The role of eukaryotic translation initiation factor 6 in tumors

Zhu, Wei; Li, Gui Xian; Chen, Hong Lang; Liu, Xing Yan

doi:10.3892/ol.2017.6161

Cited by 17 publications

(16 citation statements)

References 70 publications

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“…eIF6 promotes ATF4 translation and also preferentially activates downstream start-site usage within the CEBPB mRNA, generating the oncogenic LIP isoform [105] ( Figure 4B). Hence upregulation of eIF6 in various types of cancers promotes tumorigenesis [106]. Analogously, it has been proposed that eIF3 on the 40S subunit also has an antijoining function [107] (Figure 5c).…”

Section: Factors With Anti-ribosomal Subunit Joining Activitymentioning

confidence: 67%

uORF-mediated translational control: recently elucidated mechanisms and implications in cancer

Chen

Tarn

2019

RNA Biology

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Protein synthesis is tightly regulated, and its dysregulation can contribute to the pathology of various diseases, including cancer. Increased or selective translation of mRNAs can promote cancer cell proliferation, metastasis and tumor expansion. Translational control is one of the most important means for cells to quickly adapt to environmental stresses. Adaptive translation involves various alternative mechanisms of translation initiation. Upstream open reading frames (uORFs) serve as a major regulator of stress-responsive translational control. Since recent advances in omics technologies including ribo-seq have expanded our knowledge of translation, we discuss emerging mechanisms for uORF-mediated translation regulation and its impact on cancer cell biology. A better understanding of dysregulated translational control of uORFs in cancer would facilitate the development of new strategies for cancer therapy.

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Section: Factors With Anti-ribosomal Subunit Joining Activitymentioning

confidence: 67%

uORF-mediated translational control: recently elucidated mechanisms and implications in cancer

Chen

Tarn

2019

RNA Biology

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“…However, nucleolar eIF6 and cytoplasmic eIF6 are required for the biogenesis of 60S subunits and growth factor-stimulated translation, respectively [ 178 ]. Furthermore, an increasing number of studies have demonstrated that there is a highly aberrant expression of eIF6 in various types of human cancer including colorectal cancer, head and neck carcinoma, malignant mesothelioma, acute promyelocytic leukemia and NSCLC (Table 1 ) [ 2 , 179 ]. Therefore, modulation of eIF6 activity or expression may exert an innovative treatment for cancers.…”

Section: Eifs Mis-regulation In Human Cancer and Potential Targets Fomentioning

confidence: 99%

Eukaryotic translation initiation factors as promising targets in cancer therapy

Hao

Ward

et al. 2020

Cell Commun Signal

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The regulation of the translation of messenger RNA (mRNA) in eukaryotic cells is critical for gene expression, and occurs principally at the initiation phase which is mainly regulated by eukaryotic initiation factors (eIFs). eIFs are fundamental for the translation of mRNA and as such act as the primary targets of several signaling pathways to regulate gene expression. Mis-regulated mRNA expression is a common feature of tumorigenesis and the abnormal activity of eIF complexes triggered by upstream signaling pathways is detected in many tumors, leading to the selective translation of mRNA encoding proteins involved in tumorigenesis, metastasis, or resistance to anti-cancer drugs, and making eIFs a promising therapeutic target for various types of cancers. Here, we briefly outline our current understanding of the biology of eIFs, mainly focusing on the effects of several signaling pathways upon their functions and discuss their contributions to the initiation and progression of tumor growth. An overview of the progress in developing agents targeting the components of translation machinery for cancer treatment is also provided.

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“…EIF6 is potential biomarker for cancer as it downstream modulator of cell division resulting in oncogenesis 54 . ITPA downregulation promotes DNA instability, suppression of cell growth and apoptosis in SKBR3 cell lines 55 .…”

Section: T-sne (T-distributed Stochastic Neighbour Embedding) T-sne mentioning

confidence: 99%

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

Roy

Kumar

Mittal

et al. 2020

Sci Rep

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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 IDC. 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 machinelearning 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 IDC progression. The server is publicly available at http://bioinfo.icgeb.res.in/duct-BRCA-CSP.

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The role of eukaryotic translation initiation factor 6 in tumors

Cited by 17 publications

References 70 publications

uORF-mediated translational control: recently elucidated mechanisms and implications in cancer

uORF-mediated translational control: recently elucidated mechanisms and implications in cancer

Eukaryotic translation initiation factors as promising targets in cancer therapy

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

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