EIF4G1 Is a Potential Prognostic Biomarker of Breast Cancer
Abstract
:1. Introduction
2. Methods and Materials
2.1. Data Collection and Preprocessing
2.2. Functions and Expression Analyses of EIF4G1 in Pan-Cancer
2.3. The Expression Level of EIF4G1 in Normal and Tumor Tissues
2.4. The Prognostic Value of EIF4G1 for BRCA
2.5. Enrichment Analysis
2.6. Immune Cells Infiltration, Immune Checkpoints, and Immunotherapy Response Estimation
2.7. Identification of Potential Therapeutic Compounds
2.8. IHC Staining Evaluation
2.9. Statistical Analysis
3. Results
3.1. Research Process
3.2. Association of EIF4G1 with Pan-Cancer
3.3. Upregulation of EIF4G1 in BRCA
3.4. The Expression Level of EIF4Gl in HPA
3.5. Functional Enrichment Analysis of EIF4G1
3.6. Prognostic Performance of EIF4G1 in BRCA
3.7. Analyses of Immune Infiltration, Immune Checkpoints, and Immunotherapy Response
3.8. Screening for Potential Small Molecules Drugs
3.9. IHC Experimental Verification
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: Globocan Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef]
- Cao, M.; Li, H.; Sun, D.; Chen, W. Cancer Burden of Major Cancers in China: A Need for Sustainable Actions. Cancer Commun. 2020, 40, 205–210. [Google Scholar] [CrossRef]
- Lei, S.; Zheng, R.; Zhang, S.; Wang, S.; Chen, R.; Sun, K.; Zeng, H.; Zhou, J.; Wei, W. Global Patterns of Breast Cancer Incidence and Mortality: A Population-Based Cancer Registry Data Analysis from 2000 to 2020. Cancer Commun. 2021, 41, 1183–1194. [Google Scholar] [CrossRef]
- Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer Statistics, 2022. CA Cancer J. Clin. 2022, 72, 7–33. [Google Scholar] [CrossRef]
- Yeo, S.K.; Guan, J.L. Breast Cancer: Multiple Subtypes within a Tumor? Trends Cancer 2017, 3, 753–760. [Google Scholar] [CrossRef]
- Barzaman, K.; Karami, J.; Zarei, Z.; Hosseinzadeh, A.; Kazemi, M.H.; Moradi-Kalbolandi, S.; Safari, E.; Farahmand, L. Breast Cancer: Biology, Biomarkers, and Treatments. Int. Immunopharmacol. 2020, 84, 106535. [Google Scholar] [CrossRef] [PubMed]
- Franzoi, M.A.; Romano, E.; Piccart, M. Immunotherapy for Early Breast Cancer: Too Soon, Too Superficial, or Just Right? Ann. Oncol. 2021, 32, 323–336. [Google Scholar] [CrossRef] [PubMed]
- Harbeck, N.; Gnant, M. Breast Cancer. Lancet 2017, 389, 1134–1150. [Google Scholar] [CrossRef] [PubMed]
- Ali, H.; Rueda, O.M.; Chin, S.F.; Curtis, C.; Dunning, M.J.; Aparicio, S.; Caldas, C. Genome-Driven Integrated Classification of Breast Cancer Validated in over 7,500 Samples. Genome Biol. 2014, 15, 431. [Google Scholar] [CrossRef]
- Wang, S.; Zhang, Q.; Yu, C.; Cao, Y.; Zuo, Y.; Yang, L. Immune Cell Infiltration-Based Signature for Prognosis and Immunogenomic Analysis in Breast Cancer. Brief Bioinform. 2021, 22, 2020–2031. [Google Scholar] [CrossRef]
- Hanker, A.B.; Sudhan, D.R.; Arteaga, C.L. Overcoming Endocrine Resistance in Breast Cancer. Cancer Cell 2020, 37, 496–513. [Google Scholar] [CrossRef] [PubMed]
- Emens, L.A. Breast Cancer Immunotherapy: Facts and Hopes. Clin. Cancer Res. 2018, 24, 511–520. [Google Scholar] [CrossRef] [Green Version]
