Department of Urology, Chungbuk National University College of Medicine, Cheongju, Chungbuk, South Korea.
Mol Med. 2011 May-Jun;17(5-6):478-85. doi: 10.2119/molmed.2010.00274. Epub 2011 Feb 4.
There are no reliable criteria to handle disease progression of muscle invasive bladder cancer (MIBC), which strongly influences patient survival. Therefore, an accurate predicting method to identify progressive MIBC patients is greatly needed. The aim of this study was to identify a genetic signature associated with disease progression in MIBC. To address this issue, we analyzed three independent cohorts (a training set, test set 1 and test set 2) comprising a total of 128 MIBC patients. Microarray gene expression profiling, including gene network analysis, was performed in the training set to identify a gene expression signature associated with disease progression. The prognostic value of the signature was validated in test set 1 and test set 2 by microarray and real-time reverse transcriptase polymerase chain reaction (RT-PCR), respectively. The determination of gene expression patterns by microarray data analysis identified 1,320 genes associated with disease progression. Gene network analysis of the 1,320 genes suggested that IL1B, S100A8, S100A9 and EGFR were important mediators of MIBC progression. We validated this putative four-gene signature in two independent cohorts (log-rank test, P < 0.05 each, respectively) and estimated the predictive value of the signature by multivariate Cox regression analysis (hazard ratio [HR], 6.24; 95% confidence interval [CI], 1.58-24.61; P = 0.009). Finally, signature-based stratification demonstrated that the four-gene signature was an independent predictor of MIBC progression. In conclusion, a molecular signature defined by four genes represents a promising diagnostic tool for the identification of MIBC patients at high risk of progression.
目前尚无可靠的标准来处理肌层浸润性膀胱癌（MIBC）的疾病进展，这强烈影响患者的生存。因此，非常需要一种准确的预测方法来识别进展性 MIBC 患者。本研究的目的是确定与 MIBC 疾病进展相关的遗传特征。为了解决这个问题，我们分析了三个独立的队列（一个训练集、一个测试集 1 和一个测试集 2），共包含 128 名 MIBC 患者。在训练集中进行微阵列基因表达谱分析，包括基因网络分析，以确定与疾病进展相关的基因表达特征。通过微阵列和实时逆转录聚合酶链反应（RT-PCR）分别在测试集 1 和测试集 2 中验证该特征的预后价值。通过微阵列数据分析确定基因表达模式，确定了 1320 个与疾病进展相关的基因。对 1320 个基因的基因网络分析表明，IL1B、S100A8、S100A9 和 EGFR 是 MIBC 进展的重要介质。我们在两个独立的队列中验证了这个假定的四基因特征（对数秩检验，P 值均 <0.05），并通过多变量 Cox 回归分析估计了该特征的预测价值（风险比[HR]，6.24；95%置信区间[CI]，1.58-24.61；P=0.009）。最后，基于特征的分层表明，该四基因特征是 MIBC 进展的独立预测因子。总之，由四个基因定义的分子特征代表了一种有前途的诊断工具，可用于识别 MIBC 患者中具有高进展风险的患者。
