Analyzing genetic and epigenetic HORMAD alterations in breast cancer resistance and metastatic events

Abstract

Epigenetic alterations in regulatory genes, genetic factors, and genomic instability, which cause breast cancer, can also contribute to disease resistance. HORMAD , which encode proteins containing HORMA domains and are involved in homologous recombination, have important roles in cancer emergence and progression. In this study, we uncovered putative breast cancer therapeutic targets by examining HORMAD1 and HORMAD2 genetic and epigenetic alterations. mRNA levels of HORMAD1 and HORMAD2 in breast cancer samples and normal breast tissues, as well as mRNA levels in normal, breast cancer, and metastatic breast cancer samples, were analyzed using TNMplot. Prognostic value, genetic alterations, epigenetic alterations, genetic variations, ROC plots, functional prediction, and immune infiltration of HORMAD1 and HORMAD2 were conducted with KMPlotter, cBioportal, methsurv, ClinVar, ROC Plotter, PredictSNP, PANTHER, and TIMER 2.0, respectively. Both HORMAD1 and HORMAD2 mRNA levels were lower in breast cancer samples, and lower in metastatic breast cancer samples. Patients expressing higher HORMAD1 and HORMAD2 levels had favorable overall survival (OS) rates than the opposite groups. HORMAD1 and HORMAD2 gene amplifications and deletions were also observed. Pathway enrichment analyses showed that Wnt signaling alterations contributed to cell proliferation. Increased DNA methylation levels were identified in HORMAD2 when compared with HORMAD1 in patients. Two 1021C>T (Q334) and 430A>G (T144A) variants of HORMAD1 were shown to have clinical significance in patients. Also, functional prediction mutant analysis of HORMAD1 confirmed that S287F exerted a deleterious effect on amino acid impact, however, further investigations are warranted. Receiver operating characteristic (ROC) plot data indicated a significant correlation between HORMAD2 levels and anti-human epidermal growth factor receptor 2 (HER2) sensitivity. Genetic and epigenetic changes in HORMAD1 and HORMAD2 genes may be used as indicators and targets for overcoming breast cancer resistance and limiting metastasis in breast cancer cells via Wnt targeting. Further research is required to verify our findings.

Keywords

• HORMAD
• breast cancer resistance
• metastasis
• bioinformatics
• genetic and epigenetic alterations

References

1. [1] Wilkinson L, Gathani T. Understanding breast cancer as a global health concern. Br J Radiol. 2022; 95(1130): 20211033. https://doi.org./10.1259/bjr.20211033
2. [2] Hartkopf AD, Grischke EM, Brucker SY. Endocrine-resistant breast cancer: Mechanisms and treatment. Breast Care (Basel). 2020; 15(4): 347-354. https://doi.org./10.1159/000508675
3. [3] Ramos A, Sadeghi S, Tabatabaeian H. Battling chemoresistance in cancer: Root causes and strategies to uproot them. Int J Mol Sci. 2021; 22(17):9451. https://doi.org./10.3390/ijms22179451.
4. [4] Riggio AI, Varley KE, Welm AL. The lingering mysteries of metastatic recurrence in breast cancer. Br J Cancer. 2021; 124(1): 13-26. https://doi.org./10.1038/s41416-020-01161-4
5. [5] Rimawi MF, De Angelis C, Schiff R. Resistance to Anti-HER2 Therapies in Breast Cancer. Am Soc Clin Oncol Educ Book. 2015 (35): e157-e164. https://doi.org./10.14694/EdBook_AM.2015.35.e157
6. [6] Fath MK, Azargoonjahromi A, Kiani A, Jalalifar F, Osati P, Oryani MA, Shakeri F, Nasirzadeh F, Khalesi B, Nabi Afjadi M, Zalpoor H, Mard-Soltani M, Zahra Payandeh Z. The role of epigenetic modifications in drug resistance and treatment of breast cancer. Cell Mol Biol Lett. 2022; 27(1): 52. https://doi.org./10.1186/s11658-022-00344-6.
7. [7] Li A, Schleicher SM, Andre F, Mitri ZI. Genomic alteration in metastatic breast cancer and ıts treatment. Am Soc Clin Oncol Educ Book. 2020 (40): 30-43. https://doi.org./10.1200/EDBK_280463
8. [8] Liu K, Wang Y, Zhu Q, Li P, Chen J, Tang Z, Shen Y, Cheng X, Lu LY, Liu Y. Aberrantly expressed HORMAD1 disrupts nuclear localization of MCM8-MCM9 complex and compromises DNA mismatch repair in cancer cells. Cell Death Dis. 2020 ;11(7): 519. https://doi.org./10.1038/s41419-020-2736-1.

