High-risk human papillomavirus (HPV) type 16, which is responsible for greater than 50% of cervical cancer cases, is the most prevalent and lethal HPV type. However, the molecular mechanisms of cervical carcinogenesis remain elusive, particularly the early steps of HPV infection that may transform normal cervical epithelium into a pre-neoplastic state. Here, we report that a group of microRNAs (microRNAs) were aberrantly decreased in HPV16-positive normal cervical tissues, and these groups of microRNAs are further reduced in cervical carcinoma. Among these miRNAs, miR196a expression is the most reduced in HPV16-infected tissues. Interestingly, miR196a expression is low in HPV16-positive cervical cancer cell lines but high in HPV16-negative cervical cancer cell lines. Furthermore, we found that only HPV16 early gene E5 specifically down-regulated miRNA196a in the cervical cancer cell lines. In addition, HoxB8, a known miR196a target gene, is up-regulated in the HPV16 cervical carcinoma cell line but not in HPV18 cervical cancer cell lines. Various doses of miR196a affected cervical cancer cell proliferation and apoptosis. Altogether, these results suggested that HPV16 E5 specifically down-regulates miR196a upon infection of the human cervix and initiates the transformation of normal cervix cells to cervical carcinoma.

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COC166-9 is an ovarian cancer-specific monoclonal antibody, and COC166-9-based immunotherapy has been shown to possess killing effects against ovarian cancer cells in vitro and in vivo. However the antigen recognized by COC166-9 (COC166-9-Ag, CA166-9) has not been identified and the clinical significance of CA166-9 expression remains unknown. We found that CA166-9 was positive in 53.1% of ovarian cancer tissues. Expression of CA166-9 was strongly correlated with the cancer recurrence (P<0.001). Patients with positive CA166-9 had substantially shorter overall survival (P=0.026) and disease-free survival (P=0.002). CA166-9 was also shown to be an independent predictive factor for overall survival (HR=2.454, P=0.016) and disease-free survival (HR=2.331, P=0.021). We identified CA166-9 as human immunoglobulin γ-1 heavy chain constant region (IGHG1). Purified IGHG1 promoted proliferation, migration, and invasion of CA166-9-negative ovarian cancer HOC1A cells, whereas it had minimal effects on the phenotypes of CA166-9-positive ovarian cancer CAOV-3 cells. In addition, overexpression of IGHG1 enhanced migration of ovarian cancer cells. On the contrary, COC166-9 inhibited proliferation, migration, and invasion of CAOV-3 cells, but had no effects on HOC1A cells. Therefore, IGHG1 similarly to CA166-9, could play an important role in ovarian cancer development and may serve as a potential prognostic marker and a therapeutical target for ovarian cancer.

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Objective This study was performed to examine the value of computed tomography-based texture assessment for characterizing different types of ovarian neoplasms. Methods This retrospective study involved 225 patients with histopathologically confirmed ovarian tumors after surgical resection. Two different data sets of thick (5-mm) slices (during regular and portal venous phases) were analyzed. Raw data analysis, principal component analysis, linear discriminant analysis, and nonlinear discriminant analysis were performed to classify ovarian tumors. The radiologist’s misclassification rate was compared with the MaZda (texture analysis software) findings. The results were validated with the neural network classifier. Receiver operating characteristic curves were analyzed to determine the performances of different parameters. Results Nonlinear discriminant analysis had a lower misclassification rate than the other analyses. Thirty texture parameters significantly differed between the two groups. In the training set, WavEnLH_s-3 and WavEnHL_s-3 were the optimal texture features during the regular phase, while WavEnHH_s-4 and Kurtosis seemed to be the most discriminative features during the portal venous phase. In the validation test, benign versus malignant tumors and benign versus borderline lesions were well-distinguished. Conclusions Computed tomography-based texture features provide a useful imaging signature that may assist in differentiating benign, borderline, and early-stage ovarian cancer.

