Eukaryotic translation initiation factor 3B accelerates the progression of esophageal squamous cell carcinoma by activating β-catenin signaling pathway

Xu, Fan; Xu, Chang; Gu, Jie; Liu, Xiaoming; Liu, Ronghua; Huang, Enyu; Yuan, Yunfeng; Zhao, Guochao; Jiang, Jiahao; Xu, Chen; Chu, Yiwei; Chen, Lu

doi:10.18632/oncotarget.9726

Cited by 31 publications

(48 citation statements)

References 24 publications

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“…In addition, apoptosis increased after eIF3b depletion. Similar pro-apoptotic effects have been reported in colon cancer 13 , glioblastoma 12 , and esophageal squamous carcinoma 19 . Interestingly, ER-stress-induced apoptosis seemed to be downregulated after eIF3b depletion.…”

Section: Discussionsupporting

confidence: 81%

“…We then showed that eIF3b depletion inhibited cell proliferation by compromising the cell cycle and inducing cell apoptosis. The G1/S arrest after eIF3b depletion has also been noted in bladder cancer 20 , glioblastoma 12 , and esophageal squamous carcinoma 19 . Wang et al found that reduction in overall protein synthesis by eIF3b depletion lead to the smaller size of cells which cannot pass the major size regulatory “R or restriction point” checkpoint, which is also a G1/S arrest reason 20 .…”

Section: Discussionmentioning

confidence: 81%

“…Earlier research showed that eIF3b played an important role in colon cancer 13 , glioblastoma 12 , esophageal squamous carcinoma 19 , and bladder cancer 20 . However, the role of eIF3b in ccRCC remains unclear.…”

Section: Introductionmentioning

confidence: 99%

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Eukaryotic Translation Initiation Factor 3b is both a Promising Prognostic Biomarker and a Potential Therapeutic Target for Patients with Clear Cell Renal Cell Carcinoma

Zang¹,

Zhang²,

Yan³

et al. 2017

J. Cancer

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Eukaryotic translation initiation factors (eIFs) constitute a new class of therapeutic cancer targets. EIF3b is the major scaffold protein of eIF3 (the largest core of eIFs). We sought to define the role played by and the mechanism of action of eIF3b in patients with clear cell renal cell carcinoma (ccRCC). We found that high-level eIF3b expression in tumors was not only associated with an aggressive tumor phenotype, but was also independently prognostic for patients with ccRCC. Knockdown of eIF3b impaired the action of the Akt pathway, thus inhibiting cell proliferation by disrupting the cell cycle and triggering apoptosis. Furthermore, the epithelial-to-mesenchymal transition was impaired after eIF3b depletion, via suppression of cell migration and invasion. Additionally, eIF3b knockdown significantly inhibited the growth of subcutaneous xenografts in mice. Together, these data show that eIF3b is both a promising prognostic biomarker and a potential therapeutic target for patients with ccRCC.

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Section: Discussionsupporting

confidence: 81%

Section: Discussionmentioning

confidence: 81%

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Eukaryotic Translation Initiation Factor 3b is both a Promising Prognostic Biomarker and a Potential Therapeutic Target for Patients with Clear Cell Renal Cell Carcinoma

Zang¹,

Zhang²,

Yan³

et al. 2017

J. Cancer

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“…8 Another study investigating the role of eIF3b in esophageal squamous cell carcinoma (ESCC) also revealed that eIF3b expression is much higher in ESCC tissues and ESCC cell lines, while a reduction in eIF3b suppresses cell proliferation and stimulates cell apoptosis through the regulation of the β-catenin signaling pathway. 18 In bladder and prostate cancer cells, eIF3b deletion decreases cell growth and represses the G1/S cell cycle transition by regulating cyclin A, E, Rb, and p27Kip1 protein expression -but not mRNA expression -and it reduces migration as well as interrupting the actin cytoskeleton and focal adhesions. 11 A recently published study showed that knockdown of eIF3b decreases cell viability and promotes apoptosis in osteosarcoma cells due to the regulation of tumor necrosis factor receptor superfamily member 21 (TNFRSF21).…”

Section: Discussionmentioning

confidence: 99%

Inhibition of eukaryotic initiation factor 3B suppresses proliferation and promotes apoptosis of chronic myeloid leukemia cells

Huang¹,

He²,

Wang³

et al. 2019

Adv Clin Exp Med

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Background. Eukaryotic translation initiation factor 3B (eIF3b) has been reported to be overexpressed in colon, bladder and prostate cancers as well as in glioblastoma. However, there is no report on any correlation of eIF3b gene expression with cell proliferation and apoptosis in chronic myeloid leukemia (CML). Objectives. In this study, we evaluated the role of eIF3b in cell proliferation and apoptosis in CML. Material and methods. Samples from patients with CML and CML cell lines were used. Quantitative RT-PCR, siRNA transfection, flow cytometry, and western blot analysis were performed. Results. Quantitative RT-PCR revealed that the expression of eIF3b mRNA in CML patients was higher than that in the non-malignant controls. The proliferation of CML cells decreased after transfection of the cells with siRNA. The proportion of cells in the G1 and S phases in the experimental group decreased after transfection, while the number of cells in the G2/M phase increased, as compared with the control group. The total cell apoptosis percentage in the sheIF3b group (transduction with lentivirus-anti-eIF3b in K562 cells) was higher than the shCtrl group (transduction with empty-vector lentivirus in K562 cells) after transfection. Caspase 3/7 activity was higher and the expression of anti-apoptotic protein BCL-2 was lower in the sheIF3b group than in the shCtrl group after transfection. Conclusions. Our results suggest that downregulation of eIF3b expression inhibits proliferation and induces apoptosis in CML cells.

