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Expression of Eukaryotic Translation Initiation Factor 3 Subunit B in Liver Cancer and Its Prognostic Significance

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436 EXPERIMENTAL AND THERAPEUTIC MEDICINE 20: 436-446, 2020 Expression of eukaryotic translation initiation factor 3 subunit B in liver cancer and its prognostic significance QING YUE1*, LINGYU MENG2*, BAOXING JIA2 and WEI HAN2 Departments of 1Oncology and 2Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, Jilin 130021, P.R. China Received July 16, 2019; Accepted December 19, 2019 DOI: 10.3892/etm.2020.8726 Abstract. Liver cancer is one of the major malignancies with in high EIF3B expression and low EIF3B expression groups. the worst prognosis among all solid tumor types. It is therefore In conclusion, high EIF3B expression was indicated to be an ponderable to explore prognostic biomarkers and therapeutic independent prognostic biomarker for patients with liver cancer. targets for liver cancer. Eukaryotic translation initiation factor 3 subunit B (EIF3B) is closely linked to the transcription initia- Introduction tion of cancer-associated genes. In the present study, EIF3B was indicated to be a potential prognostic biomarker of liver cancer. Liver cancer is a common malignant tumor type with high The mRNA expression level of EIF3B in liver cancer was morbidity and mortality (1). Although various treatments have assessed by analyzing the Cancer Genome Atlas dataset. χ2 and been improved, the mortality rate of liver cancer is still high Fisher's exact tests were used to assess the association of EIF3B and the prognosis remains poor (2,3). Therefore, prognostic expression with clinical parameters. Receiver-operating char- biomarkers of liver cancer have become one of the hotspots acteristic curve analysis was used for evaluating the diagnostic of current research (4). The discovery of accurate prognostic value of EIF3B. Overall and relapse-free survival were assessed biomarkers may contribute to clinical guidance in order to using Kaplan-Meier curves to determine the association improve the evaluation system of liver cancer. between EIF3B expression and survival. Univariate and multi- The family of eukaryotic translation initiation factors (EIFs) variate Cox regression analysis were performed to identify the participates in eukaryotic translation by regulating the interac- factors affecting overall/relapse-free survival. Gene set enrich- tion between ribosomes and RNA. It is the rate-limiting step ment analysis (GSEA) was used to identify signaling pathways of protein synthesis and participates in numerous processes associated with EIF3B in liver cancer. It was revealed that that are deregulated in cancer cells, including DNA repair EIF3B was highly expressed in liver cancer tissues and it had a and proliferation, cell cycle and apoptosis (5). EIF 3 subunit promising diagnostic ability. Furthermore, the survival analysis B (EIF3B) is an important member of the family of EIFs and indicated that patients with high EIF3B expression generally had has been observed to be overexpressed in numerous cancer shorter overall as well as relapse-free survival. Univariate and types, including clear cell renal cell carcinoma (6), esopha- multivariate Cox analysis suggested that high EIF3B mRNA geal squamous cell carcinoma (7), glioblastoma (8), ovarian expression may serve as an independent biomarker for the cancer (9), osteosarcoma (10) and lung cancer (11), and has prognostication of patients with liver cancer. GSEA suggested an important role in the progression and prognosis of several that MYC-V1 (HALLMARK_MYC_TARGETS_V1 geneset; cancer types (12-14). In addition, Golob-Schwarzl et al (15) P=0.009), MYC-V2 (HALLMARK_MYC_TARGETS_V2 reported that EIF3B was upregulated in hepatitis C virus geneset; P=0.004) and DNA repair pathways (HALLMARK_ (HCV)-associated hepatocellular carcinoma (HCC). However, DNA_REPAIR geneset; P<0.001) were differentially enriched the precise role of EIF3B in liver cancer has remained elusive. To further evaluate the roles of EIF3B in patients with liver cancer, the expression of EIF3B was examined in a dataset from The Cancer Genome Atlas (TCGA) database. The χ2 and Fisher's exact tests were used to assess the association Correspondence to: Dr Wei Han, Department of Hepatobiliary of EIF3B with clinicopathological parameters and demo- and Pancreatic Surgery, The First Hospital of Jilin University, graphic features. Receiver operating characteristics (ROC) 71 Xinmin Street, Changchun, Jilin 130021, P.R. China E-mail: [email protected] curve analysis was used for evaluating the diagnostic value of EIF3B. Kaplan-Meier overall survival and relapse-free *Contributed equally survival analysis were performed to determine the association between EIF3B expression and survival. Univariate and multi- Key words: eukaryotic translation initiation factor 3 subunit B, liver variate Cox regression analysis were performed to identify the cancer, prognosis, The Cancer Genome Atlas, biomarker factors affecting overall survival and relapse-free