Identification of a MicroRNA Signature Associated With Lymph Node Metastasis in Endometrial Endometrioid Cancer

Abstract

BackgroundLymph node metastasis (LNM) is an important prognostic factor in endometrial cancer. Anomalous microRNAs (miRNAs) are associated with cell functions and are becoming a powerful tool to characterize malignant transformation and metastasis. The aim of this study was to construct a miRNA signature to predict LNM in endometrial endometrioid carcinoma (EEC).MethodCandidate target miRNAs related to LNM in EEC were screened by three methods including differentially expressed miRNAs (DEmiRs), weighted gene co-expression network analysis (WGCNA), and decision tree algorithms. Samples were randomly divided into the training and validation cohorts. A miRNA signature was built using a logistic regression model and was evaluated by the area under the curve (AUC) of receiver operating characteristic curve (ROC) and decision curve analysis (DCA). We also conducted pathway enrichment analysis and miRNA–gene regulatory network to look for potential genes and pathways engaged in LNM progression. Survival analysis was performed, and the miRNAs were tested whether they expressed differently in another independent GEO database.ResultThirty-one candidate miRNAs were screened and a final 15-miRNA signature was constructed by logistic regression. The model showed good calibration in the training and validation cohorts, with AUC of 0.824 (95% CI, 0.739–0.912) and 0.821 (95% CI, 0.691–0.925), respectively. The DCA demonstrated the miRNA signature was clinically useful. Hub miRNAs in signature seemed to contribute to EEC progression via mitotic cell cycle, cellular protein modification process, and molecular function. MiR-34c was statistically significant in survival that a higher expression of miR-34c indicated a higher survival time. MiR-34c-3p, miR-34c-5p, and miR-34b-5p were expressed differentially in GSE75968.ConclusionThe miRNA signature could work as a noninvasive method to detect LNM in EEC with a high prediction accuracy. In addition, miR-34c cluster may be a key biomarker referring LNM in endometrial cancer.

Can miRNAs be useful biomarkers in improving prognostic stratification in endometrial cancer patients? An update review

“…60 These results are intriguing as ascribe a role to the miR-34 family in the EC landscape. Recently, its involvement has been also supported by additional evidences 61 model and the molecular classification. 64 This aspect reflects the prognostic ability of this model and paves the way for a further ment of the current TCGA classification scheme.…”

“… 60 These results are intriguing as ascribe a role to the miR‐34 family in the EC landscape. Recently, its involvement has been also supported by additional evidences 61 ; Fu et al analyzed the TCGA—UCEC (Uterine Corpus Endometrial Carcinoma) database with the aim to construct a miRNA signature able to predict LNM. The authors retrieved the data of 324 patients and identified 113 miRNAs differently expressed between the two sets of patients (ie, with and without LNM).…”

The Role of miRNAs in the Development, Proliferation, and Progression of Endometrial Cancer

“…Table 3 summarizes the above data. [128] miR-107-5p [68] miR-429 [129,130] miR-501 [131] miR-576-5p [132] Downregulation miR-24b-5p [133] miR-26a-5p [82] miR-34a [65,66] miR-34b-5p [134] miR-34c-3p [134] miR-34c-5p [134] miR-148b [71] miR-204-5p [126] miR-505 [135] 2.2.5. The Impact of miRNA Changes on Survival and Recurrence in Patients with Endometrial Cancer Mortality related to endometrial cancer continues to increase [136].…”