Robust Gene Expression Signature from Formalin-Fixed Paraffin-Embedded Samples Predicts Prognosis of Non-Small-Cell Lung Cancer Patients. Homo sapiens

The requirement of frozen tissues for microarray experiments limits the clinical usage of genome-wide expression profiling using microarray technology. Keywords: Lung Cancer Prognosis, Gene Expression Signature, Formalin Fixed Paraffin Embedded Samples The goal of this study is to test the feasibility of developing lung cancer prognosis gene signatures using genome-wide expression profiling of formalin-fixed paraffin-embedded (FFPE) samples, which are widely available and provide a valuable rich source for studying the association of molecular changes in cancer and associated clinical outcomes. FFPE tumor specimens were collected, and total RNA was processed for analysis on the Affymetrix U133 plus 2.0 arrays according to Affymetrix protocols. The quality control procedure for microarray data analysis was based on the percentage of present calls calculated by the MAS5 package. We selected 55 arrays with at least 15% of probe sets present, and we selected 1400 probe sets that present on all 55 arrays for data analysis. After microarray analysis QC, we used the RMA background correction algorithm to remove non-specific background noise. A robust regression model was fitted to the probe level data, and the fitted expression values for the probes at the 3' end were used to summarize the probe set expression values. Quantile-quantile normalization was used to normalize all the arrays. The 55 samples and the derived gene expression values for 1400 genes based on the robust regression model were used to develop gene signatures and were uploaded as supplementary data (GSE29013_fitted_1400_probes.txt). Overall design: We micro dissected tumor area from FFPE specimen, and used Affymetrix U133 plus 2.0 arrays to obtain gene expression data.

微阵列实验对冰冻组织的需求限制了利用微阵列技术开展全基因组表达谱分析的临床应用。
关键词：肺癌预后、基因表达特征、福尔马林固定石蜡包埋（Formalin Fixed Paraffin Embedded，FFPE）样本。
本研究旨在探索利用福尔马林固定石蜡包埋（FFPE）样本的全基因组表达谱开发肺癌预后基因特征的可行性；此类样本易于获取，是探究癌症分子改变与临床结局关联的宝贵丰富资源。
收集FFPE肿瘤标本，按照Affymetrix实验流程，对总RNA进行处理后在Affymetrix U133 Plus 2.0基因芯片上开展分析。
微阵列数据分析的质控流程基于MAS5软件包计算的检出率。
我们筛选出55张检出探针集占比不低于15%的芯片，并选取在全部55张芯片中均存在的1400个探针集用于后续数据分析。
微阵列分析质控完成后，我们采用RMA背景校正算法去除非特异性背景噪音。
针对探针水平数据构建稳健回归模型，并利用3'端探针的拟合表达值汇总得到探针集的表达量。
采用分位数归一化方法对所有芯片数据进行标准化处理。
本研究利用这55份样本及基于稳健回归模型得到的1400个基因的表达量数据开发基因特征，并将相关数据作为补充材料上传（文件名为GSE29013_fitted_1400_probes.txt）。
实验整体设计：从FFPE标本中显微切割肿瘤区域，采用Affymetrix U133 Plus 2.0基因芯片获取基因表达数据。