Department of Genetics, Cancer Research Institute, Biomedical Research Center of Slovak Academy of Sciences, 84505 Bratislava, Slovakia.
Laboratory of Bioinformatics, Biomedical Research Center of Slovak Academy of Sciences, 84505 Bratislava, Slovakia.
Int J Mol Sci. 2020 Dec 24;22(1):127. doi: 10.3390/ijms22010127.
The current guidelines for diagnosis, prognosis, and treatment of endometrial cancer (EC), based on clinicopathological factors, are insufficient for numerous reasons; therefore, we investigated the relevance of miRNA expression profiles for the discrimination of different EC subtypes. Among the miRNAs previously predicted to allow distinguishing of endometrioid ECs (EECs) according to different grades (G) and from serous subtypes (SECs), we verified the utility of miR-497-5p. In ECs, we observed downregulated miR-497-5p levels that were significantly decreased in SECs, clear cell carcinomas (CCCs), and carcinosarcomas (CaSas) compared to EECs, thereby distinguishing EEC from SEC and rare EC subtypes. Significantly reduced miR-497-5p expression was found in high-grade ECs (EEC G3, SEC, CaSa, and CCC) compared to low-grade carcinomas (EEC G1 and mucinous carcinoma) and ECs classified as being in advanced FIGO (International Federation of Gynecology and Obstetrics) stages, that is, with loco-regional and distant spread compared to cancers located only in the uterus. Based on immunohistochemical features, lower miR-497-5p levels were observed in hormone-receptor-negative, p53-positive, and highly Ki-67-expressing ECs. Using a machine learning method, we showed that consideration of miR-497-5p expression, in addition to the traditional clinical and histopathologic parameters, slightly improves the prediction accuracy of EC diagnosis. Our results demonstrate that changes in miR-497-5p expression influence endometrial tumorigenesis and its evaluation may contribute to more precise diagnoses.
目前基于临床病理因素的子宫内膜癌（EC）诊断、预后和治疗指南存在诸多不足；因此，我们研究了 miRNA 表达谱对于区分不同 EC 亚型的相关性。在之前预测的 miRNA 中，miR-497-5p 可用于区分不同分级（G）的子宫内膜样型 EC（EEC）和浆液型亚型（SECs）。我们验证了 miR-497-5p 的实用性。在 EC 中，我们观察到 miR-497-5p 的表达水平下调，与 EEC 相比，SECs、透明细胞癌（CCCs）和癌肉瘤（CaSas）中 miR-497-5p 的水平显著降低，从而将 EEC 与 SEC 和罕见的 EC 亚型区分开来。与低分级癌（EEC G1 和黏液癌）和国际妇产科联合会（FIGO）分期较低的 EC 相比，高级别 EC（EEC G3、SEC、CaSa 和 CCC）中 miR-497-5p 的表达显著降低，FIGO 分期表示具有局部和远处转移，而不是仅位于子宫内的癌症。基于免疫组织化学特征，我们观察到激素受体阴性、p53 阳性和高 Ki-67 表达的 EC 中 miR-497-5p 水平较低。使用机器学习方法，我们表明除了传统的临床和组织病理学参数外，考虑 miR-497-5p 的表达可略微提高 EC 诊断的预测准确性。我们的结果表明，miR-497-5p 表达的变化影响子宫内膜肿瘤的发生，其评估可能有助于更精确的诊断。
