The Association Between TP53 Mutaions and TGF-β Signaling Pathway Copy Number Alterations in Breast Cancer: A Multivariate Survival Analysis Using TCGA Data TP53 Mutations and TGF-β Signaling Pathway
Article Sidebar
Main Article Content
Abstract
This study aimed to investigate the association between TP53 gene mutations and copy number variations (CNAs) in certain TGF-β pathway genes and the impact overall survival in patients, using TCGA data (number of patients = 479). Fisher's exact tests revealed a statistically significant association between Tp53 mutation and TGF_β pathway variant {odds ratio = 4.44, 95% confidence interval: 1.61–12.27, p = 0.005}. Fisher's exact tests revealed a statistically significant association among Tp53 mutation and TGF_β pathway variant (odds ratio=4.44, 95% confidence intermission: 1.61–12.27, p = 0.005)). Kaplan Meier survival analysis did not show any statistically significant difference in overall survival between patients with TP53 mutations and those without (p=0.692)), nor between those with TGF-β copy number variations and those without ((p = 0.162). When patients were categorized into three groups (no TP53 mutation, TP53 mutation, and TP53 mutation with TGF-β copy number heterozygosity), the log-rank test was also not statistically significant (p = 0.373). Sensitivity analyses by tumor stage yielded consistent (p = 0.691 and p = 0.145, respectively). Multivariable Cox regression confirmed that older age (HR = 1.04 per year, p < 0.001)) and progressive stage (Stage IV vs. I: HR = 5.78, p < 0.001) were independent predictors of inferior OS, while TP53 mutation (HR = 1.08, p = 0.917) and TGF-β CNA (HR=1.38, p = 0.303) were not. In conclusion, although TP53 mutations are significantly associated with TGF-β pathway CNAs, neither alteration independently predicts OS in breast cancer after adjusting for age and stage.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
The Journal of Medical and Oral Biosciences uses a Creative Commons Attribution (CC Attribution-ShareAlike 4.0) and its license of (CC BY-SA 4.0). This license allows the authors to hold ownership of the copyright of their articles.
Attribution-Share Alike 4.0 International License (CC BY-SA 4.0). 
Under the CC BY-SA 4.0 the authors are free to:
Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
No use, distribution or reproduction is permitted which does not comply with these terms.
https://creativecommons.org/licenses/by-sa/4.0/
References
References
1. Gurcan F. Enhancing breast cancer prediction through stacking ensemble and deep learning integration. PeerJ Computer Science. 2025 Feb 3;11: e2461. https://doi.org/10.7717/peerj-cs.2461
2. Munisamy S, Vinayagam MM. ADVANCES IN PHYTOMEDICINE AND THERAPEUTIC APPROACHES FOR EMERGING FRONTIERS IN BREAST CANCER. Biological Sciences Today: Emerging Discoveries and Application. 2025:29.
3. Yersal O, Barutca S. Biological subtypes of breast cancer: Prognostic and therapeutic implications. World journal of clinical oncology. 2014 Aug 10;5(3):412. doi: 10.5306/wjco.v5.i3.412
4. Li Y, Zhang C, Chen J, Tu G, Tang L. FKBP10 expression and TP53 mutation predict prognosis and chemotherapy response in triple-negative breast cancer. Discover Oncology. 2025; 16:1409. https://doi.org/10.1007/s12672-025-03275-8
5. Jing H, Gao Y, Jing L, Yang H, Liu S. Recent advances in therapeutic use of transforming growth factor-beta inhibitors in cancer and fibrosis. Front Oncol. 2025; 15:1489701. https://doi.org/10.3389/fonc.2025.1489701
6. Gottumukkala SB, Ganesan TS, Palanisamy A. Comprehensive molecular interaction map of TGFβ-induced epithelial to mesenchymal transition in breast cancer. NPJ Syst Biol Appl. 2024;10(1):53. DOI: 10.1038/s41540-024-00378-w
7. Hwang SH, Baek SH, Lee MJ, Kook Y, Bae SJ, Ahn SG, et al. Clinical relevance of TP53 mutation and its characteristics in breast cancer with long-term follow-up. Cancers (Basel). 2024;16(23):3899. DOI: 10.3390/cancers16233899
8. Baba AB, Rah B, Bhat GR, Mushtaq I, Parveen S, Hassan R, et al. Transforming growth factor-beta (TGF-β) signaling in cancer: a betrayal within. Front Pharmacol. 2022; 13:791272. DOI: 10.3389/fphar.2022.791272
9. Belizario JE, Loggulo AF. Insights into breast cancer phenotyping through molecular omics approaches and therapy response. Cancer Drug Resist. 2019;2(3):527. doi: 10.20517/cdr.2018.009
10. Waks AG, Winer EP. Breast cancer treatment: a review. JAMA. 2019;321(3):288-300. DOI: 10.1001/jama.2018.19323
11. Smolarz B, Nowak AZ, Romanowicz H. Breast cancer—epidemiology, classification, pathogenesis and treatment: a review. Cancers (Basel). 2022;14(10):2569. DOI: 10.3390/cancers14102569
12. Toi M, Ohashi Y, Seow A, Moriya T, Tse G, Sasano H, et al. Epidemiology, pathology and treatment of breast cancer. Jpn J Clin Oncol. 2010;40(Suppl 1):i13-i18. DOI: 10.1093/jjco/hyq122
13. Kashyap D, Pal D, Sharma R, Garg VK, Goel N, Koundal D, et al. Global increase in breast cancer incidence: risk factors and preventive measures. Biomed Res Int. 2022; 2022:9605439. doi: 10.1155/2022/9605439.
