排序方式: 共有30条查询结果,搜索用时 15 毫秒
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人胃癌组织中APC基因杂合缺失的分析北京医科大学临床肿瘤学院崔建涛,吕有勇目前已证明:APC基因的突变、缺失除与结肠癌有关外,与其它一些肿瘤,如肺癌、食管癌、胰腺癌也有关。国外有报道在胃癌中发现有APC基因缺失。本实验室在对我国建立的几株胃癌细胞系的... 相似文献
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目的 :探讨癌基因HER2、mdm 2和C myc扩增与胃癌恶性程度、转移及预后的关系。 方法 :用差异竞争性多聚酶链反应检测胃癌原发灶、癌旁、转移淋巴结及远处脏器转移癌中HER2、mdm 2和C myc的基因变异。结果 :HER2扩增频率在近端胃癌组中明显高于远端胃癌组 (分别是 11/ 13和 5 / 19,P <0 .0 0 5 ) ,侵及浆膜组明显高于未侵及浆膜组 (12 / 18和 4/ 14,P <0 .0 5 )。mdm 2的扩增频率在转移淋巴结中高于胃原发癌 (12 / 2 1和 12 / 32 ,P>0 .0 5 ) ,在 3例淋巴结微转移灶中发现mdm 2扩增。C myc在胃癌原发灶及转移淋巴结中的扩增频率分别是6 / 32和 5 / 2 1,2例远处脏器转移癌中有扩增。结论 :HER2、mdm 2和C myc基因扩增与胃癌的快速生长有关 ,三者联合检测可能为判断胃癌恶性程度、转移及预后提供有价值的信息。 相似文献
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[目的]了解李沧区0~5岁儿童血铅水平。[方法]对2007年5月至2008年6月在青岛市李沧区妇幼保健所查体的1178名0~5岁儿童血铅检测结果进行分析。[结果]检测1178名儿童,血铅为7~211μg/L,平均47.08μg/L,≥100μg/L者78例,铅中毒检出率为6.62%。血铅均值(μg/L),男童为48.48,女童为45.42(P〈0.01);入园儿童为50.53,散居儿童为43.09(P〈0.01);郊区、城区、工业区儿童分别为41.54、43.36、54.64(P〈0.01);不同年龄儿童的差异有统计学意义(P〈0.05)。铅中毒检出率,男童为8.16%,女童为4.81%(P〈0.05);入园组为8.72%,散居组为3.31%(P〈0.05);郊区、城区、工业区儿童分别为3.49%、5.97%、10.26%(P〈0.01);不同年龄的差异有统计学意义(P〈0.05)。[结论]李沧区0~5岁儿童血铅水平和铅中毒率处于青岛市一般水平,随着年龄的增长而增高。 相似文献
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目的 探讨CLOCK基因单核苷酸多态性(SNP)与子宫内膜异位症相关性卵巢癌(EAOC)患者化疗敏感性的关系。方法 选择2014年3月至2015年9月在我院接受治疗的EAOC患者129例,年龄38~67岁,平均年龄(46.32±8.96)岁。对患者进行铂类药物为基础的联合化疗,根据患者化疗敏感情况将129例患者分为敏感组(n=71)和不敏感组(n=58),对比两组患者的一般资料和病理特征,采用PCR检测CLOCK基因SNP位点的基因分型及分布情况,探讨不同基因型与化疗效果的相关性,采用Hardy-Weinberg遗传平衡吻合度检验法计算各基因型的理论值。通过Logistic回归分析EAOC患者化疗敏感性的危险因素,分析不同病理特征及基因型对EAOC患者预后影响。结果 rs3749474位点等位基因C/T,分型CC、TT、CT;rs3817444位点等位基因C/A,分型为CC、AA、CA;rs12504300位点等位基因C/G,分型为CC、GG、CG;rs3805148位点等位基因C/A,分型为CC、AA、CA。与敏感组患者相比,不敏感组患者的FIGO分期多为晚期,分化程度主要为未分化-... 相似文献
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RasgenefamilyconsistsofH--ras,KrasandN--ras,codingP21proteinwithGTPaseactivation.Pointmutationsatcodons12,13and61ofrasgenesresultinequilibriumshiftofrasproteinstowardtheactivatedstate,whichconstitutivelyactivatesthemitogenicsignaltransductionpathway['].Frequencyofrasgenemutationingastriccanceriscontroversialandlittlewasknowninprecancerouslesions.Inthisarticle,PCR/RFLP,PCR/SSCPandDNAsequencingwereusedtoinvestigaterasgenemutationrateanditspathogeneticroleingastriccancerandprecancerousl… 相似文献
