Role of Fibroblast Growth Factor Pathway Receptor Genes in Breast Cancer
DOI:
https://doi.org/10.11113/mjfas.v21n1.3691Keywords:
FGF pathways, breast cancer, clinical information, Kaplan-Meier test, Chi-Square test, BRB array, IBM SPSS, FGFR1, FGFR1OP2, FGF2, FGFR2, cancer progression.Abstract
This study investigates the role of the Fibroblast Growth Factor (FGF) Pathway in breast cancers at the RNA expression level. Key cancer-related genes within the FGF pathway were analysed using datasets containing RNA and clinical information for breast cancer patients. The study involved 266, 289, and 2089 patient samples across different datasets. Various statistical tests, including Kaplan-Meier Test, Chi-Square Test, overall survival, and disease-free survival analysis, were conducted using tools such as BRB array and IBM SPSS Statistics. Associations between RNA expression and clinicopathological features were identified, such as FGFR1 being linked to early-stage grades and FGFR1OP associated with late-stage grades. Expression patterns of specific genes were also correlated with different cancer statuses. Surprisingly, survival analysis revealed contradictory findings, with FGFR1OP2 and FGF2 associated with poor overall survival, FGFR2 with good survival, and FGFR1OP2 linked to poor disease-free survival in breast cancer. These inconsistencies emphasize the necessity of additional study to better understand the dual roles of FGF pathway genes in cancer progression.
References
Lord Angahar. (2017, July). An overview of breast cancer epidemiology, risk factors, pathophysiology, and cancer risks reduction. MOJ Biology and Medicine, 1, Article 00019. https://doi.org/10.15406/mojbm.2017.01.00019
Scully, O. J., Bay, B.-H., Yip, G., & Yu, Y. (2012, September). Breast cancer metastasis. Cancer Genomics & Proteomics, 9(5), 311–320. https://cgp.iiarjournals.org/content/9/5/311
Boutry, J., et al. (2022, January). The evolution and ecology of benign tumors. Biochimica et Biophysica Acta - Reviews on Cancer, 1877(1), 188643. https://doi.org/10.1016/j.bbcan.2021.188643
Folkman, J. (1976). The vascularization of tumors. Scientific American, 234(5), 58–73. https://www.jstor.org/stable/24950351
Zhang, C. (2021). A brief research progress of breast cancer. E3S Web of Conferences, 308, 02022. https://doi.org/10.1051/e3sconf/202130802022
Torre, L. A., Islami, F., Siegel, R. L., Ward, E. M., & Jemal, A. (2017, April). Global cancer in women: Burden and trends. Cancer Epidemiology, Biomarkers & Prevention, 26(4), 444–457. https://doi.org/10.1158/1055-9965.EPI-16-0858
Xu, S., et al. (2021, May). The global, regional, and national burden and trends of breast cancer from 1990 to 2019: Results from the Global Burden of Disease Study 2019. Frontiers in Oncology, 11, 689562. https://doi.org/10.3389/fonc.2021.689562
DeSantis, C. E., et al. (2019). Breast cancer statistics, 2019. CA: A Cancer Journal for Clinicians, 69(6), 438–451. https://doi.org/10.3322/caac.21583
Vaida, M. L., et al. (2024, May 15). Identifying robust biomarker panels for breast cancer screening. Preprints. https://doi.org/10.20944/preprints202405.0996.v1
Islami, F., Siegel, R. L., & Jemal, A. (2020, October). The changing landscape of cancer in the USA — opportunities for advancing prevention and treatment. Nature Reviews Clinical Oncology, 17(10), 631–649. https://doi.org/10.1038/s41571-020-0378-y
Bhardwaj, P. V., Gupta, S., Elyash, A., & Teplinsky, E. (2024, January). Male breast cancer: A review on diagnosis, treatment, and survivorship. Current Oncology Reports, 26(1), 34–45. https://doi.org/10.1007/s11912-023-01489-z
