Endometrial cancer (EC) is the most common gynecologic cancer in women, and the incidence of EC has increased by about 1% per year in the U. S over the last 10 years. Although 5-year survival rates for early-stage EC are around 80%, certain subtypes of EC that lose nuclear hormone receptor (NHR) expression are associated with poor survival rates. For example, estrogen receptor (ER)-negative EC typically harbors a worse prognosis compared to ER-positive EC. The molecular basis for the loss of NHR expression in endometrial tumors and its contribution to poor survival is largely unknown. Furthermore, there are no tools to systematically identify tumors that lose NHR mRNA expression relative to normal tissue. The development of such an approach could identify sets of NHR-based biomarkers for classifying patients into subgroups with poor survival outcomes. Here, a new computational method, termed receptLoss, was developed for identifying NHR expression loss in endometrial cancer relative to adjacent normal tissue. When applied to gene expression data from The Cancer Genome Atlas (TCGA), receptLoss identified 6 NHRs that were highly expressed in normal tissue and exhibited expression loss in a subset of endometrial tumors. Three of the six identified NHRs – estrogen, progesterone, and androgen receptors – that are known to lose expression in ECs were correctly identified by receptLoss. Additionally, a novel association was found between thyroid hormone receptor beta (THRB) expression loss, increased expression of miRNA-146a, and increased rates of 5-year survival in the EC TCGA patient cohort. THRB expression loss occurs independently of estrogen and progesterone expression loss, suggesting the discovery of a distinct, clinically-relevant molecular subgroup. ReceptLoss is a novel, open-source software tool to systematically identify NHR expression loss in cancer. The application of receptLoss to endometrial cancer gene expression data identified THRB, a previously undescribed biomarker of survival in endometrial cancer. Applying receptLoss to expression data from additional cancer types could lead to the development of biomarkers of disease progression for patients with any other tumor type. ReceptLoss can be applied to expression data from additional cancer types with the goal of identifying biomarkers of differential survival.

中文翻译：

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鉴定出甲状腺激素受体β表达缺失且生存率提高的子宫内膜癌患者的新亚组。

子宫内膜癌 (EC) 是女性最常见的妇科癌症，过去 10 年来，美国 EC 的发病率每年增加约 1%。尽管早期 EC 的 5 年生存率约为 80%，但失去核激素受体 (NHR) 表达的某些 EC 亚型与较差的生存率相关。例如，与 ER 阳性 EC 相比，雌激素受体 (ER) 阴性 EC 的预后通常较差。子宫内膜肿瘤中 NHR 表达缺失的分子基础及其对不良生存的影响尚不清楚。此外，没有工具可以系统地识别相对于正常组织失去 NHR mRNA 表达的肿瘤。这种方法的开发可以识别基于 NHR 的生物标志物集，用于将患者分为生存结果较差的亚组。在此，开发了一种称为 receptLoss 的新计算方法，用于识别子宫内膜癌相对于邻近正常组织的 NHR 表达缺失。当应用到癌症基因组图谱 (TCGA) 的基因表达数据时，receptLoss 识别出 6 个在正常组织中高表达并在子宫内膜肿瘤子集中表现出表达缺失的 NHR。receptLoss 正确识别了 6 个已识别的 NHR 中的 3 个（雌激素、孕激素和雄激素受体），这些受体已知在 EC 中失去表达。此外，在 EC TCGA 患者队列中，甲状腺激素受体 β (THRB) 表达缺失、miRNA-146a 表达增加和 5 年生存率增加之间发现了一种新的关联。THRB 表达丧失独立于雌激素和孕激素表达丧失而发生，表明发现了一个独特的、临床相关的分子亚组。ReceptLoss 是一种新颖的开源软件工具，可系统地识别癌症中的 NHR 表达缺失。将 receptLoss 应用于子宫内膜癌基因表达数据确定了 THRB，这是一种先前未描述的子宫内膜癌生存的生物标志物。将receptLoss应用于其他癌症类型的表达数据可能会导致为任何其他肿瘤类型患者开发疾病进展的生物标志物。ReceptLoss 可应用于其他癌症类型的表达数据，目的是识别差异生存的生物标志物。