SUMOylation Wrestles With the Occurrence and Development of Breast Cancer

Qin, Yuanyuan; Yuan, Hong; Chen, Xu; Yang, Xinyi; Xing, Zhengcao; Shen, Yajie; Dong, Wanying; An, Siming; Qi, Yitao; Wang, Hongmei

doi:10.3389/fonc.2021.659661

Cited by 5 publications

(4 citation statements)

References 111 publications

(158 reference statements)

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“…The identified pathways and their frequencies are illustrated in Figure 5. For TCGA-BRCA, evidence of association with breast cancer or cancer in general can be found for all the identified pathways in previous literatures [39][40][41][42][43][44][45][46][47][48]. The pathway with frequency three in Figure 5a, R-HSA-163560, is triglyceride catabolism.…”

Section: Risk Group Assignment and Deepshap Interpretationmentioning

confidence: 63%

AUTOSurv: Interpretable Deep Learning Framework for Cancer Survival Analysis Incorporating Clinical and Multi-omics Data

Jiang,

Xu,

Bai

et al. 2023

Preprint

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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.

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Section: Risk Group Assignment and Deepshap Interpretationmentioning

confidence: 63%

AUTOSurv: Interpretable Deep Learning Framework for Cancer Survival Analysis Incorporating Clinical and Multi-omics Data

Jiang,

Xu,

Bai

et al. 2023

Preprint

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“…SUMOylation is a dynamic process regulated by a family of SUMO-specific proteases called sentrin-specific protease (SENP), which reversely detaches SUMO molecules from the target proteins [ 15 , 16 ]. Among the six members of the SENP family, it was reported that SENP1 plays a critical role in coordinating developmental angiogenesis by affecting the SUMOylation of VEGFR2 and NOTCH1 [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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“…Different from the ubiquitination mediated targets degradation (mainly referring to K48-linked ubiquitin chains), SUMOylation predominantly regulates protein localization and activity, and involves in multiple important biologic functions including cell cycle control, gene expression, genome stability, and protein trafficking, which are important for cell and tissue homeostasis. In recent years, many research groups, including our group, have shown that SUMO signaling is implicated in cancers [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Many different types of cancers show dysregulation of one or more components of the SUMO machinery, which predominantly results in an increase in SUMOylation.…”

Section: Introductionmentioning

confidence: 99%

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

Shi

et al. 2022

IJMS

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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.

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SUMOylation Wrestles With the Occurrence and Development of Breast Cancer

Cited by 5 publications

References 111 publications

AUTOSurv: Interpretable Deep Learning Framework for Cancer Survival Analysis Incorporating Clinical and Multi-omics Data

AUTOSurv: Interpretable Deep Learning Framework for Cancer Survival Analysis Incorporating Clinical and Multi-omics Data

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

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

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