Gene Expression Profiling Predicts Survival in Conventional Renal Cell Carcinoma

BackgroundConventional renal cell carcinoma (cRCC) accounts for most of the deaths due to kidney cancer. Tumor stage, grade, and patient performance status are used currently to predict survival after surgery. Our goal was to identify gene expression features, using comprehensive gene expression profiling, that correlate with survival. Methods and FindingsGene expression profiles were determined in 177 primary cRCCs using DNA microarrays. Unsupervised hierarchical clustering analysis segregated cRCC into five gene expression subgroups. Expression subgroup was correlated with survival in long-term follow-up and was independent of grade, stage, and performance status. The tumors were then divided evenly into training and test sets that were balanced for grade, stage, performance status, and length of follow-up. A semisupervised learning algorithm (supervised principal components analysis) was applied to identify transcripts whose expression was associated with survival in the training set, and the performance of this gene expression-based survival predictor was assessed using the test set. With this method, we identified 259 genes that accurately predicted disease-specific survival among patients in the independent validation group (p p ConclusionscRCC displays molecular heterogeneity and can be separated into gene expression subgroups that correlate with survival after surgery. We have identified a set of 259 genes that predict survival after surgery independent of clinical prognostic factors.

研究背景 传统肾细胞癌（conventional renal cell carcinoma, cRCC）是肾癌致死病例的主要来源。当前临床常规采用肿瘤分期、分级与患者体能状态来预测术后生存情况。本研究旨在通过全面的基因表达谱分析，挖掘与患者生存相关的基因表达特征。
研究方法与结果 本研究借助DNA微阵列技术，对177例原发性cRCC样本开展基因表达谱检测。通过无监督层次聚类分析，将原发性cRCC划分为5个基因表达亚组。该基因表达亚组与患者长期随访中的生存情况显著相关，且独立于肿瘤分级、分期以及患者体能状态这三项临床预后因素。随后，我们将全部肿瘤样本按照肿瘤分级、分期、患者体能状态与随访时长进行匹配平衡后，均匀划分为训练集与测试集。应用半监督学习算法（监督主成分分析）在训练集中筛选与生存相关的转录本，并通过测试集评估该基于基因表达的生存预测模型的性能。借助该方法，我们在独立验证队列中识别出259个可精准预测患者肿瘤特异性生存的基因（p<0.001，C指数=0.78）。
研究结论 cRCC存在分子异质性，可被划分为与术后生存相关的基因表达亚组。本研究已识别出一组共259个基因，其可独立于临床预后因素预测患者术后生存情况。