Keywordsdeubiquitylase, hepatocellular carcinoma metastasis, hypoxia inducible factor, OTUD6B, von Hippel-Lindau

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Bone homeostasis is maintained by the balance between osteoblastic bone formation and osteoclastic bone resorption. Dysregulation of this process leads to multiple diseases, including osteoporosis. However, the underlying molecular mechanisms are not fully understood. Here, we show that the global and conditional osteoblast knockout of a deubiquitinase Otub1 result in low bone mass and poor bone strength due to defects in osteogenic differentiation and mineralization. Mechanistically, the stability of FGFR2, a crucial regulator of osteogenesis, is maintained by OTUB1. OTUB1 attenuates the E3 ligase SMURF1-mediated FGFR2 ubiquitination by inhibiting SMURF1’s E2 binding. In the absence of OTUB1, FGFR2 is ubiquitinated excessively by SMURF1, followed by lysosomal degradation. Consistently, adeno-associated virus serotype 9 (AAV9)-delivered FGFR2 in knee joints rescued the bone mass loss in osteoblast-specific Otub1-deleted mice. Moreover, Otub1 mRNA level was significantly downregulated in bones from osteoporotic mice, and restoring OTUB1 levels through an AAV9-delivered system in ovariectomy-induced osteoporotic mice attenuated osteopenia. Taken together, our results suggest that OTUB1 positively regulates osteogenic differentiation and mineralization in bone homeostasis by controlling FGFR2 stability, which provides an optical therapeutic strategy to alleviate osteoporosis.

ObjectiveThe purpose of this research was to develop a radiomics model that combines several clinical features for preoperative prediction of the pathological grade of bladder cancer (BCa) using non-enhanced computed tomography (NE-CT) scanning images.Materials and methodsThe computed tomography (CT), clinical, and pathological data of 105 BCa patients attending our hospital between January 2017 and August 2022 were retrospectively evaluated. The study cohort comprised 44 low-grade BCa and 61 high-grade BCa patients. The subjects were randomly divided into training (n = 73) and validation (n = 32) cohorts at a ratio of 7:3. Radiomic features were extracted from NE-CT images. A total of 15 representative features were screened using the least absolute shrinkage and selection operator (LASSO) algorithm. Based on these characteristics, six models for predicting BCa pathological grade, including support vector machine (SVM), k-nearest neighbor (KNN), gradient boosting decision tree (GBDT), logical regression (LR), random forest (RF), and extreme gradient boosting (XGBOOST) were constructed. The model combining radiomics score and clinical factors was further constructed. The predictive performance of the models was evaluated based on the area under the receiver operating characteristic (ROC) curve, DeLong test, and decision curve analysis (DCA).ResultsThe selected clinical factors for the model included age and tumor size. LASSO regression analysis identified 15 features most linked to BCa grade, which were included in the machine learning model. The SVM analysis revealed that the highest AUC of the model was 0.842. A nomogram combining the radiomics signature and selected clinical variables showed accurate prediction of the pathological grade of BCa preoperatively. The AUC of the training cohort was 0.919, whereas that of the validation cohort was 0.854. The clinical value of the combined radiomics nomogram was validated using calibration curve and DCA.ConclusionMachine learning models combining CT semantic features and the selected clinical variables can accurately predict the pathological grade of BCa, offering a non-invasive and accurate approach for predicting the pathological grade of BCa preoperatively.