USP7 interacts with and destabilizes oncoprotein SET.

Oncoprotein SE translocation (SET) is frequently overexpressed in different types of tumors and correlated with poor prognosis of cancer patients. Targeting SET has been considered a promising strategy for cancer intervention. However, the mechanisms by which SET is regulated under cellular conditions are largely unknown. Here, by performing a tandem affinity purification-mass spectrometry (TAP-MS), we identify that the ubiquitin-specific protease 7 (USP7) forms a stable protein complex with SET in cancer cells. Further analyses reveal that the acidic domain of SET directly binds USP7 while both catalytic domain and ubiquitin-like (UBL) domains of USP7 are required for SET binding. Knockdown of USP7 has no effect on the mRNA level of SET. However, we surprisingly find that USP7 depletion leads to a dramatic elevation of SET protein levels, suggesting that USP7 plays a key role in destabilizing oncoprotein SET, possibly through an indirect mechanism. To our knowledge, our data report the first deubiquitinase (DUB) that physically associates with oncoprotein SET and imply an unexpected regulatory effect of USP7 on SET stability.

Oncoprotein SET-associated transcription factor ZBTB11 triggers lung cancer metastasis.

Metastasis is the major cause of lung cancer-related death, but the mechanisms governing lung tumor metastasis remain incompletely elucidated. SE translocation (SET) is overexpressed in lung tumors and correlates with unfavorable prognosis. Here we uncover SET-associated transcription factor, zinc finger and BTB domain-containing protein 11 (ZBTB11), as a prometastatic regulator in lung tumors. SET interacts and collaborates with ZBTB11 to promote lung cancer cell migration and invasion, primarily through SET-ZBTB11 complex-mediated transcriptional activation of matrix metalloproteinase-9 (MMP9). Additionally, by transcriptional repression of proline-rich Gla protein 2 (PRRG2), ZBTB11 links Yes-associated protein 1 (YAP1) activation to drive lung tumor metastasis independently of SET-ZBTB11 complex. Loss of ZBTB11 suppresses distal metastasis in a lung tumor mouse model. Overexpression of ZBTB11 is recapitulated in human metastatic lung tumors and correlates with diminished survival. Our study demonstrates ZBTB11 as a key metastatic regulator and reveals diverse mechanisms by which ZBTB11 modulates lung tumor metastasis.

Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning.

OBJECTIVES: To develop a dynamic nomogram containing radiomics signature and clinical features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and design an automatic image segmentation model to reduce labor and time costs. METHODS: Data from 217 medulloblastoma (MB) patients over the past 4 years were collected and separated into a training set and a test set. Intraclass correlation coefficient (ICC), random survival forest (RSF), and least absolute shrinkage and selection operator (LASSO) regression methods were employed to select variables in the training set. Univariate and multivariate Cox proportional hazard models, as well as Kaplan-Meier analysis, were utilized to determine the relationship among the radiomics signature, clinical features, and overall survival. A dynamic nomogram was developed. Additionally, a 3D-Unet deep learning model was used to train the automatic tumor delineation model. RESULTS: Higher Rad-scores were significantly associated with worse OS in both the training and validation sets (p < 0.001 and p = 0.047, respectively). The Cox model combined clinical and radiomics signatures ([IBS = 0.079], [C-index = 0.747, SE = 0.045]) outperformed either radiomics signatures alone ([IBS = 0.081], [C-index = 0.738, SE = 0.041]) or clinical features alone ([IBS = 0.085], [C-index = 0.565, SE = 0.041]). The segmentation model had mean Dice coefficients of 0.80, 0.82, and 0.78 in the training, validation, and test sets respectively. A deep learning-based tumor segmentation model was built with Dice coefficients of 0.8372, 0.8017, and 0.7673 on the training set, validation set, and test set, respectively. CONCLUSIONS: A combination of radiomics features and clinical characteristics enhances the accuracy of OS prediction in medulloblastoma patients. Additionally, building an MRI image automatic segmentation model reduces labor and time costs. CLINICAL RELEVANCE STATEMENT: A survival prognosis model based on radiomics and clinical characteristics could improve the accuracy of prognosis estimation for medulloblastoma patients, and an MRI-based automatic tumor segmentation model could reduce the cost of time. KEY POINTS: • A model that combines radiomics and clinical features can predict the survival prognosis of patients with medulloblastoma. • Online nomogram and image automatic segmentation model can help doctors better judge the prognosis of medulloblastoma and save working time. • The developed AI system can help doctors judge the prognosis of diseases and promote the development of precision medicine.

Coupled deglycosylation-ubiquitination cascade in regulating PD-1 degradation by MDM2.

Posttranslational modifications represent a key step in modulating programmed death-1 (PD-1) functions, but the underlying mechanisms remain incompletely defined. Here, we report crosstalk between deglycosylation and ubiquitination in regulating PD-1 stability. We show that the removal of N-linked glycosylation is a prerequisite for efficient PD-1 ubiquitination and degradation. Murine double minute 2 (MDM2) is identified as an E3 ligase of deglycosylated PD-1. In addition, the presence of MDM2 facilitates glycosylated PD-1 interaction with glycosidase NGLY1 and promotes subsequent NGLY1-catalyzed PD-1 deglycosylation. Functionally, we demonstrate that the absence of T cell-specific MDM2 accelerates tumor growth by primarily upregulating PD-1. By stimulating the p53-MDM2 axis, interferon-α (IFN-α) reduces PD-1 levels in T cells, which, in turn, exhibit a synergistic effect on tumor suppression by sensitizing anti-PD-1 immunotherapy. Our study reveals that MDM2 directs PD-1 degradation via a deglycosylation-ubiquitination coupled mechanism and sheds light on a promising strategy to boost cancer immunotherapy by targeting the T cell-specific MDM2-PD-1 regulatory axis.

