A Tumor Suppressive Role of CYLD as a Novel Potential DUB of Aurora B in Cervical Cancer.

Background: Cervical cancer is a common leading cause of cancer related to women death worldwide. Cylindromatosis (CYLD) is known as an important tumor suppressor in various human cancers, and a deubiquitination enzyme (DUB) as well. Previously, we identified Skp2 as an E3 ligase of Aurora B ubiquitination, but the DUB of Aurora B still remains unknown. Methods: Aurora B ubiquitination site is identified through in vivo ubiquitination assay. Activity of Aurora B and CENPA was detected by immunoblotting (IB) and immunofluorescence (IF) assay. Protein-to-protein interaction was investigated by immunoprecipitation (IP). Cell chromosome dynamics was monitored by live-cell time-lapse Imaging. Cancer cell proliferation, colony formation, apoptosis, and cell invasion and migration assays were also performed. Protein level was checked by immunohistochemical (IHC) staining in clinical cervical cancer samples. Results: We identified Lysine 115 (K115) as the main Aurora B ubiquitination site for Skp2. We could also detect an interaction of Aurora B with the DUB CYLD. We found that CYLD promoted deubiquitination of Aurora B, and regulated Aurora B activity and function as well. Compared with control, we found it took more time for the cells to finish cell mitosis with CYLD over-expression. Furthermore, we found that CYLD deficiency promoted cervical cancer cell proliferation, colony formation, cell migration and invasion, and inhibited apoptosis instead, whereas it is just opposite with CYLD over-expression. In clinical cervical cancer samples, we showed a negative correlation of CYLD expression with Aurora B activation and histological cancer cell invasion. Furthermore, there was less CYLD abundance and higher Aurora B activity in advanced cancer samples compared with early stage. Conclusions: Our findings uncover CYLD as a novel potential DUB of Aurora B, which inhibits Aurora B activation and its subsequent function in cell mitosis, and also provide more evidence for its tumor suppressor function in cervical cancer.

Initial surgical versus non-surgical treatments for advanced hypopharyngeal cancer: A meta-analysis with trial sequential analysis.

BACKGROUND: There is no consensus on whether initial surgical or non-surgical treatments should be the standard treatment pattern for advanced hypopharyngeal cancer. The aim of the meta-analysis was systematically and quantitatively compare the relative efficacy between initial surgical and non-surgical therapies in patients with advanced-stage hypopharyngeal carcinoma. METHODS: A comprehensive search was performed in PubMed, the ISI Web of Knowledge, the Cochrane Library, and Embase databases from inception to April 10, 2019. Citation screening, data abstraction and quality assessment were performed in duplicate. Meta-analysis with trial sequential analysis (TSA) was used to assess the primary and secondary outcomes. Besides, we used the Grading of Recommendations Assessment Development and Evaluation (GRADE) to evaluate the certainty of the body of evidence. RESULTS: A total of 17 trials was appraised with 2539 patients that complied with inclusion and exclusion criterion. Pooled analyses indicated patients receiving primary surgical and non-surgical therapy did not significantly differ in overall survival (OS) (relative risk [RR] = 1.04, 95% confidence interval [CI] = 0.94 to 1.15), with TSA inconclusive. Additionally, patients treated with primary surgical experienced an increased disease free survival (DFS) probability compared with non-surgical treatment (RR 1.20, 95% CI = 1.05 to1.37), while TSA is inconclusive. Notably, non-surgical management did have a beneficial efficacy on larynx preservation (RR 0.48, 95% CI = 0.33 to 0.70), and TSA also provided conclusive evidence. GRADE indicated the level of evidence was low or very low for primary or secondary outcomes. CONCLUSION: The results of our meta-analysis indicated when compared to surgical treatments, non-surgical therapy for patients with advanced hypopharyngeal carcinoma appears to have equivalent efficacy, and it offers an opportunity to preserve laryngeal function. Due to inconclusive evidence by TSA, further investigation with large randomized clinical trials (RCTs) using modern approaches should be undertaken to verify the results of this meta-analysis. TRIAL REGISTRATION: PROSPERO registration number: CRD42018118563. Registered on December 19, 2018.

15-lncRNA-Based Classifier-Clinicopathologic Nomogram Improves the Prediction of Recurrence in Patients with Hepatocellular Carcinoma.

