Bladder cancer-associated gene expression signatures identified by profiling of exfoliated urothelia.
Bladder cancer-associated gene expression signatures identified by profiling of exfoliated urothelia.
复制标题
DOI：
10.1158/1055-9965.epi-08-1002
复制
发表时间：
2009-02
期刊：
影响因子：
--
通讯作者：
Goodison S
中科院分区：
文献类型：
--
作者：
Rosser CJ;Liu L;Sun Y;Villicana P;McCullers M;Porvasnik S;Young PR;Parker AS;Goodison S
膀胱癌是美国第五种最常见的恶性肿瘤，也是世界上最常见的恶性肿瘤之一。它有50%的复发概率，因此提倡对患者进行严格的、长期的监测。软性膀胱镜联合尿液细胞学检查(VUC)是主要的诊断方法，但膀胱镜检查是一种令人不适的侵入性检查方法，且VUC对除高级别肿瘤外的所有肿瘤的敏感性都很低。因此，改进非侵入性尿检评估策略将使患者受益。我们应用基因表达微阵列分析了从46名后来证实有或没有膀胱癌的患者的膀胱冲洗液中恢复的尿路脱落。来自包含56,000个靶点的微阵列的数据接受了一组统计分析，以确定与膀胱癌相关的基因签名。采用层次聚类和监督学习算法对样本进行基于肿瘤负荷的分类。319个基因探针的差异表达基因集与膀胱癌的存在相关(P<；0.01)，蛋白质相互作用网络的可视化显示血管内皮生长因子和AGT是肿瘤细胞中的关键因子。使用监督机器学习和交叉验证方法构建了一个14基因分子分类器，该分类器能够对膀胱癌患者和非膀胱癌患者进行分类，总体准确率为76%。我们的结果表明，利用肿瘤尿路脱落中存在的分子标记来检测膀胱癌是可能的。对癌症相关图谱的进一步研究和验证可能会揭示出对膀胱癌的非侵入性检测和监测的重要生物标志物。
Bladder cancer is the fifth most commonly diagnosed malignancy in the United States and one of the most prevalent worldwide. It harbors a probability of recurrence of >50%, thus rigorous, long-term surveillance of patients is advocated. Flexible cystoscopy coupled with voided urine cytology (VUC) is the primary diagnostic approach, but cystoscopy is an uncomfortable, invasive procedure and the sensitivity of VUC is poor in all but high-grade tumors. Thus, improvements in non-invasive urinalysis assessment strategies would benefit patients. We applied gene expression microarray analysis to exfoliated urothelia recovered from bladder washes obtained prospectively from 46 patients with subsequently confirmed presence or absence of bladder cancer. Data from microarrays containing 56,000 targets was subjected to a panel of statistical analyses to identify bladder cancer-associated gene signatures. Hierarchical clustering and supervised learning algorithms were used to classify samples on the basis of tumor burden. A differentially expressed geneset of 319 gene probes was associated with the presence of bladder cancer (P<0.01), and visualization of protein interaction networks revealed VEGF and AGT as pivotal factors in tumor cells. Supervised machine learning and a cross-validation approach were used to build a 14-gene molecular classifier that was able to classify patients with and without bladder cancer with an overall accuracy of 76%. Our results show that it is possible to achieve the detection of bladder cancer using molecular signatures present in exfoliated tumor urothelia. Further investigation and validation of the cancer-associated profiles may reveal important biomarkers for the non-invasive detection and surveillance of bladder cancer.