Background During the development of cervical cancer, HPV infection causes a series of changes in transcription factors and microRNAs. But their relationships with pathogenic processes are not clear. Methods Base on previous study, to analyse the relationship among HPV16 infection and the related transcription factors, related miRNAs, so as to further understand the molecular mechanism of HPV16 infection to cervical cancer, around the HPV16 related miRNAs we have reported, the methods of bioinformatics prediction, histology, cell model in vitro and molecular interaction were used for prediction and validation respectively Results The results showed that NF-κB family members(c-Rel, p65 and p50) were identified as main HPV16rmiR-transcription factors. They have different expressive characteristics in cervical lesions and play tumorigenesis or progression roles in different periods of HPV16 infection. c-Rel, p65 and p50 act as mediators which link the HPV16 E5 and HPV16 related miRNAs. Among them, c-Rel affects the occurrence and progression of cervical cancer during whole HPV16 infection stage through miR133a-3p–modulated mir-379-369 cluster with a positive feedback way which targeted c-Rel itself and its positive regulator AKT3. Conclusion So in the course of HPV16 infection, the E5, c-Rel, and miR-133a-3p form a positive feedback system which aim at mir-379-369 cluster for the whole process from HPV16 infection to cervical cancer.

Background: This study aimed to investigate the expression of regeneration-related genes in canine urine during bladder repair. Materials & methods: Canine urine samples were collected after partial cystectomy. Regenerative mRNA of hypoxia-inducible factor (HIF), vascular endothelial growth factor (VEGF), key stem cell transcription factors and cholinergic signals were detected. Results: HIF-1α, VEGF, CD44, IL-6 and prominin-1 expression in canine urine after partial cystectomy exhibited two similar peaks at ∼2 weeks. HIF-1α and VEGF expression were higher in the afternoon than the morning. The expression of key stem cell transcription factors and cholinergic signals also exhibited a rhythm along with bladder healing. Conclusions: The expression of HIF-1α, VEGF, key stem cell transcription factors and cholinergic signals exhibited a time curve distribution during canine bladder healing. The expression trend of some regenerative genes was similar during bladder healing, and a cooperative effect may exist.

Background To explore and evaluate value a preoperative diagnosis model with contrast-enhanced computed tomography (CECT) imaging-based radiomics analysis in differentiating benign ovarian tumors (BeOTs), borderline ovarian tumors (BOTs), and early-stage malignant ovarian tumors (eMOTs). Results The retrospective research was conducted with pathologically confirmed 258 ovarian tumors patients from January 2014 to February 2021. All patients underwent preoperative CECT examination. The patients were randomly allocated to a training cohort (n = 198) and a test cohort (n = 60). A summary of 4238 radiomic features were extracted per patient. By providing a 3D characterization of the regions of interest (ROI) with ITK SNAP software at the maximum level of enhanced CT image, radiomic features were extracted from the ROI with an in-house software written in Python. The Wilcoxon–Mann–Whitney (WMW) test, least absolute shrinkage and selection operator logistic regression (LASSO-LR) and support vector machine (SVM) were employed to select the radiomic features. Five machine learning (ML) algorithms were applied to construct three-class diagnostic models for characterizing ovarian tumors taking the selected radiomic features parameters. Leave-one-out cross-validation (LOOCV) that estimated performance in an ‘independent’ dataset was implemented to evaluate the performance of the radiomics models in the training cohort. An independent dataset, that is the test cohort, was used to verify the generalization ability of the radiomics models. The receiver operating characteristics (ROC) was used to evaluate diagnostic performance of radiomics model. Global diagnostic performance of five models were evaluated by average area under the ROC curve (AUC). Conclusion The average ROC indicated that random forest (RF) diagnostic model in training cohort demonstrated the best diagnostic performance (micro average AUC, 0.98; macro average AUC, 0.99), which was then confirmed with by internal cross-validation (LOOCV) (micro average AUC, 0.89; macro average AUC, 0.88) and external validation (test cohort) (micro average AUC, 0.81; macro average AUC, 0.79). Our proposed CECT image-based radiomics diagnostic models may effectively assist in preoperatively differentiating BeOTs, BOTs, and eMOTs.