Department of Clinical Laboratory, Wenzhou People's Hospital, The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou, China.
Department of Cardiology, Institute of Translational Medicine, Baotou Central Hospital, Baotou, China.
J Cell Mol Med. 2020 Apr;24(8):4533-4546. doi: 10.1111/jcmm.15111. Epub 2020 Mar 9.
As endometrial cancer (EC) is a major threat to female health worldwide, the ability to provide an accurate diagnosis and prognosis of EC is promising to improve its treatment guidance. Since the discovery of miRNAs, it has been realized that miRNAs are associated with every cell function, including malignant transformation and metastasis. This study aimed to explore diagnostic and prognostic miRNA markers of EC. In this study, differential analysis and machine learning were performed, followed by correlation analysis of miRNA-mRNA based on the miRNA and mRNA expression data. Nine miRNAs were identified as diagnostic markers, and a diagnostic classifier was established to distinguish between EC and normal endometrium tissue with overall correct rates >95%. Five specific prognostic miRNA markers were selected to construct a prognostic model, which was confirmed more effective in identifying EC patients at high risk of mortality compared with the FIGO staging system. This study demonstrates that the expression patterns of miRNAs may hold promise for becoming diagnostic and prognostic biomarkers and novel therapeutic targets for EC.
由于子宫内膜癌(EC)是全球范围内女性健康的主要威胁,因此提供 EC 的准确诊断和预后的能力有望改善其治疗指导。自 miRNA 被发现以来,人们已经意识到 miRNA 与包括恶性转化和转移在内的每个细胞功能都有关联。本研究旨在探索 EC 的诊断和预后 miRNA 标志物。在本研究中,进行了差异分析和机器学习,然后基于 miRNA 和 mRNA 表达数据进行了 miRNA-mRNA 的相关性分析。确定了 9 个 miRNA 作为诊断标志物,并建立了一个诊断分类器,用于区分 EC 和正常子宫内膜组织,总体准确率>95%。选择了 5 个特定的预后 miRNA 标志物来构建预后模型,与 FIGO 分期系统相比,该模型在识别 EC 患者死亡风险较高方面更有效。本研究表明,miRNA 的表达模式可能有望成为 EC 的诊断和预后生物标志物以及新的治疗靶点。