survival. Furthermore, gene set enrichment analysis (GSEA) was used to explore EIF3B-associated signaling pathways. YUE et al: EXPRESSION OF EIF3B IN LIVER CANCER AND PROGNOSTIC SIGNIFICANCE 437 Table I. Demographic and clinical characteristics of the cohort Table I. Continued. from The Cancer Genome Atlas-liver hepatocellular carci- noma dataset. Characteristics N (373) Characteristics N (373) Vital status Deceased 130 (34.85) Age (years) Alive 243 (65.15) <55 117 (31.45) Sample type ≥55 255 (68.55) Primary tumor 371 (99.46) NA 1 (0.00) Recurrent tumor 2 (0.54) Sex Overall survival (ten years) Female 121 (32.44) No 237 (64.58) Male 252 (67.56) Yes 130 (35.42) Histological type Relapse-free survival (ten years) Fibrolamellar carcinoma 3 (0.80) No 179 (55.94) Hepatocellular carcinoma 363 (97.32) Yes 141 (44.06) Hepatocholangiocarcinoma (mixed) 7 (1.88) EIF3B Histologic grade High 109 (29.22) NA 5 (1.34) Low 264 (70.78) G1 55 (14.75) G2 178 (47.72) NA, not available; EIF3B, eukaryotic translation initiation factor 3 G3 123 (32.98) subunit B; G1-4, grade relating to degree of differentiation; T1-4, G4 12 (3.22) size and or extension of the primary tumor; TX, tumor could not be assessed; N0, no regional lymph node metastasis; N1, regional lymph Stage node metastasis present; NX, lymph nodes could not be assessed; NA 24 (6.43) M0, no distant metastasis; M1, metastasis to distant organs; MX, I 172 (46.11) metastasis could not be assessed; R0, no residual tumor visible under II 87 (23.32) the microscope; R1, residual tumor visible under the microscope; R2, residual tumor visible to the naked eye; RX, residual tumor could not III 85 (22.79) be assessed. IV 5 (1.34) T classification NA 2 (0.54) T1 182 (48.79) Materials and methods T2 95 (25.47) T3 80 (21.45) Data source. The clinical information of patients and their T4 13 (3.49) RNAseq data were obtained from TCGA (https://cancerge- TX 1 (0.27) nome.nih.gov/). All patients from the liver hepatocellular N classification carcinoma (LIHC) cohort were screened based on TCGA NA 1 (0.27) inclusion and exclusion pre-selection criteria. N0 253 (67.83) Statistical analysis. R (version 3.6.1; The R Foundation) (16) N1 4 (1.07) was used for statistical analysis (t-test, Kruskal-Wallis with NX 115 (30.83) Dunn's post-hoc test, Wilcoxon sum-rank test) and genera- M classification tion of images. The ggplot2 package (17) was used to draw M0 267 (71.58) boxplots of the EIF3B expression in subgroups by clinical M1 4 (1.07) characteristics. χ2 and Fisher's exact tests were applied to MX 102 (27.35) estimate the significance of the association between EIF3B Radiation therapy expression and clinicopathological or demographic charac- NA 25 (6.70) teristics. The pROC package (18) was used to plot the ROC curves and assess the diagnostic ability of EIF3B, and patients No 340 (91.15) were divided into a high expression group and low expression Yes 8 (2.14) group according to the best operating system cut-off value Residual tumor determined by the Youden index. The survival package (19) NA 7 (1.88) was used to draw survival curves. A univariate Cox linear R0 326 (87.40) regression model was utilized to select correlative variables R1 17 (4.56) affecting survival time. Multivariate Cox regression analysis R2 1 (0.27) was employed to evaluate the independent influencing factors RX 22 (5.90) of survival time. 438 EXPERIMENTAL AND THERAPEUTIC MEDICINE 20: 436-446, 2020 Figure 1. Differences in EIF3B expression among subgroups of patients. Boxplots showing differences in EIF3B expression according to (A) tissue type (P<0.001 vs. normal tissue controls), (B) age, (C) sex, (D) histologic grade (P<0.001), (E) histological type, (F) T classification, (G) N classification, (H) M classification, (I) radiation therapy, (J) residual tumor classification, (K) sample type, (L) clinical stage and (M) vital status (P=0.005). EIF3B, eukaryotic translation initiation factor 3 subunit B. G1-4, grade relating to degree of differentiation; T1-4, size and or extension of the primary tumor; TX, tumor could not be assessed; N0, no regional lymph node metastasis; N1, regional lymph node metastasis present; NX, lymph nodes could not be assessed; M0, no distant metastasis; M1, metastasis to distant organs; MX, metastasis could not be assessed; R0, no residual tumor visible under the microscope; R1, residual tumor visible under the microscope; R2, residual tumor visible to the naked eye; RX, residual tumor could not be assessed. GSEA. GSEA may be used to determine whether a predefined therapy, residual tumor, relapse-free survival, histological set of genes is able to indicate significant, consistent differ- type, stage, vital status, survival data, T/N/M classification and ences between two biological states (20). In the present study, EIF3B expression are provided. GSEA was performed in the ‘h.all.v6.2.symbols.gmt’ and ‘c2. cp.biocarta.v6.2.symbols.gmt’ gene sets using GSEA3.0 Expression of EIF3B in liver tissues. Boxplots revealed that software. The standardized enrichment fraction was obtained EIF3B was significantly upregulated in liver cancer compared by 1,000 permutation analyses.
