因为CDC27、UBE2D2、UBR1、SPSB1、HERC2和RLIM;表达下调的关键miRNA为miR-1205,其关键靶基因为WSB1、FBXL8、UBE2W、PTPN11、ARF6、DNAJC6和COPS2。生存分析表明,UBR1（P=0.007 2）和PTPN11（P=0.029）表达上调可显著降低TNBC患者的整体生存率。经筛选获得的关键miRNA及其关键靶基因可作为潜在分子标记物用于TNBC的早期诊断、治疗靶点选择和预后判断,并为后续的研究提供参考依据。     

Allelic variation and differential expression of the mSIN3A histone deacetylase complex gene Arid4b promote mammary tumor growth and metastasis

Accumulating evidence suggests that breast cancer metastatic progression is modified by germline polymorphism, although specific modifier genes have remained largely undefined. In the current study, we employ the MMTV-PyMT transgenic mouse model and the AKXD panel of recombinant inbred mice to identify AT-rich interactive domain 4B (Arid4b; NM_194262) as a breast cancer progression modifier gene. Ectopic expression of Arid4b promoted primary tumor growth in vivo as well as increased migration and invasion in vitro, and the phenotype was associated with polymorphisms identified between the AKR/J and DBA/2J alleles as predicted by our genetic analyses. Stable shRNA-mediated knockdown of Arid4b caused a significant reduction in pulmonary metastases, validating a role for Arid4b as a metastasis modifier gene. ARID4B physically interacts with the breast cancer metastasis suppressor BRMS1, and we detected differential binding of the Arid4b alleles to histone deacetylase complex members mSIN3A and mSDS3, suggesting that the mechanism of Arid4b action likely involves interactions with chromatin modifying complexes. Downregulation of the conserved Tpx2 gene network, which is comprised of many factors regulating cell cycle and mitotic spindle biology, was observed concomitant with loss of metastatic efficiency in Arid4b knockdown cells. Consistent with our genetic analysis and in vivo experiments in our mouse model system, ARID4B expression was also an independent predictor of distant metastasis-free survival in breast cancer patients with ER+ tumors. These studies support a causative role of ARID4B in metastatic progression of breast cancer.     

Significance of PELP1 in ER-negative breast cancer metastasis

Breast cancer metastasis is a major clinical problem. The molecular basis of breast cancer progression to metastasis remains poorly understood. PELP1 is an estrogen receptor (ER) coregulator that has been implicated as a proto-oncogene whose expression is deregulated in metastatic breast tumors and whose expression is retained in ER-negative tumors. We examined the mechanism and significance of PELP1-mediated signaling in ER-negative breast cancer progression using two ER-negative model cells (MDA-MB-231 and 4T1 cells) that stably express PELP1-shRNA. These model cells had reduced PELP1 expression (75% of endogenous levels) and exhibited less propensity to proliferate in growth assays in vitro. PELP1 downregulation substantially affected migration of ER-negative cells in Boyden chamber and invasion assays. Using mechanistic studies, we found that PELP1 modulated expression of several genes involved in the epithelial mesenchymal transition (EMT), including MMPs, SNAIL, TWIST, and ZEB. In addition, PELP1 knockdown reduced the in vivo metastatic potential of ER-negative breast cancer cells and significantly reduced lung metastatic nodules in a xenograft assay. These results implicate PELP1 as having a role in ER-negative breast cancer metastasis, reveal novel mechanism of coregulator regulation of metastasis via promoting cell motility/EMT by modulating expression of genes, and suggest PELP1 may be a potential therapeutic target for metastatic ER-negative breast cancer.     

Identification of a novel aspartic-like protease differentially expressed in human breast cancer cell lines

Four different human breast cancer cell lines were examined to search for genes associated with tumor growth and metastasis. Each of these cell lines, MDA-MB-453, MCF-7, MDA-MB-231 and MDA-MB-435, displays different phenotypic characteristics ranging from poorly to highly tumorigenic and metastatic. The differences in gene expression profiles of these cell lines generated by differential display technique should allow one to identify candidates as putative oncogenes or tumor/metastasis suppressor genes. A novel cDNA expressed in the highly tumorigenic and metastatic cell line, MDA-MB-435, was identified and isolated by this approach. The function for this gene, designated ALP56 (aspartic-like protease 56 kDa), in tumor progression is suggested by the homology of the encoded protein to aspartic proteases, such as cathepsin D. The amino acid residues in two catalytic domains of this family are highly conserved in those domains of ALP56. Northern hybridization indicated that the expression of ALP56 is associated with growth and metastasis of MDA-MB-435 tumors in immunodeficient mice. In situ hybridization of biopsies from breast cancer and colon cancer patients indicated that ALP56 is upregulated in human primary tumors and liver metastasis. These results suggest that this novel gene correlates with human tumor progression.     

Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

Background

One of the major goals in gene and protein expression profiling of cancer is to identify biomarkers and build classification models for prediction of disease prognosis or treatment response. Many traditional statistical methods, based on microarray gene expression data alone and individual genes' discriminatory power, often fail to identify biologically meaningful biomarkers thus resulting in poor prediction performance across data sets. Nonetheless, the variables in multivariable classifiers should synergistically interact to produce more effective classifiers than individual biomarkers.

Results

We developed an integrated approach, namely network-constrained support vector machine (netSVM), for cancer biomarker identification with an improved prediction performance. The netSVM approach is specifically designed for network biomarker identification by integrating gene expression data and protein-protein interaction data. We first evaluated the effectiveness of netSVM using simulation studies, demonstrating its improved performance over state-of-the-art network-based methods and gene-based methods for network biomarker identification. We then applied the netSVM approach to two breast cancer data sets to identify prognostic signatures for prediction of breast cancer metastasis. The experimental results show that: (1) network biomarkers identified by netSVM are highly enriched in biological pathways associated with cancer progression; (2) prediction performance is much improved when tested across different data sets. Specifically, many genes related to apoptosis, cell cycle, and cell proliferation, which are hallmark signatures of breast cancer metastasis, were identified by the netSVM approach. More importantly, several novel hub genes, biologically important with many interactions in PPI network but often showing little change in expression as compared with their downstream genes, were also identified as network biomarkers; the genes were enriched in signaling pathways such as TGF-beta signaling pathway, MAPK signaling pathway, and JAK-STAT signaling pathway. These signaling pathways may provide new insight to the underlying mechanism of breast cancer metastasis.

Conclusions

We have developed a network-based approach for cancer biomarker identification, netSVM, resulting in an improved prediction performance with network biomarkers. We have applied the netSVM approach to breast cancer gene expression data to predict metastasis in patients. Network biomarkers identified by netSVM reveal potential signaling pathways associated with breast cancer metastasis, and help improve the prediction performance across independent data sets.