Iwao Kyoko, Matoba Ryo, Ueno Noriko, Ando Akiko, Miyoshi Yasuo, Matsubara Kenichi, Noguchi Shinzaburo, Kato Kikuya
Taisho Laboratory of Functional Genomics, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0101, Japan.
Hum Mol Genet. 2002 Jan 15;11(2):199-206. doi: 10.1093/hmg/11.2.199.
The natural progression of breast cancer differs greatly between patients; the precise prediction of this disease course will improve the efficacy of therapeutics. Gene expression profiling may elucidate the undiscovered biological variations between seemingly similar cancers, leading to a new cancer classification system valuable in accurate diagnosis. The expression levels of 2412 genes, derived from 98 cancer samples, were precisely recorded by a high throughput RT-PCR technique, adapter-tagged competitive PCR. Subsequent cluster analysis revealed a molecular profile, correlating with estrogen receptor levels and the presence of lymph node metastases. We analyzed 301 cancer samples for the expression patterns of 21 genes critical in this categorization. The classification of the samples into three major groups was verified utilizing principal component analysis. This molecular classification system correlated significantly with early recurrence, independent of lymph node status. This malignant potential is associated with the expression levels of a group of genes, which comprise a set of candidates potentially useful in diagnostic prediction. These genes and the associated control mechanisms may also be effective therapeutic targets.
乳腺癌在不同患者中的自然病程差异很大；精确预测这种疾病进程将提高治疗效果。基因表达谱分析可能会阐明看似相似的癌症之间未被发现的生物学差异，从而形成一种在准确诊断中很有价值的新癌症分类系统。通过高通量RT-PCR技术（衔接子标签竞争PCR）精确记录了来自98个癌症样本的2412个基因的表达水平。随后的聚类分析揭示了一种分子特征，与雌激素受体水平和淋巴结转移的存在相关。我们分析了301个癌症样本中对该分类至关重要的21个基因的表达模式。利用主成分分析验证了样本分为三大类的分类情况。这种分子分类系统与早期复发显著相关，与淋巴结状态无关。这种恶性潜能与一组基因的表达水平相关，这组基因包含一组可能在诊断预测中有用的候选基因。这些基因及相关的调控机制也可能是有效的治疗靶点。