Chandrasekar, S. A., et al. (2023, August). Implications of toll-like receptors (TLRs) and their signaling mechanisms in human cancers. Pathology - Research and Practice, 248, 154673. https://doi.org/10.1016/j.prp.2023.154673
Youssef, S., Abd El-Moneim, E., & Elsayed, H. (2024, March). The relationship between reproductive factors and risk of breast cancer among pre-menopausal women. Helwan International Journal of Nursing Research and Practice, 3(5), 468–486. https://doi.org/10.21608/hijnrp.2024.265284.1123
Assi, H. A., Khoury, K. E., Dbouk, H., Khalil, L. E., Mouhieddine, T. H., & El Saghir, N. S. (2013, June). Epidemiology and prognosis of breast cancer in young women. Journal of Thoracic Disease, 5(Suppl 1), S2–S8. https://doi.org/10.3978/j.issn.2072-1439.2013.05.24
Kelsey, J. L., & Horn-Ross, P. L. (1993, January). Breast cancer: Magnitude of the problem and descriptive epidemiology. Epidemiologic Reviews, 15(1), 7–16. https://doi.org/10.1093/oxfordjournals.epirev.a036118
Lei, S., et al. (2021). Global patterns of breast cancer incidence and mortality: A population-based cancer registry data analysis from 2000 to 2020. Cancer Communications, 41(11), 1183–1194. https://doi.org/10.1002/cac2.12207
McPherson, K., Steel, C. M., & Dixon, J. M. (2000, September). ABC of breast diseases: Breast cancer—epidemiology, risk factors, and genetics. BMJ, 321(7261), 624. https://doi.org/10.1136/bmj.321.7261.624
Jatoi, I., & Miller, A. B. (2003, April). Why is breast-cancer mortality declining? Lancet Oncology, 4(4), 251–254. https://doi.org/10.1016/S1470-2045(03)01037-4
Duffy, S. W., Tabar, L., Vitak, B., & Warwick, J. (2006). Tumor size and breast cancer detection: What might be the effect of a less sensitive screening tool than mammography? Breast Journal, 12(s1), S91–S95. https://doi.org/10.1111/j.1075-122X.2006.00207.x
Siu, A. L., & on behalf of the U.S. Preventive Services Task Force. (2016, February). Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Annals of Internal Medicine, 164(4), 279–296. https://doi.org/10.7326/M15-2886
Hulka, B. S., & Moorman, P. G. (2008, September). Reprint of breast cancer: Hormones and other risk factors. Maturitas, 61(1), 203–213. https://doi.org/10.1016/j.maturitas.2008.11.016
Kelsey, J. L., & Bernstein, L. (1996, May). Epidemiology and prevention of breast cancer. Annual Review of Public Health, 17, 47–67. https://doi.org/10.1146/annurev.pu.17.050196.000403
Pócza, T., Grolmusz, V. K., Papp, J., Butz, H., Patócs, A., & Bozsik, A. (2021, April). Germline structural variations in cancer predisposition genes. Frontiers in Genetics, 12, 634217. https://doi.org/10.3389/fgene.2021.634217
Pastinen, T., Ge, B., & Hudson, T. J. (2006, April). Influence of human genome polymorphism on gene expression. Human Molecular Genetics, 15(suppl_1), R9–R16. https://doi.org/10.1093/hmg/ddl044
Retnomawarti, R., Panigoro, S. S., & Paramita, R. I. (2023, June). The bioinformatics application in detecting germline and somatic variants towards breast cancer using next generation sequencing. Journal of Applied Science, Engineering, and Technology Education, 5(1), 25–34. https://doi.org/10.35877/454RI.asci1608
Itoh, N., & Ornitz, D. M. (2008). Functional evolutionary history of the mouse Fgf gene family. Developmental Dynamics, 237(1), 18–27. https://doi.org/10.1002/dvdy.21388
Forsten-Williams, K., Chua, C. C., & Nugent, M. A. (2005, April). The kinetics of FGF-2 binding to heparan sulfate proteoglycans and MAP kinase signaling. Journal of Theoretical Biology, 233(4), 483–499. https://doi.org/10.1016/j.jtbi.2004.10.020
Kopfstein, L., & Christofori, G. (2006). Metastasis: Cell-autonomous mechanisms versus contributions by the tumor microenvironment. Volume 63.
