Common human cancer genes discovered by integrated gene-expression analysis.

Microarray technology enables a standardized, objective assessment of oncological diagnosis and prognosis. However, such studies are typically specific to certain cancer types, and the results have limited use due to inadequate validation in large patient cohorts. Discovery of genes commonly regulated in cancer may have an important implication in understanding the common molecular mechanism of cancer. We described an integrated gene-expression analysis of 2,186 samples from 39 studies to identify and validate a cancer type-independent gene signature that can identify cancer patients for a wide variety of human malignancies. The commonness of gene expression in 20 types of common cancer was assessed in 20 training datasets. The discriminative power of a signature defined by these common cancer genes was evaluated in the other 19 independent datasets including novel cancer types. QRT-PCR and tissue microarray were used to validate commonly regulated genes in multiple cancer types. We identified 187 genes dysregulated in nearly all cancerous tissue samples. The 187-gene signature can robustly predict cancer versus normal status for a wide variety of human malignancies with an overall accuracy of 92.6%. We further refined our signature to 28 genes confirmed by QRT-PCR. The refined signature still achieved 80% accuracy of classifying samples from mixed cancer types. This signature performs well in the prediction of novel cancer types that were not represented in training datasets. We also identified three biological pathways including glycolysis, cell cycle checkpoint II and plk3 pathways in which most genes are systematically up-regulated in many types of cancer. The identified signature has captured essential transcriptional features of neoplastic transformation and progression in general. These findings will help to elucidate the common molecular mechanism of cancer, and provide new insights into cancer diagnostics, prognostics and therapy.

微阵列技术能够对肿瘤诊断和预后进行标准化、客观的评估。然而，此类研究通常针对特定的癌症类型，并且由于在大型患者队列中验证不足，其结果用途有限。发现癌症中普遍受调控的基因对于理解癌症的常见分子机制可能具有重要意义。 我们描述了对来自39项研究的2186个样本进行的综合基因表达分析，以识别和验证一种不依赖癌症类型的基因特征，该特征能够识别多种人类恶性肿瘤患者。在20个训练数据集中评估了20种常见癌症中基因表达的共性。在包括新型癌症类型的其他19个独立数据集中评估了由这些常见癌症基因定义的特征的判别能力。实时定量聚合酶链反应（QRT - PCR）和组织微阵列被用于验证多种癌症类型中普遍受调控的基因。我们确定了在几乎所有癌组织样本中失调的187个基因。这个187基因特征能够以92.6%的总体准确率有力地预测多种人类恶性肿瘤的癌与正常状态。我们进一步将我们的特征精简为经QRT - PCR确认的28个基因。精简后的特征在对混合癌症类型的样本进行分类时仍能达到80%的准确率。该特征在预测训练数据集中未包含的新型癌症类型时表现良好。我们还确定了三个生物学通路，包括糖酵解、细胞周期检查点II和plk3通路，其中大多数基因在许多类型的癌症中系统性地上调。 所确定的特征总体上捕捉到了肿瘤转化和进展的关键转录特征。这些发现将有助于阐明癌症的常见分子机制，并为癌症诊断、预后和治疗提供新的见解。