Background: Endometrial cancer is the most common malignancy of the female genital tract worldwide, and endometrial endometrioid adenocarcinoma (EEC) is the major histological type of endometrial cancer. There is a great need for better markers with high sensitivity and specificity to permit early diagnosis and proper management of EEC. The aim of our study is to identify a miRNA classifier within plasma as a noninvasive biomarker for EEC diagnosis.
Methods: This study was a retrospective case-control analysis which contained two independent cohorts including 93 participants. First, we screened 375 miRNAs in 29 plasma samples. 9 of the miRNAs were selected to be evaluated their expression by quantitative reverse-transcriptase polymerase chain reaction. A stepwise logistic regression model was then used to establish a new classifier in the validation cohort. Area under the receiver operating characteristic curve was used to evaluate the diagnostic accuracy. Co-expression analysis was used to verify the independence of results.
Results: miR-15b, -27a, and -223 were found to be differentially expressed in the EEC plasma between the two cohorts and had few connections with other miRNAs. The areas under the curve (AUC) were 0.768, 0.813, and 0.768 for miR-15b, -27a, and 223, respectively. miR-27a and CA125 can be combined as a potential non-invasive biomarker for detecting EEC, with the AUC of 0.894.
Conclusion: Our study demonstrated three miRNAs, including miR-15b, -27a, and -233 have a good clinical value in EEC diagnosis. The classifier, including miR-27a and CA125, demonstrated a high accuracy in the diagnosis of EEC and might serve as a novel non-invasive biomarker in the future.