Santos Edgardo S, Blaya Marcelo, Raez Luis E
University of Miami Leonard M. Miller School of Medicine/Sylvester Comprehensive Cancer Center, FL, USA.
Clin Lung Cancer. 2009 May;10(3):168-73. doi: 10.3816/CLC.2009.n.023.
Despite new developments in molecular techniques and better knowledge on lung cancer tumor biology, many genetic alterations associated with the development and progression of lung carcinogenesis still remain unclear. Although the development of targeted agents has improved response rates and survival, lung cancer has a very high mortality rate, even for early stages. Thus, there is a greater need for other mechanisms or technologies that may help us diagnose, predict, and treat patients with lung cancer in a more effective way. One of these technologies has been the use of genomics. Some of the available genomic technologies include single-nucleotide polymorphism analysis, high-throughput capillary sequencing, serial analysis of gene expression, and gene expression arrays. DNA microarray analysis is capable of discovering changes in DNA expression within the neoplastic tumor. Thus, gene expression array could help us to decipher the complexity and interaction of different oncogenic pathways and, hence, could contribute to the selection of better targeted agents on an individual basis rather than a general and nonspecific approach as it has been done for many decades. Several studies initiated a few years ago have started to produce fruitful results. Herein, we review the role of gene expression profiling in lung cancer as a diagnostic tool, predictive and prognostic biomarker, and its potential use for a "personalized" medicine in the years to come.
尽管分子技术有了新进展,且对肺癌肿瘤生物学有了更深入了解,但许多与肺癌发生发展相关的基因改变仍不清楚。尽管靶向药物的发展提高了缓解率和生存率,但肺癌的死亡率仍然很高,即使是早期阶段。因此,更迫切需要其他机制或技术,以帮助我们更有效地诊断、预测和治疗肺癌患者。其中一项技术就是基因组学的应用。一些现有的基因组技术包括单核苷酸多态性分析、高通量毛细管测序、基因表达序列分析和基因表达阵列。DNA微阵列分析能够发现肿瘤组织中DNA表达的变化。因此,基因表达阵列可以帮助我们解读不同致癌途径的复杂性和相互作用,从而有助于基于个体情况而非几十年来一直采用的通用非特异性方法来选择更好的靶向药物。几年前启动的几项研究已开始产生丰硕成果。在此,我们综述基因表达谱在肺癌中作为诊断工具、预测和预后生物标志物的作用,以及其在未来“个性化”医学中的潜在应用。