Genome-scale analysis of lung cancer progression.

Uriel M Malyankar, John R MacDougall
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引用次数: 9

Abstract

Among cancers, lung cancer is the single biggest killer in the US. It is estimated that lung cancer was responsible for 171900 newly diagnosed cases of cancer in the US in 2003, and for 157200 deaths. Over many years, however, there has been little improvement in the clinical outcome of lung cancer, and any improvement in the incidence or mortality from lung cancer can largely be attributed to smoking cessation and not to the success of therapy. The histopathology of lung cancer reveals that it is a disease with many faces. Lung cancer is often nonresponsive to traditional therapy, leaving few, if any, alternatives in the management of the advanced stages of the disease. The molecular pathogenesis of lung cancer, only recently illuminated, involves numerous molecular and cell biological changes revealing a very complex disease progression. Large-scale mRNA expression analysis has been recently used to classify lung cancers molecularly. These techniques have been used successfully to differentiate lung cancer histotypes based on patterns of genes expressed. The use of protein analysis to this end has also been attempted, with limited correlation with RNA experiments. This likely reflects the limited sensitivity of the technologies and complex, poorly understood post-synthesis protein modifications. In any event, there have been great strides made in understanding the nature of lung cancer from a molecular perspective; these effects represent a great advancement in the diagnosis and prognosis of lung cancer. Moreover, these advances may lead to the improvement of patient survival by guiding the choice of more efficacious therapy.

肺癌进展的基因组尺度分析。
在癌症中,肺癌是美国最大的单一杀手。据估计,2003年美国有171900例新诊断的癌症病例是由肺癌引起的,157200例死亡是由肺癌引起的。然而,多年来,肺癌的临床结果几乎没有改善,肺癌发病率或死亡率的任何改善在很大程度上可归因于戒烟,而不是治疗的成功。肺癌的组织病理学表明,肺癌是一种多面性疾病。肺癌通常对传统疗法没有反应,因此在晚期疾病的治疗中几乎没有其他选择。肺癌的分子发病机制,直到最近才被阐明,涉及许多分子和细胞生物学变化,揭示了一个非常复杂的疾病进展。大规模mRNA表达分析最近被用于肺癌的分子分类。这些技术已经成功地用于根据表达的基因模式区分肺癌组织型。为此目的也尝试了使用蛋白质分析,但与RNA实验的相关性有限。这可能反映了技术的有限敏感性和复杂的,知之甚少的合成后蛋白质修饰。无论如何,从分子角度理解肺癌的本质已经取得了很大的进步;这些作用在肺癌的诊断和预后方面取得了巨大的进步。此外,这些进展可能会通过指导选择更有效的治疗方法来改善患者的生存。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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