Background: MicroRNAs (miRs) have been shown to play important roles in various cancers and may be a reliable prognostic marker. However, its prognostic value in endometrial carcinoma (UCEC) needs to be further explored.

Objectives: The aim of this study was to create a miR-based signature to effectively predict the prognosis for patients with uterine corpus endometrial carcinoma (UCEC).

Methods: Using UCEC data set in TCGA, we identified differentially expressed miRs between UCEC and healthy endometrial tissues. The LASSO method was used to construct a miR-based signature prognosis index for predicting prognosis in the training cohort. The miR-based signature prognosis index was validated in an independent test cohort. MiRNet tool was applied to perform functional enrichment analysis of this miR-based signature.

Results: A total of 208 miRs were differentially expressed between UCEC and healthy endometrial tissues. Five miRs (miR-652, miR-3170, miR-195, miR-34a, and miR-934) were identified to generate a prognosis index (PI). The five-miR signature is a promising biomarker for predicting the 5-year-survival rate of UCEC with AUC = 0.730. The PI remained an independent prognostic factor adjusted by routine clinicopathologic factors. Using the PI, we could successfully identify the high-risk individuals, furthermore, it still worked in an independent test cohort. The five miRs involved in various pathways associated with cancer.

Conclusion: We proposed and validated a five-miR signature that could serve as an independent prognostic predictor of UCECs.

背景：MicroRNAs (miRs) 已被证明在各种癌症中发挥重要作用，并且可能是一个可靠的预后标志物。然而，其在子宫内膜癌（UCEC）中的预后价值需要进一步探讨。

目的：本研究的目的是创建基于 miR 的特征，以有效预测子宫体子宫内膜癌 (UCEC) 患者的预后。

方法：使用 TCGA 中的 UCEC 数据集，我们确定了 UCEC 和健康子宫内膜组织之间差异表达的 miR。LASSO 方法用于构建基于 miR 的特征预后指数，用于预测训练队列中的预后。基于 miR 的特征预后指数在一个独立的测试队列中得到验证。应用 MiRNet 工具对该基于 miR 的特征进行功能富集分析。

结果：UCEC与健康子宫内膜组织间共有208个miR差异表达。鉴定了五个 miR（miR-652、miR-3170、miR-195、miR-34a 和 miR-934）以生成预后指数（PI）。5-miR 特征是预测 UCEC 5 年生存率的有前途的生物标志物，AUC = 0.730。PI 仍然是通过常规临床病理因素调整的独立预后因素。使用 PI，我们可以成功识别高风险个体，此外，它仍然在独立的测试队列中起作用。这五个 miR 参与了与癌症相关的各种途径。

结论：我们提出并验证了可以作为 UCEC 独立预后预测因子的 5-miR 特征。