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“…Fourth, some studies have revealed that translation initiation factor 3b ( EIF3B ), a key subunit of the largest translation initiation factor that acts to ensure the accuracy of translation initiation, is closely related to oncogenesis [68]. Xu et al [69] reported that EIF3B can accelerate the progression of esophageal squamous cell carcinoma by activating β-catenin signaling, which can promote immune escape and resistance to anti-PD-1 therapy [4]. Thus, copy-number gain of EIF3B may be associated with resistance to anti-PD-1 therapy in melanoma thrugh β-catenin activation.…”

Section: Resultsmentioning

confidence: 99%

A computational network approach to identify predictive biomarkers and therapeutic combinations for anti-PD-1 immunotherapy in cancer

Wang

Livingston

et al. 2020

Preprint

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24 Background: Despite remarkable success, only a subset of cancer patients have shown benefit from the anti-PD1 25 therapy. Therefore, there is a growing need to identify predictive biomarkers and therapeutic combinations for 26 improving the clinical efficacy. 27Results: Based upon the hypothesis that aberrations of any gene that are close to MHC class I genes in the gene 28 network are likely to deregulate MHC I pathway and affect tumor response to anti-PD1, we developed a network 29 approach to infer genes, pathway, and potential therapeutic target genes associated with response to PD-1/PD-L1 30 checkpoint immunotherapies in cancer. Our approach successfully identified genes (e.g. B2M and PTEN) and 31 pathways (e.g. JAK/STAT and WNT) known to be associated with anti-PD1 response. Our prediction was further 32 validated by 5 CRISPR gene sets associated with tumor resistance to cytotoxic T cells. Our results also showed that 33 many cancer genes that act as hubs in the gene network may drive immune evasion through indirectly deregulating 34 the MHC I pathway. The integration analysis of transcriptomic data of the 34 TCGA cancer types and our prediction 35 reveals that MHC I-immunoregulations may be tissue-specific. The signature-based score, the MHC I association 36 immunoscore (MIAS), calculated by integration of our prediction and TCGA melanoma transcriptomic data also 37 showed a good correlation with patient response to anti-PD1 for 354 melanoma samples complied from 5 cohorts. 38In addition, most targets of the 36 compounds that have been tested in clinical trials or used for combination 39 treatments with anti-PD1 are in the top list of our prediction (AUC=0.833). Integration of drug target data with our 40 top prediction further identified compounds that were recently shown to enhance tumor response to anti-PD1, such 41 as inhibitors of GSK3B, CDK, and PTK2. 42Conclusion: Our approach is effective to identify candidate genes and pathways associated with response to anti- 43PD-1 therapy, and can also be employed for in silico screening of potential compounds to enhances the efficacy of 44 anti-PD1 agents against cancer. 45 46 47 48 50 Breakthroughs in cancer immunotherapies have opened a new front in the war against cancer [1]. Instead of 51 directly targeting cancer cells using specific inhibitors, immunotherapies stimulate and modulate the host's 52 immune system to eliminate cancer cells. Recently, immune checkpoint blockade (ICB), which enhances T-cell 53 activity by inhibiting immunosuppressive checkpoint molecules such as cytotoxic T-lymphocyte-associated antigen 54 4 (CTLA-4), programmed cell death 1 (PD-1), and programmed cell death protein ligand 1 (PD-L1), has produced 55remarkably durable responses in some cancer patients. Despite these successes, only a subset of cancer patients 56 benefits from these therapies, and rates of response vary widely among cancer types. Therefore, there is a growing 57 need to understand the mechanisms underlying this de novo resistance, to select predictive biomar...

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Eukaryotic translation initiation factor 3B accelerates the progression of esophageal squamous cell carcinoma by activating β-catenin signaling pathway

Cited by 31 publications

References 24 publications

Eukaryotic Translation Initiation Factor 3b is both a Promising Prognostic Biomarker and a Potential Therapeutic Target for Patients with Clear Cell Renal Cell Carcinoma

Eukaryotic Translation Initiation Factor 3b is both a Promising Prognostic Biomarker and a Potential Therapeutic Target for Patients with Clear Cell Renal Cell Carcinoma

Inhibition of eukaryotic initiation factor 3B suppresses proliferation and promotes apoptosis of chronic myeloid leukemia cells

A computational network approach to identify predictive biomarkers and therapeutic combinations for anti-PD-1 immunotherapy in cancer

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