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    1 2 3 Pharmacological dimerization and activation of the exchange factor eIF2B antagonizes the 4 integrated stress response 5 6 7 *Carmela Sidrauski1,2, *Jordan C. Tsai1,2, Martin Kampmann2,3, Brian R. Hearn4, Punitha 8 Vedantham4, Priyadarshini Jaishankar4 , Masaaki Sokabe5, Aaron S. Mendez1,2, Billy W. 9 Newton6, Edward L. Tang6.7, Erik Verschueren6, Jeffrey R. Johnson6,7, Nevan J. Krogan6,7,, 10 Christopher S. Fraser5, Jonathan S. Weissman2,3, Adam R. Renslo4, and Peter Walter 1,2 11 12 1Department of Biochemistry and Biophysics, University of California, San Francisco, United 13 States 14 2Howard Hughes Medical Institute, University of California, San Francisco, United States 15 3Department of Cellular and Molecular Pharmacology, University of California, San Francisco, 16 United States 17 4Department of Pharmaceutical Chemistry and the Small Molecule Discovery Center, University 18 of California at San Francisco, United States 19 5Department of Molecular and Cellular Biology, College of Biological Sciences, University of 20 California, Davis, United States 21 6QB3, California Institute for Quantitative Biosciences, University of California, San Francisco, 22 United States 23 7Gladstone Institutes, San Francisco, United States 24 25 * Both authors contributed equally to this work 26 27 28 Abstract 29 30 The general translation initiation factor eIF2 is a major translational control point. Multiple 31 signaling pathways in the integrated stress response phosphorylate eIF2 serine-51, inhibiting 32 nucleotide exchange by eIF2B. ISRIB, a potent drug-like small molecule, renders cells 33 insensitive to eIF2α phosphorylation and enhances cognitive function in rodents by blocking 34 long-term depression. ISRIB was identified in a phenotypic cell-based screen, and its mechanism 35 of action remained unknown.
  • Relevance of Translation Initiation in Diffuse Glioma Biology and Its

    Relevance of Translation Initiation in Diffuse Glioma Biology and Its

    cells Review Relevance of Translation Initiation in Diffuse Glioma Biology and its Therapeutic Potential Digregorio Marina 1, Lombard Arnaud 1,2, Lumapat Paul Noel 1, Scholtes Felix 1,2, Rogister Bernard 1,3 and Coppieters Natacha 1,* 1 Laboratory of Nervous System Disorders and Therapy, GIGA-Neurosciences Research Centre, University of Liège, 4000 Liège, Belgium; [email protected] (D.M.); [email protected] (L.A.); [email protected] (L.P.N.); [email protected] (S.F.); [email protected] (R.B.) 2 Department of Neurosurgery, CHU of Liège, 4000 Liège, Belgium 3 Department of Neurology, CHU of Liège, 4000 Liège, Belgium * Correspondence: [email protected] Received: 18 October 2019; Accepted: 26 November 2019; Published: 29 November 2019 Abstract: Cancer cells are continually exposed to environmental stressors forcing them to adapt their protein production to survive. The translational machinery can be recruited by malignant cells to synthesize proteins required to promote their survival, even in times of high physiological and pathological stress. This phenomenon has been described in several cancers including in gliomas. Abnormal regulation of translation has encouraged the development of new therapeutics targeting the protein synthesis pathway. This approach could be meaningful for glioma given the fact that the median survival following diagnosis of the highest grade of glioma remains short despite current therapy. The identification of new targets for the development of novel therapeutics is therefore needed in order to improve this devastating overall survival rate. This review discusses current literature on translation in gliomas with a focus on the initiation step covering both the cap-dependent and cap-independent modes of initiation.