Prostate Cancer

Prostate cancer, a leading cause of cancer death, displays a broad range of clinical behavior from relatively indolent to aggressive metastatic disease. To explore potential molecular variation underlying this clinical heterogeneity, we profiled gene expression in 62 primary prostate tumors, as well as 41 normal prostate specimens and nine lymph node metastases, using cDNA microarrays containing approximately 26,000 genes. Unsupervised hierarchical clustering readily distinguished tumors from normal samples, and further identified three subclasses of prostate tumors based on distinct patterns of gene expression. High-grade and advanced stage tumors, as well as tumors associated with recurrence, were disproportionately represented among two of the three subtypes, one of which also included most lymph node metastases. To further characterize the clinical relevance of tumor subtypes, we evaluated as surrogate markers two genes differentially expressed among tumor subgroups by using immunohistochemistry on tissue microarrays representing an independent set of 225 prostate tumors. Positive staining for MUC1, a gene highly expressed in the subgroups with "aggressive" clinicopathological features, was associated with an elevated risk of recurrence (P = 0.003), whereas strong staining for AZGP1, a gene highly expressed in the other subgroup, was associated with a decreased risk of recurrence (P = 0.0008). In multivariate analysis, MUC1 and AZGP1 staining were strong predictors of tumor recurrence independent of tumor grade, stage, and preoperative prostate-specific antigen levels. Our results suggest that prostate tumors can be usefully classified according to their gene expression patterns, and these tumor subtypes may provide a basis for improved prognostication and treatment stratification. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Keywords: disease_state_design Using regression correlation

前列腺癌是癌症致死的主要诱因之一，其临床行为谱跨度极大，从相对惰性的病变到侵袭性转移性疾病不等。为探究该临床异质性背后潜在的分子变异机制，我们采用搭载约26000个基因的cDNA微阵列（cDNA microarray），对62例原发性前列腺肿瘤、41例正常前列腺组织标本以及9例淋巴结转移灶进行了基因表达谱分析。无监督层次聚类（unsupervised hierarchical clustering）可轻易区分肿瘤样本与正常样本，并基于独特的基因表达模式进一步鉴定出三类前列腺肿瘤亚类。高级别、晚期肿瘤以及伴有复发的肿瘤在三类亚类中的两类中占比异常偏高，其中一类还涵盖了绝大多数淋巴结转移灶。为进一步明确肿瘤亚类的临床相关性，我们针对独立队列的225例前列腺肿瘤构建组织微阵列（tissue microarray），通过免疫组织化学（immunohistochemistry）技术，将在肿瘤亚组中差异表达的两个基因作为替代标志物进行评估。在具有“侵袭性”临床病理特征的亚组中高表达的MUC1基因，其阳性染色与复发风险升高显著相关（P=0.003）；而在另一亚组中高表达的AZGP1基因，其强染色则与复发风险降低相关（P=0.0008）。在多变量分析（multivariate analysis）中，MUC1与AZGP1的染色结果可独立于肿瘤分级、分期以及术前前列腺特异性抗原（prostate-specific antigen，PSA）水平，有效预测肿瘤复发。本研究结果表明，可依据基因表达模式对前列腺肿瘤进行有效的分类，而这些肿瘤亚类可为优化预后评估与治疗分层提供依据。疾病状态实验设计类型指的是针对感染、病理病变、综合征等各类疾病状态展开研究的实验设计类型。关键词：disease_state_design、回归相关性