One-third of cervical cancer patients are still diagnosed at advanced stages. The five-year survival rate is decreased in about 50% of advanced stage cervical cancer patients worldwide, and the clinical outcomes are remarkably varied and difficult to predict. One of the miRNAs known to be associated with cancer tumorigenesis is miR-944. However, the prognostic value of miR-944 in cervical cancer has not been fully investigated. The aim of this study was to analyze clinical significance and prognostic value of miR-944 in cervical cancer. The expression levels of miR-944 were detected using quantitative reverse transcription polymerase chain reaction in five types of cervical cancer cell lines and 116 formalin-fixed paraffin-embedded (FFPE) cervical tissues. The association between the expression levels of miR-944 and prognostic value was analyzed using the Kaplan-Meier analysis and Cox proportional hazards model. The expression levels of miR-944 in cervical cancer tissues were significantly higher compared with those in normal tissues (P < 0.0001). Moreover, the expression levels of miR-944 in cervical cancer cell lines and FFPE tissues with human papillomavirus (HPV) infection were significantly higher compared to those without HPV infection (P < 0.01 and P = 0.02). High miR-944 expression was also markedly associated with bulky tumor size (P = 0.026), advanced International Federation of Gynecology and Obstetrics (FIGO) stage (P = 0.042), and lymph node metastasis (P = 0.030). In particular, high miR-944 expression group showed shorter overall survival than the low miR-944 expression group in the advanced FIGO stage (84.4% vs. 44.4%, HR = 4.0, and P = 0.01). These results suggest that miR-944 may be used as a novel biomarker for improving prognosis and as a potential therapeutic target.

Liquid biopsy has been emerging for early screening and treatment monitoring at each cancer stage. However, the current blood-based diagnostic tools in breast cancer have not been sufficient to understand patient-derived molecular features of aggressive tumors individually. Herein, we aimed to develop a blood test for the early detection of breast cancer with cost-effective and high-throughput considerations in order to combat the challenges associated with precision oncology using mRNA-based tests. We prospectively evaluated 719 blood samples from 404 breast cancer patients and 315 healthy controls, and identified 10 mRNA transcripts whose expression is increased in the blood of breast cancer patients relative to healthy controls. Modeling of the tumor-associated circulating transcripts (TACTs) is performed by means of four different machine learning techniques (artificial neural network (ANN), decision tree (DT), logistic regression (LR), and support vector machine (SVM)). The ANN model had superior sensitivity (90.2%), specificity (80.0%), and accuracy (85.7%) compared with the other three models. Relative to the value of 90.2% achieved using the TACT assay on our test set, the sensitivity values of other conventional assays (mammogram, CEA, and CA 15-3) were comparable or much lower, at 89%, 7%, and 5%, respectively. The sensitivity, specificity, and accuracy of TACTs were appreciably consistent across the different breast cancer stages, suggesting the potential of the TACTs assay as an early diagnosis and prediction of poor outcomes. Our study potentially paves the way for a simple and accurate diagnostic and prognostic tool for liquid biopsy.