Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Department of Health and Human Services, Bethesda, Maryland 20892-7236, USA.
Clin Cancer Res. 2010 Jan 15;16(2):430-41. doi: 10.1158/1078-0432.CCR-09-1736. Epub 2010 Jan 12.
The molecular drivers that determine histology in lung cancer are largely unknown. We investigated whether microRNA (miR) expression profiles can differentiate histologic subtypes and predict survival for non-small cell lung cancer.
We analyzed miR expression in 165 adenocarcinoma and 125 squamous cell carcinoma (SQ) tissue samples from the Environment And Genetics in Lung cancer Etiology (EAGLE) study using a custom oligo array with 440 human mature antisense miRs. We compared miR expression profiles using t tests and F tests and accounted for multiple testing using global permutation tests. We assessed the association of miR expression with tobacco smoking using Spearman correlation coefficients and linear regression models, and with clinical outcome using log-rank tests, Cox proportional hazards, and survival risk prediction models, accounting for demographic and tumor characteristics.
MiR expression profiles strongly differed between adenocarcinoma and SQ (P(global) < 0.0001), particularly in the early stages, and included miRs located on chromosome loci most often altered in lung cancer (e.g., 3p21-22). Most miRs, including all members of the let-7 family, were downregulated in SQ. Major findings were confirmed by quantitative real time-polymerase chain reaction (qRT-PCR) in EAGLE samples and in an independent set of lung cancer cases. In SQ, the low expression of miRs that are downregulated in the histology comparison was associated with 1.2- to 3.6-fold increased mortality risk. A five-miR signature significantly predicted survival for SQ.
We identified a miR expression profile that strongly differentiated adenocarcinoma from SQ and had prognostic implications. These findings may lead to histology-based therapeutic approaches.
决定肺癌组织学的分子驱动因素在很大程度上尚不清楚。我们研究了 microRNA（miR）表达谱是否可以区分组织学亚型并预测非小细胞肺癌的生存情况。
我们使用带有 440 个人成熟反义 miRs 的定制寡核苷酸阵列，分析了 EAGLE 研究中 165 例腺癌和 125 例鳞状细胞癌（SQ）组织样本中的 miR 表达。我们使用 t 检验和 F 检验比较 miR 表达谱，并使用全局置换检验对多重检验进行了处理。我们使用 Spearman 相关系数和线性回归模型评估了 miR 表达与吸烟的关系，并使用对数秩检验、Cox 比例风险和生存风险预测模型评估了 miR 表达与临床结局的关系，同时考虑了人口统计学和肿瘤特征。
腺癌和 SQ 之间的 miR 表达谱差异很大（P(global) < 0.0001），尤其是在早期阶段，并且包括位于肺癌中经常改变的染色体位置的 miR（例如 3p21-22）。大多数 miR，包括 let-7 家族的所有成员，在 SQ 中下调。主要发现通过 EAGLE 样本中的定量实时聚合酶链反应（qRT-PCR）和独立的肺癌病例集得到了证实。在 SQ 中，组织学比较中下调的 miR 表达水平低与 1.2 至 3.6 倍的死亡率增加风险相关。五个 miR 标志物显著预测了 SQ 的生存情况。
我们确定了一种能够强烈区分腺癌和 SQ 的 miR 表达谱，并且具有预后意义。这些发现可能导致基于组织学的治疗方法。
