To date, the molecular mechanism underlying constitutive signal transducer and activator of transcription 3 (STAT3) activation in gliomas is largely unclear. In this study, we report that Smad6 is overexpressed in nuclei of glioma cells, which correlates with poor patient survival and regulates STAT3 activity via negatively regulating the Protein Inhibitors of Activated STAT3 (PIAS3). Mechanically, Smad6 interacts directly with PIAS3, and this interaction is mediated through the Mad homology 2 (MH2) domain of Smad6 and the Ring domain of PIAS3. Smad6 recruits Smurf1 to facilitate PIAS3 ubiquitination and degradation, which also depends on the MH2 domain and the PY motif of Smad6. Consequently, Smad6 reduces PIAS3-mediated STAT3 inhibition and promotes glioma cell growth and stem-like cell initiation. Moreover, the Smad6 MH2 transducible protein restores PIAS3 expression and subsequently reduces gliomagenesis. Collectively, we conclude that nuclear-Smad6 enhances glioma development by inducing PIAS3 degradation and subsequent STAT3 activity upregulation.

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Background Gleason score (GS) is a histologic prognostic factor and the basis of treatment decision‐making for prostate cancer (PCa). Treatment regimens between lower‐grade (GS ≤7) and high‐grade (GS >7) PCa differ largely and have great effects on cancer progression. Purpose To investigate the use of different sequences in biparametric MRI (bpMRI) of the prostate gland for noninvasively distinguishing high‐grade PCa. Study Type Retrospective. Population In all, 489 patients (training cohort: N = 326; test cohort: N = 163) with PCa between June 2008 and January 2018. Field Strength/Sequence 3.0T, pelvic phased‐array coils, bpMRI including T2‐weighted imaging (T2WI) and diffusion‐weighted imaging (DWI); apparent diffusion coefficient map extracted from DWI. Assessment The whole prostate gland was delineated. Radiomic features were extracted and selected using the Kruskal–Wallis test, the minimum redundancy‐maximum relevance, and the sequential backward elimination algorithm. Two single‐sequence radiomic (T2WI, DWI) and two combined (T2WI‐DWI, T2WI‐DWI‐Clinic) models were respectively constructed and validated via logistic regression. Statistical Tests The Kruskal–Wallis test and chi‐squared test were utilized to evaluate the differences among variable groups. P < 0.05 determined statistical significance. The area under the receiver operating characteristic curve (AUC), specificity, sensitivity, and accuracy were used to evaluate model performance. The Delong test was conducted to compare the differences between the AUCs of all models. Result All radiomic models showed significant (P < 0.001) predictive performances. Between the single‐sequence radiomic models, the DWI model achieved the most encouraging results, with AUCs of 0.801 and 0.787 in the training and test cohorts, respectively. For the combined models, the T2WI‐DWI models acquired an AUC of 0.788, which was almost the same with DWI in the test cohort, and no significant difference was found between them (training cohort: P = 0.199; test cohort: P = 0.924). Data Conclusion Radiomics based on bpMRI can noninvasively identify high‐grade PCa before the operation, which is helpful for individualized diagnosis of PCa. Level of Evidence 4 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2020;52:1102–1109.