14. Shan R, Dai LJ, Shao ZM, Jiang YZ. Evolving molecular subtyping of breast cancer advances precision treatment. Cancer Biol Med. 2024;21(9):731. doi: 10.20892/j.issn.2095-3941.2024.0222
15. Lopez-Gonzalez L, Sanchez Cendra A, Sanchez Cendra C, Roberts Cervantes D, Cassinello Espinosa J, Pekarek T, et al. Exploring biomarkers in breast cancer: hallmarks of diagnosis, treatment, and follow-up in clinical practice. DOI: 10.3390/medicina60010168
16. Rakha E, Soria D, Green AR, Lemetre C, Powe DG, Nolan CC, et al. Nottingham Prognostic Index Plus (NPI+): a modern clinical decision-making tool in breast cancer. 2014. DOI: 10.1038/bjc.2014.120
17. Gluz O, Hofmann D, Würstlein R, Liedtke C, Nitz U, Harbeck N. Genomic profiling in luminal breast cancer. 2013. DOI: 10.1159/000357535
18. Melhem-Bertrandt A, Bojadzieva J, Ready KJ, Obeid E, Liu DD, Gutierrez-Barrera AM, et al. Early onset HER2-positive breast cancer is associated with germline TP53 mutations. Cancer. 2012;118(4):908-913. DOI: 10.1002/cncr.26377
19. Fu J, Tong Y, Xu Z, Li Y, Zhao Y, Wang T, et al. Impact of TP53 mutations on EGFR-tyrosine kinase inhibitor efficacy and potential treatment strategy. Clin Lung Cancer. 2023;24(1):29-39. DOI: 10.1016/j.cllc.2022.08.007
20. Jasar D, Filipovski V, Kubelka-Sabit K. Clinicopathological and histomorphological association in K-ras mutated colorectal cancer. Virchows Arch. 2022; 481:1-364.
21. Parikh D, Shah M. A comprehensive study on epigenetic signatures to monitor disease progression and response to therapy in breast cancer. Biomed Anal. 2024;1(3):205-217. https://doi.org/10.1016/j.bioana.2024.06.004
22. Zhang Y, Alexander PB, Wang XF. TGF-β family signaling in the control of cell proliferation and survival. Cold Spring Harb Perspect Biol. 2017;9(4):a022145. DOI: 10.1101/cshperspect.a022145
23. Gungor MZ, Uysal M, Senturk S. The bright and dark side of TGF-β signaling in hepatocellular carcinoma: mechanisms, dysregulation, and therapeutic implications. Cancers (Basel). 2022;14(4):940. DOI: 10.3390/cancers14040940
24. Wang X, Zhang L, Si H. Combining luteolin and curcumin synergistically suppresses triple-negative breast cancer by regulating IFN and TGF-β signaling pathways. Biomed Pharmacother. 2024; 178:117221. DOI: 10.1016/j.biopha.2024.117221
25. Syed V. TGF-β signaling in cancer. J Cell Biochem. 2016;117(6):1279-1287. DOI: 10.1002/jcb.25496
26. Sa G, Das T. Anti-cancer effects of curcumin: cycle of life and death. Cell Div. 2008;3(1):14. doi: 10.1186/1747-1028-3-14
27. Hao Y, Baker D, Ten Dijke P. TGF-β-mediated epithelial-mesenchymal transition and cancer metastasis. Int J Mol Sci. 2019;20(11):2767. DOI: 10.3390/ijms20112767
28. Testa U, Castelli G, Pelosi E. Breast cancer: a molecularly heterogeneous disease needing subtype-specific treatments. Med Sci (Basel). 2020;8(1):18. DOI: 10.3390/medsci8010018
29. Gerratana L, Davis AA, Velimirovic M, Clifton K, Hensing WL, Shah AN, et al. Interplay between ESR1/PIK3CA variants and oncogenic pathway alterations in metastatic breast cancer. Breast Cancer Res. 2023;25(1):112. doi: 10.1186/s13058-023-01718-0
30. Sundaramurthy G. A probabilistic approach for automated discovery of biomarkers using expression data from microarray or RNA-Seq datasets [dissertation]. Cincinnati: University of Cincinnati; 2016. doi: 10.1016/j.compbiomed.2015.07.029
31. Lin XY, Guo L, Lin X, Wang Y, Zhang G. Concomitant PIK3CA and TP53 mutations in breast cancer: analysis of clinicopathologic features and prognosis. J Breast Cancer. 2023;26(4):363. doi: 10.4048/jbc.2023.26.e30
32. Ying XH, Zhang KY, Zhu XZ, Jiang SH, Chen L, Li JJ, et al. Integrated genomic profiling identifies predictive biomarkers for neoadjuvant therapy response in breast cancers. 2025. https://doi.org/10.21203/rs.3.rs-7055446/v1
