Bladder cancer microarrays. Homo sapiens

Background: Current diagnosis and treatment of urinary bladder cancer (BC) has shown great progress with the utilization of microarrays. Purpose: Our goal was to identify common differentially expressed (DE) genes among clinically relevant subclasses of BC using microarrays. Methodology/Principal Findings: BC samples and controls, both experimental as well as publicly available datasets, were analyzed by whole genome microarrays. We grouped the samples according to their histology and defined the DE genes in each sample individually as well as in each tumor group, respectively. A dual analysis strategy was followed: First, experimental samples were analyzed and conclusions were extracted; and second, experimental sets were combined with publicly available microarray datasets and were further analyzed in search for common DE genes. The experimental dataset, identified 831 genes that were differentially expressed in all tumor samples, simultaneously. Moreover, 33 genes were up-regulated and 85 genes were down-regulated in all 10 BC samples compared to the 5 normal tissues, simultaneously. Hierarchical clustering partitioned tumor groups in accordance to their histology. K-means clustering of all genes and all samples, as well as clustering of tumor groups, presented 49 clusters. K-means clustering of common DE genes in all samples revealed 24 clusters. Genes manifested various differential patterns of expression, based on PCA. YY1 and NFκB were among the most common transcription factors that regulated the expression of the identified DE genes. Chromosome 1 contained 32 DE genes, followed by chromosomes 2 and 11, which contained 25 and 23 DE genes, respectively. Chromosome 21 had the least number of DE genes. GO analysis revealed the prevalence of transport and binding genes in the common down-regulated DE genes; the prevalence of RNA metabolism and processing genes in the up-regulated DE genes; as well as the prevalence of genes responsible for cell communication and signal transduction in the DE genes that were down-regulated in T1-Grade III tumors and up-regulated in T2/T3-Grade III tumors. Combination of all available samples revealed 17 common genes (BMP4, CRYGD, DBH, GJB1, KRT83, MPZ, NHLH1, TACR3, ACTC1, MFAP4, SPARCL1, TAGLN, TPM2, CDC20, LHCGR, TM9SF1 and HCCS) four of which participate in numerous pathways. Conclusions/Significance: The identification of the common DE genes among BC samples of different histology can provide further insight into the discovery of new putative markers. Overall design: Oligos microarray chips (~57k genes) were obtained from GE HealthCare (IL) and AppliedMicroarrays (MA) (former Amersham Biosciences) (CodeLink 57k Human Whole Genome). Hybridization was performed with the CodeLink RNA amplification and Labeling kit as described by the manufacturer, utilizing the Cy5 fluorescent dye. Slides were scanned with a microarray scanner (ScanArray 4000XL). Images were generated with ScanArray microarray acquisition software (GSI Lumonics, USA). cRNAs from three experimental setups were used in single experiments with internal spikes as controls. The experimental setups consisted of 10 urinary BC samples of different histology and 5 control samples. The scanned images were further processed with the CodeLink Expression Analysis Software v5.0 from Amersham Biosciences (presently GE Health Care Inc.). The experimental setup was analyzed based on the reference-design as described previously. All tumor samples were compared against the mean value of the control samples. Raw microarray data are available as supplementary data and at the GEO microarray database. All microarray data are MIAME compliant. We used the following publicly available microarray datasets in our analysis: 1) GSE89 dataset (GDS183), comprised of 40 BC samples; 2) GSE3167 dataset (GDS1479), comprised of 60 samples (9 controls and 51 BC samples); 3) GSE7476 dataset, composed of 12 samples (3 controls and 9 BC samples) and 4) GSE12630 dataset, comprised of 19 BC samples. In total, our pooled microarray analysis was composed of 17 control samples (n=5, for the CodeLink platform; and n=12, for the rest microarray platforms) and 129 BC samples (n=10, for the CodeLink platform; and n=119, for the rest microarray platforms). Public data were used in their available normalized form, since background correction and normalization had already been performed.

