Abba Martín C, Sun Hongxia, Hawkins Kathleen A, Drake Jeffrey A, Hu Yuhui, Nunez Maria I, Gaddis Sally, Shi Tao, Horvath Steve, Sahin Aysegul, Aldaz C Marcelo
Department of Carcinogenesis, The University of Texas M. D. Anderson Cancer Center, Science Park-Research Division, P.O. Box 389, Smithville, TX 78957, USA.
Mol Cancer Res. 2007 Sep;5(9):881-90. doi: 10.1158/1541-7786.MCR-07-0055.
Global gene expression measured by DNA microarray platforms have been extensively used to classify breast carcinomas correlating with clinical characteristics, including outcome. We generated a breast cancer Serial Analysis of Gene Expression (SAGE) high-resolution database of approximately 2.7 million tags to perform unsupervised statistical analyses to obtain the molecular classification of breast-invasive ductal carcinomas in correlation with clinicopathologic features. Unsupervised statistical analysis by means of a random forest approach identified two main clusters of breast carcinomas, which differed in their lymph node status (P=0.01); this suggested that lymph node status leads to globally distinct expression profiles. A total of 245 (55 up-modulated and 190 down-modulated) transcripts were differentially expressed between lymph node (+) and lymph node (-) primary breast tumors (fold change, >or=2; P<0.05). Various lymph node (+) up-modulated transcripts were validated in independent sets of human breast tumors by means of real-time reverse transcription-PCR (RT-PCR). We validated significant overexpression of transcripts for HOXC10 (P=0.001), TPD52L1 (P=0.007), ZFP36L1 (P=0.011), PLINP1 (P=0.013), DCTN3 (P=0.025), DEK (P=0.031), and CSNK1D (P=0.04) in lymph node (+) breast carcinomas. Moreover, the DCTN3 (P=0.022) and RHBDD2 (P=0.002) transcripts were confirmed to be overexpressed in tumors that recurred within 6 years of follow-up by real-time RT-PCR. In addition, meta-analysis was used to compare SAGE data associated with lymph node (+) status with publicly available breast cancer DNA microarray data sets. We have generated evidence indicating that the pattern of gene expression in primary breast cancers at the time of surgical removal could discriminate those tumors with lymph node metastatic involvement using SAGE to identify specific transcripts that behave as predictors of recurrence as well.
通过DNA微阵列平台测量的全球基因表达已被广泛用于对与临床特征（包括预后）相关的乳腺癌进行分类。我们生成了一个包含约270万个标签的乳腺癌基因表达序列分析（SAGE）高分辨率数据库，以进行无监督统计分析，从而获得与临床病理特征相关的乳腺浸润性导管癌的分子分类。通过随机森林方法进行的无监督统计分析确定了乳腺癌的两个主要聚类，它们在淋巴结状态方面存在差异（P = 0.01）；这表明淋巴结状态导致了全球范围内不同的表达谱。在淋巴结阳性（+）和淋巴结阴性（-）的原发性乳腺肿瘤之间，共有245个（55个上调和190个下调）转录本差异表达（倍数变化≥2；P < 0.05）。通过实时逆转录PCR（RT-PCR）在独立的人类乳腺肿瘤组中验证了各种淋巴结阳性上调转录本。我们验证了HOXC10（P = 0.001）、TPD52L1（P = 0.007）、ZFP36L1（P = 0.011）、PLINP1（P = 0.013）、DCTN3（P = 0.025）、DEK（P = 0.031）和CSNK1D（P = 0.04）转录本在淋巴结阳性乳腺癌中显著过表达。此外，通过实时RT-PCR证实DCTN3（P = 0.022）和RHBDD2（P = 0.002）转录本在随访6年内复发的肿瘤中过表达。此外，荟萃分析用于将与淋巴结阳性状态相关的SAGE数据与公开可用的乳腺癌DNA微阵列数据集进行比较。我们已获得证据表明，手术切除时原发性乳腺癌的基因表达模式可以使用SAGE区分那些有淋巴结转移累及的肿瘤，同时还能识别出作为复发预测指标的特定转录本。
