Dysregulation of ubiquitin ligases in cancer

Qi, Jianfei; Ronai, Ze’ev

doi:10.1016/j.drup.2015.09.001

Cited by 50 publications

(49 citation statements)

References 236 publications

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“…This process mediate cellular processes such as protein localization, cell cycle regulation and DNA damage repair [31]. Ubiquitin dys-regulation can affect tumour suppressor or oncogene leading to cellular transformation and cancer [32]. DNAJB1 binds to mitogen-inducible gene MIG6, a tumour suppressor, which positively regulates epidermal growth factor signalling, leading to breast cancer development [33].…”

Section: Resultsmentioning

confidence: 99%

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Roy

Kumar

Mittal

et al. 2019

Preprint

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Phone: +91 26743007Fax: +91 26742316 Email -dinesh@icgeb.res.in (DG)Early detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treatment to breast cancer patients.However, despite considerable efforts and progress, there is a need to identify the specific genomic factors responsible for, or accompanying Invasive Ductal Carcinoma (IDC) progression stages, which can aid the determination of the correct cancer stages. We have developed two-class machine-learning classification models to differentiate the early and late stages of invasive ductal carcinoma. The prediction models are trained with RNA-seq gene expression profiles representing different IDC stages of 610 patients, obtained from The Cancer Genome Atlas (TCGA). Different supervised learning algorithms were trained and evaluated with an enriched model learning, facilitated by different feature selection methods.We also developed a machine-learning classifier trained on the same datasets with training sets reduced data corresponding to IDC driver genes. Based on these two classifiers, we have developed a web-server Duct-BRCA-CSP to predict early stage from late stages of IDC based on input RNA-seq gene expression profiles. The analysis conducted by us also enables deeper insights into the stage-dependent molecular events accompanying breast ductal carcinoma progression. The server is publicly available at http://bioinfo.icgeb.res.in/duct-BRCA-CSP. Key PointsDifferent supervised machine-learning algorithms such as Random Forest, SVM and Naive Bayes were trained with enriched features of the TCGA RNA-seq datasets selected for the study.We have developed two-class classification models, trained with relevant gene expression profiles to efficiently discriminate between the early and late IDC stages.Finally, we also developed a web server using python scikit-learn to provide freely available GUI based access to the machine learning models developed by us. The server is publicly available at http://bioinfo.icgeb.res.in/duct-BRCA-CSP.

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Section: Resultsmentioning

confidence: 99%

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Roy

Kumar

Mittal

et al. 2019

Preprint

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“…Expression and activity of Siah2 is regulated by stress signals often activated in cancer, such as hypoxia, p38 activity, or responses to ER stress [53]. Siah2 controls mitochondrial fission through its regulation of AKAP121 under stress conditions including hypoxia.…”

Section: Ubiquitin Ligases As Emerging Regulators Of Mitochondrial Dymentioning

confidence: 99%

Regulators of mitochondrial dynamics in cancer

Senft

Ronai

2016

Current Opinion in Cell Biology

222

230

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Mitochondrial dynamics encompasses processes associated with mitochondrial fission and fusion, affecting their number, degree of biogenesis, and the induction of mitophagy. These activities determine the balance between mitochondrial energy production and cell death programs. Processes governing mitochondrial dynamics are tightly controlled in physiological conditions and are often deregulated in cancer. Mitochondrial protein homeostasis, transcriptional regulation, and post-translational modification are among processes that govern the control of mitochondrial dynamics. Cancer cells alter mitochondrial dynamics to resist apoptosis and adjust their bioenergetic and biosynthetic needs to support tumor initiating and transformation properties including proliferation, migration, and therapeutic resistance. This review focuses on key regulators of mitochondrial dynamics and their role in cancer.

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“…The activity of many E3s is deregulated in cancer (Supplementary information S1 (table)) by epigenetic and genetic mechanisms and/or as a consequence of altered post-translational mechanisms, which are modified in response to extrinsic and intrinsic cues (reviewed in REF. 11) (FIG. 1).…”

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confidence: 99%

Ubiquitin ligases in oncogenic transformation and cancer therapy

2017

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The cellular response to external stress signals and DNA damage depends on the activity of ubiquitin ligases (E3s), which regulate numerous cellular processes, including homeostasis, metabolism and cell cycle progression. E3s recognize, interact with and ubiquitylate protein substrates in a temporally and spatially regulated manner. The topology of the ubiquitin chains dictates the fate of the substrates, marking them for recognition and degradation by the proteasome or altering their subcellular localization or assembly into functional complexes. Both genetic and epigenetic alterations account for the deregulation of E3s in cancer. Consequently, the stability and/or activity of E3 substrates are also altered, in some cases leading to downregulation of tumour-suppressor activities and upregulation of oncogenic activities. A better understanding of the mechanisms underlying E3 regulation and function in tumorigenesis is expected to identify novel prognostic markers and to enable the development of the next generation of anticancer therapies. This Review summarizes the oncogenic and tumour-suppressor roles of selected E3s and highlights novel opportunities for therapeutic intervention.

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Dysregulation of ubiquitin ligases in cancer

Cited by 50 publications

References 236 publications

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Regulators of mitochondrial dynamics in cancer

Ubiquitin ligases in oncogenic transformation and cancer therapy

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