Katoh, M., & Nakagama, H. (2014). FGF receptors: Cancer biology and therapeutics. Medical Research Reviews, 34(2), 280–300. https://doi.org/10.1002/med.21288
Ornitz, D. M., & Itoh, N. (2015). The fibroblast growth factor signaling pathway. WIREs Developmental Biology, 4(3), 215–266. https://doi.org/10.1002/wdev.176
Masliah-Planchon, J., Garinet, S., & Pasmant, E. (2015, December). RAS-MAPK pathway epigenetic activation in cancer: miRNAs in action. Oncotarget, 7(25), 38892–38907. https://doi.org/10.18632/oncotarget.6476
Kang, D.-S., Yang, Y. R., Lee, C., Kim, S., Ryu, S. H., & Suh, P.-G. (2016, January). Roles of phosphoinositide-specific phospholipase Cγ1 in brain development. Advances in Biological Regulation, 60, 167–173. https://doi.org/10.1016/j.jbior.2015.10.002
Lim, C. P., & Cao, X. (2006, October). Structure, function, and regulation of STAT proteins. Molecular Biosystems, 2(11), 536–550. https://doi.org/10.1039/B606246F
Tanner, Y., & Grose, R. P. (2016, May). Dysregulated FGF signaling in neoplastic disorders. Seminars in Cell & Developmental Biology, 53, 126–135. https://doi.org/10.1016/j.semcdb.2015.10.012
Korc, M., & Friesel, R. E. (2009, August). The role of fibroblast growth factors in tumor growth. Current Cancer Drug Targets, 9(5), 639–651. https://doi.org/10.2174/156800909789057006
Santolla, M. F., & Maggiolini, M. (2020, October). The FGF/FGFR system in breast cancer: Oncogenic features and therapeutic perspectives. Cancers, 12(10), Article 1029. https://doi.org/10.3390/cancers12103029
Ratti, M., et al. (2020, June). MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) as new tools for cancer therapy: First steps from bench to bedside. Targeted Oncology, 15(3), 261–278. https://doi.org/10.1007/s11523-020-00717-x
Falls, J. G., Pulford, D. J., Wylie, A. A., & Jirtle, R. L. (1999, March). Genomic imprinting: Implications for human disease. American Journal of Pathology, 154(3), 635–647. https://doi.org/10.1016/S0002-9440(10)65309-6
Marcucci, G., Mrózek, K., Radmacher, M. D., Bloomfield, C. D., & Croce, C. M. (2009, June). MicroRNA expression profiling in acute myeloid and chronic lymphocytic leukaemias. Best Practice & Research Clinical Haematology, 22(2), 239–248. https://doi.org/10.1016/j.beha.2009.05.003
Iorio, M. V., et al. (2005, August). MicroRNA gene expression deregulation in human breast cancer. Cancer Research, 65(16), 7065–7070. https://doi.org/10.1158/0008-5472.CAN-05-1783
Iorio, M. V., et al. (2005). MicroRNA gene expression deregulation in human breast cancer. Cancer Research, 65(16), 7065–7070. https://doi.org/10.1158/0008-5472.CAN-05-1783
Shi, M., & Guo, N. (2009). MicroRNA expression and its implications for the diagnosis and therapeutic strategies of breast cancer. Cancer Treatment Reviews, 35(4), 328–334. https://doi.org/10.1016/j.ctrv.2008.12.002
Austin, P. C., Lee, D. S., & Fine, J. P. (2016). Introduction to the analysis of survival data in the presence of competing risks. Circulation, 133(6), 601–609. https://doi.org/10.1161/CIRCULATIONAHA.115.017719
Carter, C. L., Allen, C., & Henson, D. E. (1989). Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases. Cancer, 63(1), 181–187. https://doi.org/10.1002/1097-0142(19890101)63:1<181::AID-CNCR2820630129>3.0.CO;2-H
