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.
Highlights
Endometrial cancer is the fourth most often diagnosed malignancy in the female population worldwide
Three methods including differentially expressed miRNAs (DEmiRs), weighted gene co-expression network analysis (WGCNA), and decision tree algorithms were performed between Lymph node metastasis (LNM)-positive group and LNM-negative group to screen candidate target miRNAs
The performance of the miRNA signature was evaluated by receiver operating characteristic curve (ROC) and decision curve analysis (DCA)
Summary
Endometrial cancer is the fourth most often diagnosed malignancy in the female population worldwide. Endometrial endometrioid carcinoma (EEC) is the most common histological type of endometrial cancer (Creasman et al, 2006). Lymph node metastasis (LNM) is a key determinant of the prognosis and treatment of EEC. Lymph node evaluation is critical for diagnosis and further adjuvant therapy. Lymphadenectomy used to be the routine therapy for EEC and was critical for surgical staging. A more selective lymphadenectomy is applied, and new noninvasive ways to evaluate lymph node status before surgery need to be explored. Lymph node metastasis (LNM) is an important prognostic factor in endometrial cancer. The aim of this study was to construct a miRNA signature to predict LNM in endometrial endometrioid carcinoma (EEC)
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