Department of Molecular Cancerogenesis, Medical University of Lodz, Mazowiecka 6/8 Str., 92-215 Lodz, Poland.
Med Sci Monit. 2010 Mar;16(3):CR132-136.
Breast cancer is very heterogeneous disease at both the clinical and molecular levels. Most research is based on analysis of a single gene, but only complex investigation of genes involved in different cell processes such as apoptosis or signal transduction can help to better understand the biology of this type of tumour. Novel techniques such as microarrays and real-time RT-PCR allow performance of such complex research. Only this kind of approach can improve cancer treatment through individualisation of disease cases with different molecular backgrounds.
MATERIAL/METHODS: We performed quantitative RT-PCR to analyze levels of expression of 10 genes in 119 patient samples: 4 with known good prognosis signature (WWOX, ESR1, CDH, BAX) and 6 previously reported as bad prognosis markers of breast cancer (KRT5, KRT14, KRT17, CCNE1, BCL2, BIRC5).
The algorithm composed of 10 genes distinguishes 2 statistically significant groups of patients with different rates of disease-free survival. However, when patients were divided into 2 groups according to estrogen receptor status, this algorithm could be applied only for a group with estrogen receptor negative breast cancer. High algorithm value is a good prognostic factor of disease-free survival for patients with estrogen negative breast cancers (HR=0.26; p=0.0039), but not for patients with ER positive tumors (p>0.05).
The presented multigene algorithm may be used for outcome evaluation for estrogen receptor-negative breast cancer patients.
乳腺癌在临床和分子水平上具有很强的异质性。大多数研究都是基于单个基因的分析，但只有对涉及细胞凋亡或信号转导等不同细胞过程的基因进行复杂的研究，才能帮助更好地了解这种肿瘤的生物学特性。微阵列和实时 RT-PCR 等新技术可以进行这种复杂的研究。只有这种方法才能通过对不同分子背景的疾病病例进行个体化治疗来改善癌症治疗。
材料/方法：我们对 119 个患者样本进行了定量 RT-PCR 分析，以检测 10 个基因的表达水平：4 个具有已知良好预后特征的基因（WWOX、ESR1、CDH、BAX）和 6 个先前报道的乳腺癌不良预后标志物（KRT5、KRT14、KRT17、CCNE1、BCL2、BIRC5）。
由 10 个基因组成的算法可区分出 2 组具有不同无病生存率的统计学显著患者群体。然而，当根据雌激素受体状态将患者分为 2 组时，该算法仅适用于雌激素受体阴性乳腺癌患者。高算法值是雌激素受体阴性乳腺癌患者无病生存率的良好预后因素（HR=0.26；p=0.0039），但对雌激素受体阳性肿瘤患者则不然（p>0.05）。
所提出的多基因算法可用于评估雌激素受体阴性乳腺癌患者的预后。