33. Ran R, Ma Y, Wang H, Yang J, Yang J. Treatment strategies for HR+/HER2+ metastatic breast cancer: a review. Front Oncol. 2022; 12:975463. https://doi.org/10.3389/fonc.2022.975463
34. Spugnesi L. Genetic profile of DNA repair genes in triple-negative breast cancer. 2015. https://hdl.handle.net/20.500.14242/130423
35. Zheng Y, Li L, Cai W, Li L, Zhang R, Huang W, et al. Unveiling the role of TGF-β signaling pathway in breast cancer prognosis and immunotherapy. Front Oncol. 2024; 14:1488137. https://doi.org/10.3389/fonc.2024.1488137
36. Menon S. Transcriptomic and metabolic pathway analysis reveals mutated genes p53 and TP53 in cancer. Int J Eng Res Mod Educ. 2021;6(2):10-33. DOI 10.5281/zenodo.5553248
37. Handa S, Chatterjee M, Jan N, Mir MA. The interplay between p53 and the tumor microenvironment in progressing breast cancer. In: p53 in Breast Cancer. CRC Press; 2024. p.263-275. DOI:10.1201/9781003520771-17
38. Ferrari P, Scatena C, Ghilli M, Bargagna I, Lorenzini G, Nicolini A. Molecular mechanisms, biomarkers and emerging therapies for chemotherapy-resistant TNBC. Int J Mol Sci. 2022;23(3):1665. doi: 10.3390/ijms23031665.
39. Chakravarti D, LaBella KA, DePinho RA. Telomeres: history, health, and hallmarks of aging. Cell. 2021;184(2):306-322. DOI: 10.1016/j.cell.2020.12.028
40. Marwah H, Dewangan HK. Synergistic targeting of EGFR, ESR1, BCL2 and TP53 pathways for advanced breast cancer therapy. Curr Cancer Drug Targets. 2025. DOI: 10.2174/0115680096366956250314043513
41. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401-404. doi: 10.1158/2159-8290.CD-12-0095.
42. Dinda S, Sanchez A, Moudgil V. Estrogen-like effects of thyroid hormone on regulation of tumor suppressor proteins p53 and retinoblastoma in breast cancer cells. Oncogene. 2002;21(5):761-768. DOI: 10.1038/sj.onc.1205136
43. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61-70. https://doi.org/10.1038/nature11412
44. Donehower LA, Soussi T, Korkut A, Liu Y, Schultz A, Cardenas M, Li X, Babur O, Hsu TK, Lichtarge O, Weinstein JN. Integrated analysis of TP53 gene and pathway alterations in the cancer genome atlas. Cell reports. 2019 Jul 30;28(5):1370-84. DOI: 10.1016/j.celrep.2019.07.001
45. Meric-Bernstam F, et al. Cancer. 2018;124(6):1190-1198
46. Shahbandi A, et al. J Cell Physiol. 2020;235(10):6535-6550
47. Tang, K. H., Tang, Y. J., & Blankenship, R. E. (2011). Carbon metabolic pathways in phototrophic bacteria and their broader evolutionary implications. Frontiers in Microbiology, 2, 165. https://doi.org/10.3389/fmicb.2011.00165
48. Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503-513. DOI: 10.23637/rothamsted.8v61q
49. Kogure, Y., et al. (2023). Landscape of Genetic Alterations Underlying Hallmark Signature Changes in Cancer Reveals TP53 Aneuploidy–driven Metabolic Reprogramming. Cancer Research Communications, 3(2), 281–296. DOI: 10.1158/2767-9764.CRC-22-0073
50. Sandoval RL, Bottosso M, Tianyu L, et al. TP53-associated early breast cancer: new observations from a large cohort. J Natl Cancer Inst. 2024;116(8):1246-1254. doi:10.1093/jnci/djae074
51. Tapper, W., et al. (2008). The influence of genetic variation in 30 selected genes on the clinical characteristics of early onset breast cancer. Breast Cancer Research, 10(6), R105. DOI: 10.1186/bcr2213
52. Akarlar, G. A., et al. (2025). A Multi-Evidence Framework Rescues Low-Power Prognostic Signals and Rejects Statistical Artifacts in Cancer Genomics. arXiv preprint. https://doi.org/10.48550/arXiv.2510.18571
53. Shukla, N., et al. (2022). TGF-β at the crossroads of multiple prognosis in breast cancer, and beyond. Life Sciences, 310, 121011. DOI: 10.1016/j.lfs.2022.121011