Riggio, A. I., Varley, K. E., & Welm, A. L. (2021). The lingering mysteries of metastatic recurrence in breast cancer. British Journal of Cancer, 124(1), 13–26. https://doi.org/10.1038/s41416-020-01161-4
Györffy, B., & Schäfer, R. (2009). Meta-analysis of gene expression profiles related to relapse-free survival in 1,079 breast cancer patients. Breast Cancer Research and Treatment, 118(3), 433–441. https://doi.org/10.1007/s10549-008-0242-8
de By, R. A., et al. (2001). Principles of geographic information systems. ITC Educational Textbook Series, 1, 234. Retrieved April 30, 2024, from http://ndl.ethernet.edu.et/bitstream/123456789/52829/1/14.pdf
Guo, M., Han, C., Guan, Q., Huang, Y., & Xie, Z. (2020). A universal parallel scheduling approach to polyline and polygon vector data buffer analysis on conventional GIS platforms. Transactions in GIS, 24(6), 1630–1654. https://doi.org/10.1111/tgis.12670
West, M., et al. (2001). Predicting the clinical status of human breast cancer by using gene expression profiles. Proceedings of the National Academy of Sciences, 98(20), 11462–11467. https://doi.org/10.1073/pnas.201162998
Cheadle, C., Vawter, M. P., Freed, W. J., & Becker, K. G. (2003). Analysis of microarray data using Z score transformation. Journal of Molecular Diagnostics, 5(2), 73–81. https://doi.org/10.1016/S1525-1578(10)60455-2
Xu, X., Zhao, Y., & Simon, R. (2008). Gene set expression comparison kit for BRB-ArrayTools. Bioinformatics, 24(1), 137–139. https://doi.org/10.1093/bioinformatics/btm541
Simon, R., Lam, A., Li, M.-C., Ngan, M., Menenzes, S., & Zhao, Y. (2007). Analysis of gene expression data using BRB-ArrayTools. Cancer Informatics, 3, 117693510700300022. https://doi.org/10.1177/117693510700300022
Chambers, J. M. (2020). S, R, and data science. Proceedings of the ACM Programming Languages, 4(HOPL), 84:1–84:17. https://doi.org/10.1145/3386334
Brun, R., & Rademakers, F. (1997). ROOT — An object oriented data analysis framework. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 389(1), 81–86. https://doi.org/10.1016/S0168-9002(97)00048-X
Wang, S. (2010). A CyberGIS framework for the synthesis of cyberinfrastructure, GIS, and spatial analysis. Annals of the Association of American Geographers, 100(3), 535–557. https://doi.org/10.1080/00045601003791243
Msaouel, P., Lee, J., Karam, J. A., & Thall, P. F. (2022). A causal framework for making individualized treatment decisions in oncology. Cancers, 14(16), Article 63923. https://doi.org/10.3390/cancers14163923
Dey, J. H. (2010). Investigating the contribution of FGF receptors in breast tumorigenesis using models of mammary cancer (Thesis, University of Basel). https://doi.org/10.5451/unibas-005213494
Huang, G.-W., et al. (2018). A three-lncRNA signature predicts overall survival and disease-free survival in patients with esophageal squamous cell carcinoma. BMC Cancer, 18(1), 147. https://doi.org/10.1186/s12885-018-4058-6
Beckmann, M. W., Niederacher, D., Schnürch, H.-G., Gusterson, B. A., & Bender, H. G. (1997). Multistep carcinogenesis of breast cancer and tumor heterogeneity. Journal of Molecular Medicine, 75(6), 429–439. https://doi.org/10.1007/s001090050128
Deyrup, A. T., Tighiouart, M., Montag, A. G., & Weiss, S. W. (2008). Epithelioid hemangioendothelioma of soft tissue: A proposal for risk stratification based on 49 cases. American Journal of Surgical Pathology, 32(6), 924. https://doi.org/10.1097/PAS.0b013e31815bf8e6