- Tan, W.; Liu, M.; Wang, L.; Guo, Y.; Wei, C.; Zhang, S.; Luo, C.; Liu, N. Novel Immune-Related Genes in the Tumor Microenvironment with Prognostic Value in Breast Cancer. BMC Cancer 2021, 21, 126. [Google Scholar] [CrossRef]
- Xie, P.; Ma, Y.; Yu, S.; An, R.; He, J.; Zhang, H. Development of an Immune-Related Prognostic Signature in Breast Cancer. Front. Genet. 2019, 10, 1390. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhao, Y.; Pu, C.; Liu, Z. Exploration the Significance of a Novel Immune-Related Gene Signature in Prognosis and Immune Microenvironment of Breast Cancer. Front. Oncol. 2020, 10, 1211. [Google Scholar] [CrossRef] [PubMed]
- Tu, L.; Liu, Z.; He, X.; He, Y.; Yang, H.; Jiang, Q.; Xie, S.; Xiao, G.; Li, X.; Yao, K.; et al. Over-Expression of Eukaryotic Translation Initiation Factor 4 Gamma 1 Correlates with Tumor Progression and Poor Prognosis in Nasopharyngeal Carcinoma. Mol. Cancer 2010, 9, 78. [Google Scholar] [CrossRef] [Green Version]
- Li, L.; Luo, Q.; Xie, Z.; Li, G.; Mao, C.; Liu, Y.; Wen, X.; Yin, N.; Cao, J.; Wang, J.; et al. Characterization of the Expression of the Rna Binding Protein Eif4g1 and Its Clinicopathological Correlation with Serous Ovarian Cancer. PLoS ONE 2016, 11, e0163447. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Silvera, D.; Arju, R.; Darvishian, F.; Levine, P.H.; Zolfaghari, L.; Goldberg, J.; Hochman, T.; Formenti, S.C.; Schneider, R.J. Essential Role for Eif4gi Overexpression in the Pathogenesis of Inflammatory Breast Cancer. Nat. Cell Biol. 2009, 11, 903–908. [Google Scholar] [CrossRef]
- Wu, S.; Wagner, G. Deep Computational Analysis Details Dysregulation of Eukaryotic Translation Initiation Complex Eif4f in Human Cancers. Cell Syst. 2021, 12, 907–923.e906. [Google Scholar] [CrossRef]
- Bhat, M.; Robichaud, N.; Hulea, L.; Sonenberg, N.; Pelletier, J.; Topisirovic, I. Targeting the Translation Machinery in Cancer. Nat. Rev. Drug. Discov. 2015, 14, 261–278. [Google Scholar] [CrossRef] [PubMed]
- Pelletier, J.; Graff, J.; Ruggero, D.; Sonenberg, N. Targeting the Eif4f Translation Initiation Complex: A Critical Nexus for Cancer Development. Cancer Res. 2015, 75, 250–263. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Badura, M.; Braunstein, S.; Zavadil, J.; Schneider, R.J. DNA Damage and Eif4g1 in Breast Cancer Cells Reprogram Translation for Survival and DNA Repair Mrnas. Proc. Natl. Acad. Sci. USA 2012, 109, 18767–18772. [Google Scholar] [CrossRef] [Green Version]
- Ramirez-Valle, F.; Braunstein, S.; Zavadil, J.; Formenti, S.C.; Schneider, R.J. Eif4gi Links Nutrient Sensing by Mtor to Cell Proliferation and Inhibition of Autophagy. J. Cell Biol. 2008, 181, 293–307. [Google Scholar] [CrossRef] [Green Version]
- Li, S.; Shao, J.; Lou, G.; Wu, C.; Liu, Y.; Zheng, M. Mir-144-3p-Mediated Dysregulation of Eif4g2 Contributes to the Development of Hepatocellular Carcinoma through the Erk Pathway. J. Exp. Clin. Cancer Res. 2021, 40, 53. [Google Scholar] [CrossRef] [PubMed]
