J Mol Diagn：有助卵巢癌诊断和预后的新基因

2012年4月9日，来自杜克大学医学中心的科学家已经确定基因“开/关”分子，用来定义临床卵巢癌相关的分子亚型，指出了临床预后和诊断测试中理想的潜在治疗靶标。这些双峰基因可以定义有不同预后情况的肿瘤亚型，便于实施不同的治疗方案。研究人员的研究结果发表在《分子诊断杂志》上。

首席研究员Michael B. Datto博士解释说：我们确定了一套很小的基因，这些基因有可能成为强大的上皮性卵巢癌的预后指标。

卵巢癌在妇科恶性肿瘤中死亡率最高，是第五个最常见的妇女癌症死亡的原因。浆液性癌（卵巢上皮性恶性肿瘤中最常见的类型）的预后方法仍然相对不准确。目前晚期肿瘤患者多数预后较差。然而，在这一群体有对持久的化疗有反应的患者群体，有更好的生存期。

基于乳腺癌的早期研究工作，Datto博士和他的同事们推测卵巢上皮癌中存在临床相关的双峰基因。他们使用一个大的、公开的卵巢癌微阵列数据集，采用一种算法评估285样本中所有的基因表达情况。他们鉴定出许多基因具有强大的双峰表达模式。他们还发现双峰表达模式的基因数量与肿瘤的类型和/或整体病人的生存情况显着相关。

Datto博士表示从临床试验的角度来看，基因连续表达模式使得测试变得困难。然而，一个特定的双峰基因“关/闭”的表达的区别是相对简单的。同时研究人员表示双峰基因也可能是免疫组化定量等测试方法潜在的候选指标。

研究人员发现已知的几个双峰基因在肿瘤形成中的作用。博士Datto总结道：我们所描述的分子开关基因将不仅是在卵巢癌中，也许在多个肿瘤类型也有作用。（生物谷：Bioon）

doi:10.1016/j.jmoldx.2012.01.007
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Genes with Bimodal Expression Are Robust Diagnostic Targets that Define Distinct Subtypes of Epithelial Ovarian Cancer with Different Overall Survival

Dawn N. Kernagis, Allison H.S. Hall, Michael B. Datto

In some cancer types, certain genes behave as molecular switches, with on and off expression states. These genes tend to define tumor subtypes associated with different treatments and different patient survival. We hypothesized that clinically relevant molecular switch genes exist in epithelial ovarian cancer. To test this hypothesis, we applied a bimodal discovery algorithm to a publicly available ovarian cancer expression microarray data set, GSE9891 [285 tumors: 246 malignant serous (MS), 20 endometrioid (EM), and 18 low malignant potential (LMP) ovarian carcinomas]. Genes with robust bimodal expression patterns were identified across all ovarian tumor types and also within selected subtypes: 73 bimodal genes demonstrated differential expression between LMP versus MS and EM; 22 bimodal genes distinguished MS from EM; and 14 genes had significant association with survival among MS tumors. When these genes were combined into a single survival score, the median survival for patients with a favorable versus unfavorable score was 65 versus 29 months (P < 0.0001, hazard ratio = 0.4221). Two independent data sets [high-grade, advanced-stage serous (n = 53) and advanced-stage (n = 119) ovarian tumors] validated the survival score performance. We conclude that genes with bimodal expression patterns not only define clinically relevant molecular subtypes of ovarian carcinoma but also provide ideal targets for translation into the clinical laboratory.