Vernuccio, F., et al. (2021). Benign and malignant mimickers of infiltrative hepatocellular carcinoma: Tips and tricks for differential diagnosis on CT and MRI. Clinical Imaging, 70, 33–45. https://doi.org/10.1016/j.clinimag.2020.10.011
Fields, R. C., et al. (2007). Surgical resection of the primary tumor is associated with increased long-term survival in patients with stage IV breast cancer after controlling for site of metastasis. Annals of Surgical Oncology, 14(12), 3345–3351. https://doi.org/10.1245/s10434-007-9527-0
Wiedlocha, A., Haugsten, E. M., & Zakrzewska, M. (2021). Roles of the FGF-FGFR signaling system in cancer development and inflammation. Cells, 10(9), Article 9231. https://doi.org/10.3390/cells10092231
Agrawal, S., Maity, S., AlRaawi, Z., Al-Ameer, M., & Kumar, T. K. S. (2021). Targeting drugs against fibroblast growth factor(s)-induced cell signaling. Current Drug Targets, 22(2), 214–240. https://doi.org/10.2174/1389450121999201012201926
Piasecka, D., et al. (2019). FGFs/FGFRs-dependent signaling in regulation of steroid hormone receptors – implications for therapy of luminal breast cancer. Journal of Experimental & Clinical Cancer Research, 38(1), 230. https://doi.org/10.1186/s13046-019-1236-6
Haugsten, E. M., Wiedlocha, A., Olsnes, S., & Wesche, J. (2010). Roles of fibroblast growth factor receptors in carcinogenesis. Molecular Cancer Research, 8(11), 1439–1452. https://doi.org/10.1158/1541-7786.MCR-10-0168
Grand, E. K., et al. (2004). Identification of a novel gene, FGFR1OP2, fused to FGFR1 in 8p11 myeloproliferative syndrome. Genes, Chromosomes & Cancer, 40(1), 78–83. https://doi.org/10.1002/gcc.20023
Wesche, J., Haglund, K., & Haugsten, E. M. (2011). Fibroblast growth factors and their receptors in cancer. Biochemical Journal, 437(2), 199–213. https://doi.org/10.1042/BJ20101603
Barclay, C., et al. (2005). Basic fibroblast growth factor (FGF-2) overexpression is a risk factor for esophageal cancer recurrence and reduced survival, which is ameliorated by coexpression of the FGF-2 antisense gene. Clinical Cancer Research, 11(21), 7683–7691. https://doi.org/10.1158/1078-0432.CCR-05-0771
Shee, K., et al. (2018). Therapeutically targeting tumor microenvironment–mediated drug resistance in estrogen receptor–positive breast cancer. Journal of Experimental Medicine, 215(3), 895–910. https://doi.org/10.1084/jem.20171818
Shi, Z., Jiao, S., & Zhou, Z. (2016). STRIPAK complexes in cell signaling and cancer. Oncogene, 35(35), 4549–4557. https://doi.org/10.1038/onc.2016.9
Cihoric, N., et al. (2014). Prognostic role of FGFR1 amplification in early-stage non-small cell lung cancer. British Journal of Cancer, 110(12), 2914–2922. https://doi.org/10.1038/bjc.2014.229
Katoh, M., & Nakagama, H. (2014). FGF receptors: Cancer biology and therapeutics. Medical Research Reviews, 34(2), 280–300. https://doi.org/10.1002/med.21288
Clark, R., et al. (2013). Comparative genomic and proteomic analysis of cytoskeletal changes in dexamethasone-treated trabecular meshwork cells. Molecular & Cellular Proteomics, 12(1), 194–206. https://doi.org/10.1074/mcp.M112.019745
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