Additive effect of the AZGP1, PIP, S100A8 and UBE2C molecular biomarkers improves outcome prediction in breast carcinoma
Additive effect of the AZGP1, PIP, S100A8 and UBE2C molecular biomarkers improves outcome prediction in breast carcinoma
复制标题
DOI：
10.1002/ijc.28497
复制
发表时间：
2014-04-01
影响因子：
6.4
通讯作者：
Helou, Khalil
中科院分区：
文献类型：
--
作者：
Parris, Toshima Z.;Kovacs, Aniko;Helou, Khalil
关键细胞通路的失调对于肿瘤细胞的存活和扩增是至关重要的，这反过来会对患者的预后产生不利影响。为了开发有效的个体化癌症疗法，我们需要更好地了解在基因定义的患者亚组中哪些细胞通路受到干扰。在这里，我们在独立的基因表达微阵列数据集（n = 1，141）和全脸FFPE样本（n = 71）的免疫组织化学中验证了13标记签名的预后价值。使用考克斯回归分析评估单个标志物和含有多个标志物的组的预测性能。在外部基因表达数据集中，13个基因中的6个（AZGP 1、NME 5、S100 A8、SCUBE 2、STC 2和UBE 2C）保留了它们的预后潜力，并且与无病生存显著相关（p < 0.001）。蛋白质分析将特征细化为四个标志物组[AZGP 1，催乳素诱导蛋白（PIP），S100A8和UBE 2C]，这些标志物与周期，高级别肿瘤和较低的疾病特异性生存率显著相关。AZGP 1和PIP在浸润性乳腺组织中的水平显著低于邻近正常组织，而S100 A8和UBE 2C的水平升高。一个预测模型包含四个标志物面板与已建立的临床变量相结合，优于一个模型包含的临床变量单独。我们的研究结果表明，AZGP1、PIP、S100A8和UBE2C的失调对侵袭性乳腺癌表型至关重要，这可能是药物开发的新治疗靶点，以补充已建立的临床变量。开发新的预测性测试来评估治疗，并确定新的预后标志物来预测患者亚组的结果，可能会导致乳腺癌管理的显着改善。在这里，发现四种蛋白质AZGP1、PIP、S100A8和UBE2C的协同活性可作为将乳腺癌患者分层为复发和死亡风险组的有效标志物。当与已建立的临床变量一起考虑时，四标记物组也改善了结果预测。所提出的标记物之间的重叠信号通路表明它们可能是乳腺癌蛋白酶体抑制剂的有吸引力的靶点。
The deregulation of key cellular pathways is fundamental for the survival and expansion of neoplastic cells, which in turn can have a detrimental effect on patient outcome. To develop effective individualized cancer therapies, we need to have a better understanding of which cellular pathways are perturbed in a genetically defined subgroup of patients. Here, we validate the prognostic value of a 13-marker signature in independent gene expression microarray datasets (n = 1,141) and immunohistochemistry with full-faced FFPE samples (n = 71). The predictive performance of individual markers and panels containing multiple markers was assessed using Cox regression analysis. In the external gene expression dataset, six of the 13 genes (AZGP1, NME5, S100A8, SCUBE2, STC2 and UBE2C) retained their prognostic potential and were significantly associated with disease-free survival (p < 0.001). Protein analyses refined the signature to a four-marker panel [AZGP1, Prolactin-inducible protein (PIP), S100A8 and UBE2C] significantly correlated with cycling, high grade tumors and lower disease-specific survival rates. AZGP1 and PIP were found in significantly lower levels in invasive breast tissue as compared with adjacent normal tissue, whereas elevated levels of S100A8 and UBE2C were observed. A predictive model containing the four-marker panel in conjunction with established clinical variables outperformed a model containing the clinical variables alone. Our findings suggest that deregulated AZGP1, PIP, S100A8 and UBE2C are critical for the aggressive breast cancer phenotype, which may be useful as novel therapeutic targets for drug development to complement established clinical variables.What's new? The development of new predictive tests to assess treatment and the identification of novel prognostic markers to predict outcome in patient subgroups could lead to significant improvements in breast cancer management. Here, the synergistic activity of four proteins, AZGP1, PIP, S100A8, and UBE2C, was found to serve as an effective marker for the stratification of breast cancer patients into risk groups for recurrence and death. The four-marker panel also improved outcome prediction when considered alongside established clinical variables. Overlapping signaling pathways between the proposed markers suggest that they may be attractive targets for breast cancer proteasome inhibitors.