Department of Pathology, Yale University School of Medicine, New Haven, CT 06520, USA.
Department of Obstetrics, Gynecology and Reproductive Sciences, Division of Gynecologic Oncology, Yale University School of Medicine, New Haven, CT 06520, USA.
Hum Pathol. 2018 Jun;76:133-140. doi: 10.1016/j.humpath.2018.02.019. Epub 2018 Mar 5.
Synchronous endometrial and ovarian malignancies occur in 5% of women presenting with endometrial cancer and 10% of patients presenting with ovarian malignancy. When a high-grade serous carcinoma concurrently involves both ovary and endometrium, pathological determination of whether they are synchronous primaries or metastatic tumors from one primary site can be challenging. MicroRNAs (miRNA) are 22-nucleotide noncoding RNAs that are aberrantly expressed in cancer cells and may inherit their cellular lineage characteristics. We explored possible differential miRNA signatures that may separate high-grade ovarian serous carcinoma from primary endometrial serous carcinoma. Forty-seven samples of histologically pure high-grade serous carcinoma of both uterine (16 case) and ovarian primaries (31 cases) were included. Expression of 384 mature miRNAs was analyzed using ABI TaqMan Low-Density Arrays technology. A random forest model was used to identify miRNAs that together could differentiate between uterine and ovarian serous carcinomas. Among 150 miRNAs detectable at various levels in the study cases, a panel of 11-miRNA signatures was identified to significantly discriminate between ovarian and uterine serous carcinoma (P < .05). A nested cross-validated convergent forest plot using 6 of the 11 miRNA signature was eventually established to classify the tumors with 91.5% accuracy. In conclusion, we have characterized a miRNA signature panel in this exploratory study that shows significant discriminatory power in separating primary ovarian high-grade serous carcinoma from its endometrial counterpart.
同时性子宫内膜和卵巢恶性肿瘤发生在 5%的子宫内膜癌患者和 10%的卵巢恶性肿瘤患者中。当高级别浆液性癌同时累及卵巢和子宫内膜时,病理确定它们是同步原发性肿瘤还是来自一个原发性肿瘤的转移瘤可能具有挑战性。微小 RNA(miRNA)是 22 个核苷酸的非编码 RNA,在癌细胞中表达异常,可能继承其细胞谱系特征。我们探讨了可能的差异 miRNA 特征,以区分高级别卵巢浆液性癌和原发性子宫内膜浆液性癌。包括 47 例组织学上纯的高级别浆液性癌,分别来自子宫(16 例)和卵巢原发性肿瘤(31 例)。使用 ABI TaqMan 低密度阵列技术分析了 384 种成熟 miRNA 的表达。使用随机森林模型来识别可以区分子宫和卵巢浆液性癌的 miRNA。在研究病例中以不同水平检测到的 150 个 miRNA 中,确定了一组 11 个 miRNA 特征,可以显著区分卵巢和子宫浆液性癌(P<0.05)。最终使用 6 个 miRNA 特征的嵌套交叉验证收敛森林图建立了一个肿瘤分类模型,准确率为 91.5%。总之,在这项探索性研究中,我们描述了一个 miRNA 特征面板,该面板在区分原发性卵巢高级别浆液性癌与其子宫内膜对应物方面具有显著的区分能力。