SUMOylation Wrestles With the Occurrence and Development of Breast Cancer

AUTOSURV: INTERPRETABLE DEEP LEARNING FRAMEWORK FOR CANCER SURVIVAL ANALYSIS INCORPORATING CLINICAL AND MULTI-OMICS DATA

Accurate prognosis for cancer patients can provide critical information for optimizing treatment plans and improving life quality. Combining omics data and demographic/clinical information can offer a more comprehensive view of cancer prognosis than using omics or clinical data alone and can reveal the underlying disease mechanisms at the molecular level. In this study, we developed a novel deep learning framework to extract information from high-dimensional gene expression and miRNA expression data and conduct prognosis prediction for breast cancer and ovarian cancer patients. Our model achieved significantly better prognosis prediction than the conventional Cox Proportional Hazard model and other competitive deep learning approaches in various settings. Moreover, an interpretation approach was applied to tackle the "black-box" nature of deep neural networks and we identified features (i.e., genes, miRNA, demographic/clinical variables) that made important contributions to distinguishing predicted high- and low-risk patients. The identified associations were partially supported by previous studies.

Knockdown SENP1 Suppressed the Angiogenic Potential of Mesenchymal Stem Cells by Impacting CXCR4-Regulated MRTF-A SUMOylation and CCN1 Expression

The angiogenic potential of mesenchymal stem cells (MSCs) is critical for adult vascular regeneration and repair, which is regulated by various growth factors and cytokines. In the current study, we report that knockdown SUMO-specific peptidase 1 (SENP1) stimulated the SUMOylation of MRTF-A and prevented its translocation into the nucleus, leading to downregulation of the cytokine and angiogenic factor CCN1, which significantly impacted MSC-mediated angiogenesis and cell migration. Further studies showed that SENP1 knockdown also suppressed the expression of a chemokine receptor CXCR4, and overexpression of CXCR4 could partially abrogate MRTF-A SUMOylation and reestablish the CCN1 level. Mutation analysis confirmed that SUMOylation occurred on three lysine residues (Lys-499, Lys-576, and Lys-624) of MRTF-A. In addition, SENP1 knockdown abolished the synergistic co-activation of CCN1 between MRTF-A and histone acetyltransferase p300 by suppressing acetylation on histone3K9, histone3K14, and histone4. These results revealed an important signaling pathway to regulate MSC differentiation and angiogenesis by MRTF-A SUMOylation involving cytokine/chemokine activities mediated by CCN1 and CXCR4, which may potentially impact a variety of cellular processes such as revascularization, wound healing, and progression of cancer.

The Role of SUMO E3 Ligases in Signaling Pathway of Cancer Cells

Small ubiquitin-like modifier (SUMO)ylation is a reversible post-translational modification that plays a crucial role in numerous aspects of cell physiology, including cell cycle regulation, DNA damage repair, and protein trafficking and turnover, which are of importance for cell homeostasis. Mechanistically, SUMOylation is a sequential multi-enzymatic process where SUMO E3 ligases recruit substrates and accelerate the transfer of SUMO onto targets, modulating their interactions, localization, activity, or stability. Accumulating evidence highlights the critical role of dysregulated SUMO E3 ligases in processes associated with the occurrence and development of cancers. In the present review, we summarize the SUMO E3 ligases, in particular, the novel ones recently identified, and discuss their regulatory roles in cancer pathogenesis.