- Li, X.Y.; Zhao, Z.J.; Wang, J.B.; Shao, Y.H.; Hui, L.; You, J.X.; Yang, X.T. M7g Methylation-Related Genes as Biomarkers for Predicting Overall Survival Outcomes for Hepatocellular Carcinoma. Front. Bioeng. Biotechnol. 2022, 10, 849756. [Google Scholar] [CrossRef] [PubMed]
- Yu, L.; Ding, Y.; Wan, T.; Deng, T.; Huang, H.; Liu, J. Significance of Cd47 and Its Association with Tumor Immune Microenvironment Heterogeneity in Ovarian Cancer. Front. Immunol. 2021, 12, 768115. [Google Scholar] [CrossRef]
- Yuan, H.; Yan, M.; Zhang, G.; Liu, W.; Deng, C.; Liao, G.; Xu, L.; Luo, T.; Yan, H.; Long, Z.; et al. Cancersea: A Cancer Single-Cell State Atlas. Nucleic Acids Res. 2019, 47, D900–D908. [Google Scholar] [CrossRef] [Green Version]
- Hu, J.; Qiu, D.; Yu, A.; Hu, J.; Deng, H.; Li, H.; Yi, Z.; Chen, J.; Zu, X. Ythdf1 Is a Potential Pan-Cancer Biomarker for Prognosis and Immunotherapy. Front. Oncol. 2021, 11, 607224. [Google Scholar] [CrossRef]
- Yuan, Q.; Sun, N.; Zheng, J.; Wang, Y.; Yan, X.; Mai, W.; Liao, Y.; Chen, X. Prognostic and Immunological Role of Fun14 Domain Containing 1 in Pan-Cancer: Friend or Foe? Front. Oncol. 2019, 9, 1502. [Google Scholar] [CrossRef] [PubMed]
- Li, T.; Fan, J.; Wang, B.; Traugh, N.; Chen, Q.; Liu, J.S.; Li, B.; Liu, X.S. Timer: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 2017, 77, e108–e110. [Google Scholar] [CrossRef]
- Thul, P.J.; Lindskog, C. The Human Protein Atlas: A Spatial Map of the Human Proteome. Protein Sci. 2018, 27, 233–244. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Asplund, A.; Edqvist, P.H.; Schwenk, J.M.; Ponten, F. Antibodies for Profiling the Human Proteome-the Human Protein Atlas as a Resource for Cancer Research. Proteomics 2012, 12, 2067–2077. [Google Scholar] [CrossRef] [PubMed]
- Uhlen, M.; Zhang, C.; Lee, S.; Sjostedt, E.; Fagerberg, L.; Bidkhori, G.; Benfeitas, R.; Arif, M.; Liu, Z.; Edfors, F.; et al. A Pathology Atlas of the Human Cancer Transcriptome. Science 2017, 357, eaan2507. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yi, J.; Zhong, W.; Wu, H.; Feng, J.; Zouxu, X.; Huang, X.; Li, S.; Shuang, Z. Identification of Key Genes Affecting the Tumor Microenvironment and Prognosis of Triple-Negative Breast Cancer. Front. Oncol. 2021, 11, 746058. [Google Scholar] [CrossRef]
- Gentles, A.J.; Newman, A.M.; Liu, C.L.; Bratman, S.V.; Feng, W.; Kim, D.; Nair, V.S.; Xu, Y.; Khuong, A.; Hoang, C.D.; et al. The Prognostic Landscape of Genes and Infiltrating Immune Cells across Human Cancers. Nat. Med. 2015, 21, 938–945. [Google Scholar] [CrossRef] [Green Version]
- Newman, A.M.; Liu, C.L.; Green, M.R.; Gentles, A.J.; Feng, W.; Xu, Y.; Hoang, C.D.; Diehn, M.; Alizadeh, A.A. Robust Enumeration of Cell Subsets from Tissue Expression Profiles. Nat. Methods 2015, 12, 453–457. [Google Scholar] [CrossRef] [Green Version]
- Tang, Z.; Li, C.; Kang, B.; Gao, G.; Li, C.; Zhang, Z. Gepia: A Web Server for Cancer and Normal Gene Expression Profiling and Interactive Analyses. Nucleic Acids Res. 2017, 45, W98–W102. [Google Scholar] [CrossRef] [Green Version]