研究背景：当前，随着微阵列（microarrays）技术的应用，膀胱癌（urinary bladder cancer, BC）的诊断与治疗已取得长足进展。
研究目的：本研究旨在通过微阵列技术，筛选出膀胱癌临床相关亚型中共有的差异表达（differentially expressed, DE）基因。
方法与主要结果：本研究通过全基因组微阵列技术，对实验来源及公共数据库获取的膀胱癌样本与对照样本开展分析。我们依据样本的组织学特征进行分组，并分别针对单一样本及各肿瘤组定义差异表达基因。本研究采用双重分析策略：首先对实验样本进行独立分析并推导结论；其次将实验数据集与公共微阵列数据集合并，进一步筛选共有的差异表达基因。
通过实验数据集分析，我们共鉴定出在所有肿瘤样本中均呈现差异表达的831个基因。此外，相较于5份正常组织对照样本，10份膀胱癌样本中共存在33个上调基因与85个下调基因。层级聚类（hierarchical clustering）可依据组织学特征对肿瘤组进行准确划分。对所有基因及样本进行K均值聚类（K-means clustering），以及对肿瘤组单独聚类分析，均得到49个聚类簇；对所有样本中共有的差异表达基因进行K均值聚类，则得到24个聚类簇。基于主成分分析（principal component analysis, PCA），不同基因呈现出多样化的差异表达模式。YY1与NFκB是调控本次筛选出的差异表达基因的最常见转录因子之一。1号染色体上共包含32个差异表达基因，其次为2号与11号染色体，分别包含25个与23个差异表达基因；21号染色体上的差异表达基因数量最少。基因本体（Gene Ontology, GO）富集分析显示：在共有的下调差异表达基因中，转运与结合相关基因占比最高；在上调差异表达基因中，RNA代谢与加工相关基因占比最高；而在T1-III级肿瘤中下调、且在T2/T3-III级肿瘤中上调的差异表达基因中，细胞通信与信号转导相关基因占比最高。整合所有可用样本后，我们共筛选出17个共有差异表达基因（BMP4、CRYGD、DBH、GJB1、KRT83、MPZ、NHLH1、TACR3、ACTC1、MFAP4、SPARCL1、TAGLN、TPM2、CDC20、LHCGR、TM9SF1及HCCS），其中4个基因参与多条信号通路。
结论与意义：鉴定不同组织学特征膀胱癌样本中共有的差异表达基因，可为新型潜在生物标志物的发现提供全新的研究视角。
实验整体设计：本研究使用的寡核苷酸微阵列芯片（覆盖约57,000个基因）购自通用电气医疗集团（GE HealthCare, IL）与AppliedMicroarrays（MA，前身为Amersham Biosciences）旗下的CodeLink 57k人类全基因组芯片。按照制造商说明书，使用CodeLink RNA扩增与标记试剂盒完成杂交实验，采用Cy5荧光染料进行标记。芯片使用微阵列扫描仪（ScanArray 4000XL）进行扫描，扫描图像通过ScanArray微阵列采集软件（GSI Lumonics, 美国）生成。本研究使用3组实验体系的cRNA开展单次实验，并以内部spike作为对照。实验体系包含10份不同组织学特征的膀胱癌样本与5份对照样本。扫描得到的图像通过Amersham Biosciences（现通用电气医疗集团）旗下的CodeLink Expression Analysis Software v5.0进行后续处理。本实验基于此前报道的参考设计方案开展分析，将所有肿瘤样本与对照样本的平均值进行比对。原始微阵列数据已作为补充数据上传至GEO（Gene Expression Omnibus）微阵列数据库，且所有微阵列数据均符合MIAME标准。
本研究使用的公共微阵列数据集如下：1) GSE89数据集（GDS183），包含40份膀胱癌样本；2) GSE3167数据集（GDS1479），包含60份样本（9份对照与51份膀胱癌样本）；3) GSE7476数据集，包含12份样本（3份对照与9份膀胱癌样本）；4) GSE12630数据集，包含19份膀胱癌样本。综上，本研究合并后的微阵列分析共包含17份对照样本（其中5份来自CodeLink平台，其余12份来自其他微阵列平台）与129份膀胱癌样本（其中10份来自CodeLink平台，其余119份来自其他微阵列平台）。公共数据集均采用已完成背景校正与归一化的标准化数据进行后续分析。