Background: Circular RNAs (circRNAs) represent a class of broad and diversified endogenous RNAs that regulate gene expressions in eukaryotes. Hsa_circ_006675 has been proven as an important circRNA molecule in nasopharyngeal carcinoma (NPC), however, its function still remains elusive. This study aims to discuss the biofunctions of hsa_circ_0066755 in NPC. Methods: We detected the expression levels of hsa_circ_0066755 in NPC patients by quantitative real-time polymerase chain reaction (qRT-PCR), and the corresponding ROC curves were plotted. Functional experiments including CCK-8, colony formation, Transwell assay and Xenograft experiment were conducted. Bioinformatics analysis was performed to seek miRNAs which might have binding sites with hsa_circ_0066755 . Luciferase reporter assays were finally carried out to verify the binding sites. Results: We found significant increases of hsa_circ_0066755 in the plasma and tissues of the patients. Moreover, its levels were positively correlated with clinical staging ( P =0.019). The receiver operating characteristic (ROC) analysis showed that the area under the curves (AUCs) of tissue and plasma hsa_circ_0066755 for distinguishing NPC from non-cancerous controls were 0.8537 and 0.9044, respectively. Both tissue and plasma hsa_circ_0066755 testing presented a comparable diagnostic accuracy to the magnetic resonance imaging (MRI). Our in-vitro experiment showed that the overexpression of hsa_circ_0066755 facilitated the growth, proliferation, clone formation, invasion and migration of CNE-1 NPC cells, while its down-regulation showed completely opposite effects. The xenograft experiment showed that exogenous hsa_circ_0066755 could significantly enhance the in-vivo tumorigenic ability of CNE-1 cells. Rescue assay further confirmed hsa_circ_0066755 as a tumor facilitator by sponging miR-651 . Conclusions: Collectively, this study reported for the first time that hsa_circ_0066755 played a role of oncogene in NPC and could be used as an effective diagnostic marker for NPC, and that hsa_circ_0066755 / miR-651 axis also involved in the progression of NPC.

Background Distinguishing parotid pleomorphic adenoma (PPA) from parotid adenolymphoma (PA) is important for precision treatment, but there is a lack of readily available diagnostic methods. In this study, we aimed to explore the diagnostic value of radiomic signatures based on magnetic resonance imaging (MRI) for PPA and PA. Methods The clinical characteristic and imaging data were retrospectively collected from 252 cases (126 cases in the training cohort and 76 patients in the validation cohort) in this study. Radiomic features were extracted from MRI scans, including T1-weighted imaging (T1WI) sequences and T2-weighted imaging (T2WI) sequences. The radiomic features from three sequences (T1WI, T2WI and T1WI combined with T2WI) were selected using univariate analysis, LASSO correlation and Spearman correlation. Then, we built six quantitative radiomic models using the selected features through two machine learning methods (multivariable logistic regression, MLR, and support vector machine, SVM). The performances of the six radiomic models were assessed and the diagnostic efficacies of the ideal T1-2WI radiomic model and the clinical model were compared. Results The T1-2WI radiomic model using MLR showed optimal discriminatory ability (accuracy = 0.87 and 0.86, F-1 score = 0.88 and 0.86, sensitivity = 0.90 and 0.88, specificity = 0.82 and 0.80, positive predictive value = 0.86 and 0.84, negative predictive value = 0.86 and 0.84 in the training and validation cohorts, respectively) and its calibration was observed to be good (p > 0.05). The area under the curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.67, p < 0.001) and validation (0.90 vs. 0.68, p = 0.001) cohorts. Conclusions The T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA.

Background: Distinguishing parotid pleomorphic adenoma (PPA) from parotid adenolymphoma (PA) is important for precision treatment, but there is a lack of readily available diagnostic methods. In this study, we aimed to explore the diagnostic value of radiomic signatures based on magnetic resonance imaging (MRI) for PPA and PA. Methods: The clinical characteristic and imaging data were retrospectively collected from 252 cases (126 cases in the training cohort and 76 patients in the validation cohort) in this study. Radiomic features were extracted from MRI scans, including T1-weighted imaging (T1WI) sequences and T2-weighted imaging (T2WI) sequences. The radiomic features from three sequences (T1WI, T2WI and T1WI combined with T2WI) were selected using univariate analysis, LASSO correlation and Spearman correlation. Then, we built six quantitative radiomic models using the selected features through two machine learning methods (multivariable logistic regression, MLR, and support vector machine, SVM). The performances of the six radiomic models were assessed and the diagnostic efficacies of the ideal T1-2WI radiomic model and the clinical model were compared.Results: The T1-2WI radiomic model using MLR showed optimal discriminatory ability (accuracy = 0.87 and 0.86, F-1 score = 0.88 and 0.86, sensitivity= 0.90 and 0.88, specificity=0.82 and 0.80, positive predictive value=0.86 and 0.84, negative predictive value=0.86 and 0.84 in the training and validation cohorts, respectively) and its calibration was observed to be good (p>0.05). The area under the curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.67, p=0.000) and validation (0.90 vs. 0.68, p=0.001) cohorts.Conclusions: The T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA.

The value of computed tomography pulmonary angiography (CTPA) for the diagnosis of right ventricular dysfunction (RVD) subsequent to acute pulmonary embolism (PE). The ultrasonic cardiography (UCG) was used to assess RVD, one of the diagnostic criteria of PE caused hemodynamic collapse. Seventy six patients with confirmed PE were divided into massive (52 cases) and non-massive PE group (24 cases). The diagnostic criteria assessed for the imminent RVD were: (1) the ratio of axial diameters of the right and left ventricular chambers (RVd/LVd) exceeding 1, or (2) the right ventricular end-diastolic diameter measuring >30 mm. The CTPA diagnosed RVD was positive in 36 and negative in 40 cases. The RVD assessed by UCG was positive in 31 and negative in 45 cases. In comparison to UCG, the CTPA results UCG exhibited 96.77 % sensitivity 96.77 % and 86.67 specificity. The evaluated values both of these techniques were found in good agreement by the kappa value (κ) of 0.81, P < 0.001. In 52 cases of massive PE, CTPA determined RVD was positive in 34, and negative in 18 cases. In comparison, UCG diagnosed RVD was positive in 31 and negative in 21 cases. The sensitivity and specificity of CTPA results compared to those of UCG were 91.18 and 85.71 %, respectively. The estimates obtained were in good agreement as indicated by 0.88 κ value and P < 0.001. Twenty four cases of non-massive PE were RVD negative when assessed by CTPA, UCG however showed two cases positive in this group. Compared to UCG, the specificity of CTPA in evaluating RVD was 100 %. In the massive PE group, the average estimate of RVd/LVd ratio was significantly higher than 1 as analyzed by the non-parametric Mann-Whitney test (P < 0.001). The CTPA and UCG results showed a good correlation in massive PE cases. However, in non-massive PE group, results from two techniques were not correlated. The CTPA can accurately and reliably diagnose the PE and ensuing by estimating changes in the anatomical parameters of right ventricle. Hence, it can allow prompt diagnosis and an appropriate treatment leading to an improved prognosis.