BACKGROUND: Our study aims to develop a lncRNA-based classifier and a nomogram incorporating the genomic signature and clinicopathologic factors to help to improve the accuracy of recurrence prediction for hepatocellular carcinoma (HCC) patients. METHODS: The lncRNA profiling data of 374 HCC patients and 50 normal healthy controls were downloaded from The Cancer Genome Atlas (TCGA). Using univariable Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a 15-lncRNA-based classifier and compared our classifier to the existing six-lncRNA signature. Besides, a nomogram incorporating the genomic classifier and clinicopathologic factors was also developed. The predictive accuracy and discriminative ability of the genomic-clinicopathologic nomogram were determined by a concordance index (C-index) and calibration curve and were compared with the TNM staging system by the C-index and receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate the clinical value of our nomogram. RESULTS: Fifteen relapse-free survival (RFS-) related lncRNAs were identified, and the classifier, consisting of the identified 15 lncRNAs, could effectively classify patients into the high-risk and low-risk subgroups. The prediction accuracy of the 15-lncRNA-based classifier for predicting 2-year and 5-year RFS was 0.791 and 0.834 in the training set and 0.684 and 0.747 in the validation set, respectively, which was better than the existing six-lncRNA signature. Moreover, the AUC of genomic-clinicopathologic nomogram in predicting RFS were 0.837 in the training set and 0.753 in the validation set, and the C-index of the genomic-clinicopathologic nomogram was 0.78 (0.72-0.83) in the training set and 0.71 (0.65-0.76) in the validation set, which was better than the traditional TNM stage and 15-lncRNA-based classifier. The decision curve analysis further demonstrated that our nomogram had a larger net benefit than the TNM stage and 15-lncRNA-based classifier. The results were confirmed externally. CONCLUSION: Compared to the TNM stage, the 15-lncRNAs-based classifier-clinicopathologic nomogram is a more effective and valuable tool to identify HCC recurrence and may aid in clinical decision-making.

An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma.

Long non-coding RNAs (lncRNAs) which have little or no protein-coding capacity, due to their potential roles in the cancer disease, caught a particular interest. Our study aims to develop an lncRNAs-based classifier and a nomogram incorporating the lncRNAs classifier and clinicopathologic factors to help to improve the accuracy of recurrence prediction for head and neck squamous cell carcinoma (HNSCC) patients. The HNSCC lncRNAs profiling data and the corresponding clinicopathologic information were downloaded from TANRIC database and cBioPortal. Using univariable Cox regression and Least absolute shrinkage and selection operator (LASSO) analysis, we developed 15-lncRNAs-based classifier related to recurrence. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate clinical value of our nomogram. Consequently, fifteen recurrence-free survival (RFS) -related lncRNAs were identified, and the classifier consisting of the established 15 lncRNAs could effectively divide patients into high-risk and low-risk subgroup. The prediction ability of the 15-lncRNAs-based classifier for predicting 3- year and 5-year RFS were 0.833 and 0.771. Independent factors derived from multivariable analysis to predict recurrence were number of positive LNs, margin status, mutation count and lncRNAs classifier, which were all embedded into the nomogram. The calibration curve for the recurrence probability showed that the predictions based on the nomogram were in good coincide with practical observations. The C-index of the nomogram was 0.76 (0.72-0.79), and the area under curve (AUC) of nomogram in predicting RFS was 0.809, which were significantly higher than traditional TNM stage and 15-lncRNAs-based classifier. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage and 15-lncRNAs-based classifier. The results were confirmed externally. In summary, a visually inclusive nomogram for patients with HNSCC, comprising genomic and clinicopathologic variables, generates more accurate prediction of the recurrence probability when compared TNM stage alone, but more additional data remains needed before being used in clinical practice.

Differentiation of Small Hepatocellular Carcinoma From Dysplastic Nodules in Cirrhotic Liver: Texture Analysis Based on MRI Improved Performance in Comparison Over Gadoxetic Acid-Enhanced MR and Diffusion-Weighted Imaging.

Background: Accurate characterization of small (3 cm) hepatocellular carcinoma (sHCC) and dysplastic nodules (DNs) in cirrhotic liver is challenging. We aimed to investigate whether texture analysis (TA) based on T2-weighted images (T2WI) is superior to qualitative diagnosis using gadoxetic acid-enhanced MR imaging (Gd-EOB-MRI) and diffusion-weighted imaging (DWI) for distinguishing sHCC from DNs in cirrhosis. Materials and methods: Sixty-eight patients with 73 liver nodules (46 HCCs, 27 DNs) pathologically confirmed by operation were included. For imaging diagnosis, three sets of images were reviewed by two experienced radiologists in consensus: a Gd-EOB-MRI set, a DWI set, and a combined set (combination of Gd-EOB-MRI and DWI). For TA, 279 texture features resulting from T2WI were extracted for each lesion. The performance of each approach was evaluated by a receiver operating characteristic analysis. The area under the receiver operating characteristic curve (A z), sensitivity, specificity, and accuracy were determined. Results: The performance of TA (A z = 0.96) was significantly higher than that of imaging diagnosis using Gd-EOB-MRI set (A z = 0.86) or DWI set (A z = 0.80) alone in differentiation of sHCC from DNs (P = 0.008 and 0.025, respectively). The combination of Gd-EOB-MRI and DWI showed a greater sensitivity (95.6%) but reduced specificity (66.7%). The specificity of TA (92.6%) was significantly higher than that of the combined set (P < 0.001), but no significant difference was observed in sensitivity (97.8 vs. 95.6%, P = 0.559). Conclusion: TA-based T2WI showed a better classification performance than that of qualitative diagnosis using Gd-EOB-MRI and DW imaging in differentiation of sHCCs from DNs in cirrhotic liver. TA-based MRI may become a potential imaging biomarker for the early differentiation HCCs from DNs in cirrhosis.