The repression of oncoprotein SET by the tumor suppressor p53 reveals a p53-SET-PP2A feedback loop for cancer therapy.

The oncoprotein SET is frequently overexpressed in many types of tumors and contributes to malignant initiation and progression through multiple mechanisms, including the hijacking of the tumor suppressors p53 and PP2A. Targeting aberrant SET represents a promising strategy for cancer intervention. However, the mechanism by which endogenous SET is regulated in cancer cells remains largely unknown. Here, we identified the tumor suppressor p53 as a key regulator that transcriptionally repressed the expression of SET in both normal and cancer cells. In addition, p53 stimulated PP2A phosphatase activity via p53-mediated transcriptional repression of SET, whereby SET-mediated inhibition of PP2A was alleviated. Moreover, targeting the interaction between SET and PP2A catalytic subunit (PP2Ac) with FTY720 enhanced stress-induced p53 activation via PP2A-mediated dephosphorylation of p53 on threonine 55 (Thr55). Therefore, our findings uncovered a previously unknown p53-SET-PP2A regulatory feedback loop. To functionally potentiate this feedback loop, we designed a combined therapeutic strategy by simultaneously administrating a p53 activator and SET antagonist in cancer cells and observed a dramatic synergistic effect on tumor suppression. Our study reveals mechanistic insight into the regulation of the oncoprotein SET and raises a potential strategy for cancer therapy by stimulating the p53-SET-PP2A feedback loop.

Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma.

BACKGROUND: Medulloblastoma (MB) is a common central nervous system tumor in children with extensive heterogeneity and different prognoses. This study aimed to classify the Ki-67 index in MB with radiomic characteristics based on multi-parametric magnetic resonance imaging to guide treatment and assess the prognosis of patients. METHODS: Three sequences of T1W, CE-T1W, and T2W were used as test data. Two experienced radiologists manually segmented the tumors according to T2W images from 90 patients. The patients were divided into training and test sets at a ratio of 7:3, and 833 dimensional image features were extracted for each patient. Five models were trained using the feature set selected in three ways. Finally, the area under the curve (AUC) and accuracy (ACC) were used on the test set to evaluate the performance of the different models. RESULTS: A random forest (RF) model combining three sequence features achieved the best performance (ACC: 0.771, 95% CI: 0.727 to 0.816; AUC: 0.697, 95% CI: 0.614 to 0.78). The voting model that combined a RF and a support vector machine (SVM) had higher performance than the other models (ACC: 0.796, 95% CI: 0.76 to 0.833; AUC: 0.689, 95% CI: 0.615 to 0.763). The best prediction model that used only one sequence feature was voting in the T2W sequence (ACC: 0.736, 95% CI: 0.705 to 0.766; AUC: 0.636, 95% CI: 0.585 to 0.688). The ensemble model was better than the single training model, and a multi-sequence combination was better than a single sequence prediction. The multiple feature selection methods were better than a combination of the two methods. CONCLUSIONS: A model obtained by machine learning could help doctors predict the Ki-67 values of patients more efficiently to make targeted judgments for subsequent treatments.

Irradiation stent insertion for inoperable malignant biliary obstruction: a meta-analysis of randomized controlled trials.

The purpose of the study was to compare the relative clinical efficacies of irradiation stent (IRS) and conventional stent (CVS) insertions for the treatment of patients with malignant biliary obstruction (MBO). Pubmed, Embase, and Cochrane Library databases were searched for relevant randomized controlled trials (RCTs) from the date of inception through to August 2020. Data analysis was performed using RevMan v5.3. This meta-analysis included eight RCTs which included a total of 319 patients who had undergone IRS insertion, and 328 who had undergone CVS insertion. No significant differences in pooled Δ total bilirubin values (MD 0.34; P = 0.92), incident rates of cholangitis (P = 0.47), hemobilia (P = 0.60), or pancreatitis (P = 0.89) were detected between two groups. The rate of stent dysfunction was significantly lower in the IRS group compared to the CVS group (22.2% vs. 37.7%, P = 0.02). The pooled stent patency (P < 0.00001) and survival (P < 0.00001) were significantly longer in the IRS group compared to the CVS group. Significant heterogeneity was detected in the endpoints of rate of stent dysfunction (I2 = 52%; P = 0.08) and survival (I2 = 77%; P = 0.0005). Subgroup analysis was performed based on the different IRS types and showed significantly longer survival in the IRS group based on both types of IRS. Funnel plot analyses did not detect any evidence of publication bias. This meta-analysis included eight RCTs which included a total of 319 patients who had undergone IRS insertion, and 328 who had undergone CVS insertion. No significant differences in pooled Δ total bilirubin values (MD 0.34; P = 0.92), incident rates of cholangitis (P = 0.47), hemobilia (P = 0.60), or pancreatitis (P = 0.89) were detected between 2 groups. The rate of stent dysfunction was significantly lower in the IRS group compared to the CVS group (22.2% vs. 37.7%, P = 0.02). The pooled stent patency (P < 0.00001) and survival (P < 0.00001) were significantly longer in the IRS group compared to the CVS group. Significant heterogeneity was detected in the endpoints of rate of stent dysfunction (I2 = 52%; P = 0.08) and survival (I2 = 77%; P = 0.0005). Subgroup analysis was performed based on the different IRS types and showed significantly longer survival in the IRS group based on both types of IRS. Funnel plot analyses did not detect any evidence of publication bias. Our meta-analysis demonstrates that IRS insertion can prolong stent patency and the survival of patients with MBO compared to CVS insertion.