Gene expression patterns in human liver cancers. Homo sapiens

All the array experiments published in "Gene expression patterns in human liver cancers" by Chen X, et al. Hepatocellular carcinoma (HCC) is a leading cause of death worldwide. Using cDNA microarrays to characterize patterns of gene expression in HCC, we found consistent differences between the expression patterns in HCC compared with those seen in nontumor liver tissues. The expression patterns in HCC were also readily distinguished from those associated with tumors metastatic to liver. The global gene expression patterns intrinsic to each tumor were sufficiently distinctive that multiple tumor nodules from the same patient could usually be recognized and distinguished from all the others in the large sample set on the basis of their gene expression patterns alone. The distinctive gene expression patterns are characteristic of the tumors and not the patient; the expression programs seen in clonally independent tumor nodules in the same patient were no more similar than those in tumors from different patients. Moreover, clonally related tumor masses that showed distinct expression profiles were also distinguished by genotypic differences. Some features of the gene expression patterns were associated with specific phenotypic and genotypic characteristics of the tumors, including growth rate, vascular invasion, and p53 overexpression. Set of arrays organized by shared biological context, such as organism, tumors types, processes, etc. Keywords: Logical Set Overall design: Using regression correlation

本数据集涵盖Chen X等人发表于《人类肝癌的基因表达模式》（Gene expression patterns in human liver cancers）中的全部芯片实验数据。肝细胞癌（Hepatocellular carcinoma, HCC）是全球范围内的主要致死病因之一。本研究通过cDNA微阵列（cDNA microarray）表征肝细胞癌的基因表达模式，发现肝癌组织与非肿瘤肝组织的基因表达模式存在稳定且一致的差异；同时，肝细胞癌的基因表达模式也可与转移至肝脏的肿瘤的表达模式清晰区分。每个肿瘤固有的全局基因表达模式具有足够高的独特性，在大型样本队列中，仅凭基因表达模式即可识别并区分同一患者的多个肿瘤结节与其余所有样本。这类独特的基因表达模式属于肿瘤本身的特征，而非患者个体的特征：同一患者体内克隆独立的肿瘤结节的表达程序，其相似性并不高于不同患者来源的肿瘤之间的表达程序相似性。此外，即便属于克隆相关的肿瘤团块，只要表现出不同的表达谱，也会存在基因型层面的差异。部分基因表达模式特征与肿瘤的特定表型、基因型特征相关，包括生长速率、血管侵袭以及p53过表达。本数据集为基于共享生物学背景（如物种、肿瘤类型、生物学过程等）组织的芯片集。关键词：逻辑集（Logical Set）。总体实验设计：采用回归相关性分析