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目的 通过胃癌差异基因表达谱提供的信息鉴定用于识别胃癌分化程度的分子标志物.方法 从本实验室前期所建立的胃癌Oligo基因芯片数据库中选取15份胃癌全基因组表达谱芯片数据,包括9份低分化胃癌和6份高分化胃癌标本数据,采用生物信息学分析方法BAGEL和k-TSP筛选配对的分类器,用于识别肿瘤的分化程度.随后采用ROC曲线对所筛选的特征分子标志物的分类敏感度和特异度进行判断,鉴定具有最强分类能力的分类器.选取北京肿瘤医院30份胃癌组织标本,包括22份低分化胃癌和8份高分化胃癌标本,采用实时荧光定量PCR对所鉴定的分类器进行验证.结果 利用胃癌分化差异基因表达谱数据,采用BAGEL分析方法筛选出了121个表达变化大于2倍的差异表达基因(FC>2.0,P<0.001),并在此基础上进一步采用k-TSP分析方法获得了3组用于区分胃癌高低分化程度的胃癌特征基因,包括MYLIP和TMPRSS3,ZNF266和TM4SF1以及SNAI2和CNFN.ROC曲线结果显示,SNAI2和CNFN组合基因对胃癌标本的分化程度进行判断具有最高的分类敏感度(100%)和特异度(100%),其AUC达到1,其他两组分类器则分别为0.981和0.963.实时荧光定量PCR结果显示,在22份低分化胃癌标本中,18份标本(82%)的SNAI2的表达水平高于CNFN;在8份高分化胃癌标本中,6份标本(75%)的SNAI2的表达水平低于CNFN.结论 SNAI2和CNFN在不同分化程度胃癌中具有特定的表达模式,并且两者的表达水平呈现负相关趋势,提示SNAI2和CNFN组合可能作为判断胃癌分化程度的分子标志物.Abstract: Objective To identify biomarkers associated with the differentiated phenotype based on gene expression profiling of gastric cancer. Methods Two bioinformatic methods, BAGEL and k-TSP, were used to identify featured genes associated with differentiation in gastric cancer samples based on the Oligo gene chip data, and ROC curves were used to verify the classification sensitivity and specificity of the identified genes. Finally, a total of 30 gastric cancer samples with different differentiation levels were collected for laboratory validation using real-time PCR analyses. Results A total of 121 differentially expressed genes were identified using the BAGEL algorithm, the criterion were FC > 2. 0 and P < 0. 001.Then, the k-TSP algorithm for feature selection based on this differential expression data were used, and 3 groups of featured genes which had potential to classify poor and well differentiation gastric cancer samples were identified, including MYLIP and TMPRSS3, ZNF266 and TM4SF1, SNAI2 and CNFN. To define the featured gene groups that had the highest classification capability, ROC curves to calculate the classification sensitivity and specificity of each gene group were used. The results showed that the combination of SNAI2and CNFN as a classifier had the highest classification sensitivity and specificity. Real-time PCR results showed that 18 of 22 poor differentiation samples were found with high expression of SNAI2 and low expression of CNFN (82%); 6 of 8 well differentiation samples were of low expression of SNAI2 and high expression of CNFN (75%). Conclusion The results indicate that SNAI2 and CNFN are constantly expressed in poor or well differentiation gastric cancer samples, and the expression pattern of these two genes is opposite. These results indicate that SNAI2 and CNFN have the potential for the identification of the differentiation level of gastric cancer. 相似文献