- Charoentong, P.; Finotello, F.; Angelova, M.; Mayer, C.; Efremova, M.; Rieder, D.; Hackl, H.; Trajanoski, Z. Pan-Cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep. 2017, 18, 248–262. [Google Scholar] [CrossRef] [Green Version]
- Jiang, P.; Gu, S.; Pan, D.; Fu, J.; Sahu, A.; Hu, X.; Li, Z.; Traugh, N.; Bu, X.; Li, B.; et al. Signatures of T Cell Dysfunction and Exclusion Predict Cancer Immunotherapy Response. Nat. Med. 2018, 24, 1550–1558. [Google Scholar] [CrossRef]
- Lamb, J. Innovation—The Connectivity Map: A New Tool for Biomedical Research. Nat. Rev. Cancer 2007, 7, 54–60. [Google Scholar] [CrossRef]
- Deng, M.; Xiong, C.; He, Z.K.; Bin, Q.; Song, J.Z.; Li, W.; Qin, J. Mcts1 as a Novel Prognostic Biomarker and Its Correlation with Immune Infiltrates in Breast Cancer. Front. Genet. 2022, 13, 825901. [Google Scholar] [CrossRef] [PubMed]
- Li, T.H.; Qin, C.; Zhao, B.B.; Cao, H.T.; Yang, X.Y.; Wang, Y.Y.; Li, Z.R.; Zhou, X.T.; Wang, W.B. Identification Mettl18 as a Potential Prognosis Biomarker and Associated with Immune Infiltrates in Hepatocellular Carcinoma. Front. Oncol. 2021, 11, 665192. [Google Scholar] [CrossRef] [PubMed]
- Jo, S.; Lockridge, A.; Mohan, R.; Esch, N.; Schlichting, R.; Panigrahy, N.; Essawy, A.; Gustafson, E.; Alejandro, E.U. Translational Factor Eif4g1 Regulates Glucose Homeostasis and Pancreatic Beta-Cell Function. Diabetes 2021, 70, 155–170. [Google Scholar] [CrossRef]
- Preston, S.E.J.; Bartish, M.; Richard, V.R.; Aghigh, A.; Goncalves, C.; Smith-Voudouris, J.; Huang, F.; Thebault, P.; Cleret-Buhot, A.; Lapointe, R.; et al. Phosphorylation of Eif4e in the Stroma Drives the Production and Spatial Organisation of Collagen Type I in the Mammary Gland. Matrix Biol. 2022, 111, 264–288. [Google Scholar] [CrossRef] [PubMed]
- Loi, S.; Michiels, S.; Adams, S.; Loibl, S.; Budczies, J.; Denkert, C.; Salgado, R. The Journey of Tumor-Infiltrating Lymphocytes as a Biomarker in Breast Cancer: Clinical Utility in an Era of Checkpoint Inhibition. Ann. Oncol. 2021, 32, 1236–1244. [Google Scholar] [CrossRef]
- Denkert, C.; von Minckwitz, G.; Darb-Esfahani, S.; Lederer, B.; Heppner, B.I.; Weber, K.E.; Budczies, J.; Huober, J.; Klauschen, F.; Furlanetto, J.; et al. Tumour-Infiltrating Lymphocytes and Prognosis in Different Subtypes of Breast Cancer: A Pooled Analysis of 3771 Patients Treated with Neoadjuvant Therapy. Lancet Oncol. 2018, 19, 40–50. [Google Scholar] [CrossRef] [PubMed]
- Maibach, F.; Sadozai, H.; Seyed Jafari, S.M.; Hunger, R.E.; Schenk, M. Tumor-Infiltrating Lymphocytes and Their Prognostic Value in Cutaneous Melanoma. Front. Immunol. 2020, 11, 2105. [Google Scholar] [CrossRef]
- Quail, D.F.; Joyce, J.A. Microenvironmental Regulation of Tumor Progression and Metastasis. Nat. Med. 2013, 19, 1423–1437. [Google Scholar] [CrossRef] [PubMed]
- Boelens, M.C.; Wu, T.J.; Nabet, B.Y.; Xu, B.; Qiu, Y.; Yoon, T.; Azzam, D.J.; Twyman-Saint Victor, C.; Wiemann, B.Z.; Ishwaran, H.; et al. Exosome Transfer from Stromal to Breast Cancer Cells Regulates Therapy Resistance Pathways. Cell 2014, 159, 499–513. [Google Scholar] [CrossRef] [Green Version]
