DNA topoisomerase IIα (Topo IIα) ensures genomic integrity and unaltered chromosome inheritance and serves as a major target of several anticancer drugs. Topo IIα function is well understood, but how its expression is regulated remains unclear. Here, we identify the E3 ubiquitin ligase Smurf2 as a physiologic regulator of Topo IIα levels. Smurf2 physically interacted with Topo IIα and modified its ubiquitination status to protect Topo IIα from the proteasomal degradation in dose- and catalytically dependent manners. Smurf2-depleted cells exhibited a reduced ability to resolve DNA catenanes and pathological chromatin bridges formed during mitosis, a trait of Topo IIα-deficient cells and a hallmark of chromosome instability. Introducing Topo IIα into Smurf2-depleted cells rescued this phenomenon. Smurf2 was a determinant of Topo IIα protein levels in normal and cancer cells and tissues, and its levels affected cell sensitivity to the Topo II-targeting drug etoposide. Our results identified Smurf2 as an essential regulator of Topo IIα, providing novel insights into its control and into the suggested tumor-suppressor functions of Smurf2. .

SummaryA‐lamins, encoded by the LMNA gene, are major structural components of the nuclear lamina coordinating essential cellular processes. Mutations in the LMNA gene and/or alterations in its expression levels have been linked to a distinct subset of human disorders, collectively known as laminopathies, and to cancer. Mechanisms regulating A‐lamins are mostly obscure. Here, we identified E3 ubiquitin ligase Smurf2 as a physiological regulator of lamin A and its disease‐associated mutant form progerin (LAΔ50), whose expression underlies the development of Hutchinson‐Gilford progeria syndrome (HGPS), a devastating premature aging syndrome. We show that Smurf2 directly binds, ubiquitinates, and negatively regulates the expression of lamin A and progerin in Smurf2 dose‐ and E3 ligase‐dependent manners. Overexpression of catalytically active Smurf2 promotes the autophagic–lysosomal breakdown of lamin A and progerin, whereas Smurf2 depletion increases lamin A levels. Remarkably, acute overexpression of Smurf2 in progeria fibroblasts was able to significantly reduce the nuclear deformability. Furthermore, we demonstrate that the reciprocal relationship between Smurf2 and A‐lamins is preserved in different types of mouse and human normal and cancer tissues. These findings establish Smurf2 as an essential regulator of lamin A and progerin and lay a foundation for evaluating the efficiency of progerin clearance by Smurf2 in HGPS, and targeting of the Smurf2–lamin A axis in age‐related diseases such as cancer.

SMURF2, an E3 ubiquitin ligase and suggested tumor suppressor, operates in normal cells to prevent genomic instability and carcinogenesis. However, the mechanisms underlying SMURF2 inactivation in human malignancies remain elusive, as SMURF2 is rarely found mutated or deleted in cancers. We hypothesized that SMURF2 might have a distinct molecular biodistribution in cancer versus normal cells and tissues. The expression and localization of SMURF2 were analyzed in 666 human normal and cancer tissues, with primary focus on prostate and breast tumors. These investigations were accompanied by SMURF2 gene expression analyses, subcellular fractionation and biochemical studies, including SMURF2’s interactome analysis. We found that while in normal cells and tissues SMURF2 has a predominantly nuclear localization, in prostate and aggressive breast carcinomas SMURF2 shows a significantly increased cytoplasmic sequestration, associated with the disease progression. Mechanistic studies showed that the nuclear export machinery was not involved in cytoplasmic accumulation of SMURF2, while uncovered that its stability is markedly increased in the cytoplasmic compartment. Subsequent interactome analyses pointed to 14-3-3s as SMURF2 interactors, which could potentially affect its localization. These findings link the distorted expression of SMURF2 to human carcinogenesis and suggest the alterations in SMURF2 localization as a potential mechanism obliterating its tumor suppressor activities.

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Background Renal Cell Carcinoma (RCC) is difficult to treat with 5-year survival rate of 10% in metastatic patients. Main reasons of therapy failure are lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression. Thus, the investigation of mechanisms regulating RCC progression is fundamental to improve RCC therapy. Methods In order to identify molecular markers and gene processes involved in the steps of RCC progression, we generated several cell lines of higher aggressiveness by serially passaging mouse renal cancer RENCA cells in mice and, concomitantly, performed functional genomics analysis of the cells. Multiple cell lines depicting the major steps of tumor progression (including primary tumor growth, survival in the blood circulation and metastatic spread) were generated and analyzed by large-scale transcriptome, genome and methylome analyses. Furthermore, we performed clinical correlations of our datasets. Finally we conducted a computational analysis for predicting the time to relapse based on our molecular data. Results Through in vivo passaging, RENCA cells showed increased aggressiveness by reducing mice survival, enhancing primary tumor growth and lung metastases formation. In addition, transcriptome and methylome analyses showed distinct clustering of the cell lines without genomic variation. Distinct signatures of tumor aggressiveness were revealed and validated in different patient cohorts. In particular, we identified SAA2 and CFB as soluble prognostic and predictive biomarkers of the therapeutic response. Machine learning and mathematical modeling confirmed the importance of CFB and SAA2 together, which had the highest impact on distant metastasis-free survival. From these data sets, a computational model predicting tumor progression and relapse was developed and validated. These results are of great translational significance. Conclusion A combination of experimental and mathematical modeling was able to generate meaningful data for the prediction of the clinical evolution of RCC.