Maintenance of energy homeostasis is essential for cell survival. Here, we report that the ATP- and ubiquitin-independent REGγ-proteasome system plays a role in maintaining energy homeostasis and cell survival during energy starvation via repressing rDNA transcription, a major intracellular energy-consuming process. Mechanistically, REGγ-proteasome limits cellular rDNA transcription and energy consumption by targeting the rDNA transcription activator SirT7 for ubiquitin-independent degradation under normal conditions. Moreover, energy starvation induces an AMPK-directed SirT7 phosphorylation and subsequent REGγ-dependent SirT7 subcellular redistribution and degradation, thereby further reducing rDNA transcription to save energy to overcome cell death. Energy starvation is a promising strategy for cancer therapy. Our report also shows that REGγ knockdown markedly improves the anti-tumour activity of energy metabolism inhibitors in mice. Our results underscore a control mechanism for an ubiquitin-independent process in maintaining energy homeostasis and cell viability under starvation conditions, suggesting that REGγ-proteasome inhibition has a potential to provide tumour-starving benefits.
Objectives-This work aimed to investigate whether quantitative radiomics imaging features extracted from ultrasound (US) can noninvasively predict breast cancer (BC) metastasis to axillary lymph nodes (ALNs). Methods-Presurgical B-mode US data of 196 patients with BC were retrospectively studied. The cases were divided into the training and validation cohorts (n = 141 versus 55). The elastic net regression technique was used for selecting features and building a signature in the training cohort. A linear combination of the selected features weighted by their respective coefficients produced a radiomics signature for each individual. A radiomics nomogram was established based on the radiomics signature and US-reported ALN status. In a receiver operating characteristic curve analysis, areas under the curves (AUCs) were determined for assessing the accuracy of the prediction model in predicting ALN metastasis in both cohorts. The clinical value was assessed by a decision curve analysis. Results-In all, 843 radiomics features per case were obtained from expertdelineated lesions on US imaging in this study. Through radiomics feature selection, 21 features were selected to constitute the radiomics signature for predicting ALN metastasis. Area under the curve values of 0.778 and 0.725 were obtained in the training and validation cohorts, respectively, indicating moderate predictive ability. The radiomics nomogram comprising the radiomics signature and US-reported ALN status showed the best performance for ALN detection in the training cohort (AUC, 0.816) but moderate performance in the validation cohort (AUC, 0.759). The decision curve showed that both the radiomics signature and nomogram displayed good clinical utility. Conclusions-This pilot radiomics study provided a noninvasive method for predicting presurgical ALN metastasis status in BC.
To analyze the degree and pattern of influence of contrast-enhanced ultrasonography (CEUS) on the Bosniak classification system for complex renal cystic mass as compared with conventional ultrasonography (US). One hundred two consecutive patients with complex renal cystic masses were retrospectively analyzed. The diagnostic performance of the Conventional US and CEUS were evaluated separately for malignant and benign lesions. The diagnostic concordance rates were calculated according to pathologic diagnoses. ROC curve analysis determined the confidence in the diagnostic accuracy by calculating the area under each ROC curve. Compared to the Conventional US, septae number, wall and/or septae thickness, solid component and the Bosniak classification changed in 17 (16.7%), 39 (38.2%), 31 (30.4%), and 67 (65.7%) patients as compared with 0 (0.0%), 21 (20.6%), 31 (30.4%), and 37 (36.3%) of the treatment strategy that changed after CEUS respectively. The diagnostic performance of CEUS showed overall higher in terms of sensitivity (100.0 vs 97.2%); specificity (90.9 vs 62.1%); positive predictive value (PPV) (85.7 vs 58.3%); negative predictive value (NPV) (100.0 vs 97.6%); and the concordance with pathology (kappa = 0.876 vs 0.515). CEUS had a higher diagnostic confidence (P < .05) according to the area under the ROC curve (AUC = 0.968 vs 0.799).CEUS performed better than the Conventional US in the diagnosis of complex renal cystic mass, and it might be considered as the first tool to evaluate a complex cystic renal mass, especially for these Bosniak III masses displaying the presence of hemorrhage or infection.
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