- Pivot, X.; Villanueva, C.; Chaigneau, L.; Nguyen, T.; Demarchi, M.; Maurina, T.; Stein, U.; Borg, C. Ixabepilone, a Novel Epothilone Analog in the Treatment of Breast Cancer. Expert Opin. Investig. Drugs 2008, 17, 593–599. [Google Scholar] [CrossRef]
- Larkin, J.M.G.; Kaye, S.B. Epothilones in the Treatment of Cancer. Expert Opin. Investig. Drugs 2006, 15, 691–702. [Google Scholar] [CrossRef] [PubMed]
- Lewinn, E.B. Cardiac Glycosides and Breast Cancer. Lancet 1979, 1, 1196–1197. [Google Scholar] [CrossRef] [PubMed]
- Vaklavas, C.; Chatzizisis, Y.S.; Tsimberidou, A.M. Common Cardiovascular Medications in Cancer Therapeutics. Pharmacol. Ther. 2011, 130, 177–190. [Google Scholar] [CrossRef] [PubMed]
Clinical Features | TCGA (n = 1083) | GSE42568 (n = 104) | GSE88770 (n = 117) |
---|---|---|---|
OS Alive Dead | 933 (86.15%) 150 (13.85%) | 69 (66.35%) 35 (33.65%) | 89 (76.07%) 28 (23.93%) |
Age ≤58 >58 | 545 (50.32%) 538 (49.68%) | 56 (53.85%) 48 (46.15%) | - - |
Grade G1 G2 G3 | - - - | 11 (10.58%) 40 (38.46%) 53 (50.96%) | 13 (11.11%) 96 (82.05%) 7 (5.98%) |
PR status Positive Negative Indeterminate | 688 (63.53%) 342 (31.58%) 4 (0.37%) | - - - | 79 (67.52%) 37 (31.62%) |
ER status Positive Negative Indeterminate | 795 (73.41%) 238 (21.98%) 2 (0.18%) | - - - | 106 (90.60%) 11 (9.40%) - |
HER2 status Positive Negative Indeterminate | 161 (14.87%) 557 (51.43%) 12 (1.11%) | - - - | 7 (5.98%) 108 (92.31%) - |
Stage Stage I Stage II Stage III Stage IV Stage X | 182 (16.81%) 613 (56.60%) 247 (22.81%) 19 (1.75%) 14 (1.29%) | - - - - - | - - - - - |
T stage T1 T2 T3 T4 | 279 (25.76%) 624 (57.62%) 138 (12.74%) 39 (3.60%) | - - - - | - - - - |
N stage N0 N1 N2 N3 | 512 (47.28%) 356 (32.87%) 119 (10.99%) 76 (7.02%) | - - - - | - - - - |
M stage M0 M1 | 901 (83.19%) 21 (1.94%) | - - | - - |
Rank | Score | Name | Description | Target |
---|---|---|---|---|
8549 | −94.63 | KIN001-220 | Aurora kinase inhibitor | AURKA |
8534 | −79.24 | Digitoxigenin | ATPase inhibitor | ATP1A1 |
8530 | −71.17 | Epothilone | Microtubule inhibitor | TUBA1A, TUBA1B, TUBA1C, TUBA3C, TUBA4A, TUBA8, TUBB, TUBB1, TUBB3, TUBB4A, TUBB4B |
8529 | −69.93 | Dihydro-7-desacetyldeoxygedunin | HSP inhibitor | HSP90AA1 |
8528 | −67.25 | Fludrocortisone | Glucocorticoid receptor agonist | NR3C2, AR, NR3C1 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Li, K.; Tan, G.; Zhang, X.; Lu, W.; Ren, J.; Si, Y.; Adu-Gyamfi, E.A.; Li, F.; Wang, Y.; Xie, B.; et al. EIF4G1 Is a Potential Prognostic Biomarker of Breast Cancer. Biomolecules 2022, 12, 1756. https://doi.org/10.3390/biom12121756
Li K, Tan G, Zhang X, Lu W, Ren J, Si Y, Adu-Gyamfi EA, Li F, Wang Y, Xie B, et al. EIF4G1 Is a Potential Prognostic Biomarker of Breast Cancer. Biomolecules. 2022; 12(12):1756. https://doi.org/10.3390/biom12121756
Chicago/Turabian StyleLi, Kun, Guangqing Tan, Xin Zhang, Weiyu Lu, Jingyi Ren, Yuewen Si, Enoch Appiah Adu-Gyamfi, Fangfang Li, Yingxiong Wang, Biao Xie, and et al. 2022. "EIF4G1 Is a Potential Prognostic Biomarker of Breast Cancer" Biomolecules 12, no. 12: 1756. https://doi.org/10.3390/biom12121756