9. [9] Shin YH, Choi Y, Erdin SU, Yatsenko SA, Kloc M, Yang F, Wang JP, Meistrich ML, Rajkovic A. Hormad1 mutation disrupts synaptonemal complex formation, recombination, and chromosome segregation in mammalian meiosis. PLoS Genet. 2010; 6(11): e1001190. https://doi.org./10.1371/journal.pgen.1001190
10. [10] Daniel K, Lange J, Hached K, Fu J, Anastassiadis K, Roig I, Cooke HJ, Stewart AF, Wassmann K, Jasin M, Keeney S, Toth A. Meiotic homologue alignment and its quality surveillance are controlled by mouse HORMAD1. Nat Cell Biol. 2011; 13(5): 599-610. https://doi.org./10.1038/ncb2213
11. [11] Liu M, Chen J, Hu L, Shi X, Zhou Z, Hu Z, Sha J. HORMAD2/CT46.2, a novel cancer/testis gene, is ectopically expressed in lung cancer tissues. Mol Hum Reprod. 2012; 18(12): 599-604. https://doi.org./10.1093/molehr/gas033
12. [12] Watkins J, Weekes D, Shah V, Gazinska P, Joshi S, Sidhu B, Gillet C, Pinder S, Vanoli F, Jasin M, Mayrhofer M, Isaksson A, Cheang MCU, Mirza H, Frankum J, Lord CJ, Ashworth A, Vinayak S, Ford JM, Telli ML, Grigoriadis A, Tutt ANJ. Genomic complexity profiling reveals that HORMAD1 overexpression contributes to homologous recombination deficiency in triple-negative breast cancers. Cancer Discov. 2015; 5(5): 488-505. https://doi.org./10.1158/2159-8290.Cd-14-1092
13. [13] Chen B, Tang H, Chen X, Zhang G, Wang Y, Xie X, Liao N. Transcriptomic analyses identify key differentially expressed genes and clinical outcomes between triple-negative and non-triple-negative breast cancer. Cancer Manag Res. 2019; 11: 179-190. https://doi.org./10.2147/cmar.S187151
14. [14] Wang X, Tan Y, Cao X, Kim JA, Chen T, Hu Y, Wexler M, Wang X. Epigenetic activation of HORMAD1 in basal-like breast cancer: role in Rucaparib sensitivity. Oncotarget. 2018; 9(53): 30115-30227. https://doi.org./10.18632/oncotarget.25728
15. [15] Lin Q, Hou S, Guan F, Lin C. HORMAD2 methylation-mediated epigenetic regulation of gene expression in thyroid cancer. J Cell Mol Med. 2018; 22(10): 4640-4652. https://doi.org/10.1111/jcmm.13680
16. [16] Zhang X, Yu X. Crosstalk between Wnt/β-catenin signaling pathway and DNA damage response in cancer: a new direction for overcoming therapy resistance. Front Pharmacol. 2023; 14:1230822 https://doi.org./10.3389/fphar.2023.1230822
17. [17] Savio AJ, Daftary D, Dicks E, Buchanan DD, Parfrey PS, Young JP, Weisenberger D, Green RC, Gallinger S, McLaughlin JR, Knight JA, Bapat B. Promoter methylation of ITF2, but not APC, is associated with microsatellite instability in two populations of colorectal cancer patients. BMC Cancer. 2016; 16: 113. https://doi.org./10.1186/s12885-016-2149-9
18. [18] Zong B, Sun L, Peng Y, Wang Y, Yu Y, Lei J, Zhang Y, Guo S, Li K, Liu S. HORMAD1 promotes docetaxel resistance in triple negative breast cancer by enhancing DNA damage tolerance. Oncol Rep. 2021; 46(1):138. https://doi.org./10.3892/or.2021.8089
19. [19] El-Botty R, Vacher S, Mainguené J, Briaux A, Ibadioune S, Dahmani A, Montaudon E, Nemati F, Huguet L, Sourd L, Morriset L, Château-Joubert S, Dubois T, Maire V, Lidereau R, Rapinat A, Gentien D, Coussy F, Bièche I, Marangoni E. HORMAD1 overexpression predicts response to anthracycline-cyclophosphamide and survival in triple-negative breast cancers. Mol Oncol. 2023;17(10):2017-2028. https://doi.org./10.1002/1878-0261.13412
20. [20] Lin Q, Hou S, Guan F, Lin C. HORMAD2 methylation-mediated epigenetic regulation of gene expression in thyroid cancer. J Cell Mol Med. 2018; 22(10): 4640-4652. https://doi.org./10.1111/jcmm.13680.
21. [21] Pohl SG, Brook N, Agostino M, Arfuso F, Kumar AP, Dharmarajan A. Wnt signaling in triple-negative breast cancer. Oncogenesis. 2017; 6(4): e310. https://doi.org./10.1038/oncsis.2017.14
22. [22] Liu K, Cheng L, Zhu K, Wang J, Shu Q. The cancer/testis antigen HORMAD1 mediates epithelial–mesenchymal transition to promote tumor growth and metastasis by activating the Wnt/β-catenin signaling pathway in lung cancer. Cell Death Discov. 2022; 8(1): 136. https://doi.org./10.1038/s41420-022-00946-1
23. [23] Motwani J, Rodger EJ, Stockwell PA, Baguley BC, Macaulay EC, Eccles MR. Genome-wide DNA methylation and RNA expression differences correlate with invasiveness in melanoma cell lines. Epigenomics. 2021; 13(8): 577-598. https://doi.org./10.2217/epi-2020-0440
24. [24] Bartha Á, Győrffy B. TNMplot.com: A web tool for the comparison of gene expression in normal, tumor and metastatic tissues. Int J Mol Sci. 2021; 22(5):2622. https://doi.org./10.3390/ijms22052622
25. [25] Győrffy B. Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer. Comput Struct Biotechnol J. 2021; 19: 4101-4109. https://doi.org/10.1016/j.csbj.2021.07.014
26. [26] Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Anders Jacobsen, Caitlin J Byrne, Michael L Heuer, Erik Larsson, Yevgeniy Antipin, Boris Reva, Arthur P Goldberg, Chris Sander, Nikolaus Schultz. The cbio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discovery. 2012; 2(5): 401-404. https://doi.org./10.1158/2159-8290.Cd-12-0095
27. [27] Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, Cerami E, Sander C, Schultz N. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013; 6(269): pl1. https://doi.org./10.1126/scisignal.2004088
28. [28] Modhukur V, Iljasenko T, Metsalu T, Lokk K, Laisk-Podar T, Vilo J. MethSurv: A web tool to perform multivariable survival analysis using DNA methylation data. Epigenomics. 2018; 10(3): 277-288. https://doi.org./10.2217/epi 2017-0118
29. [29] Metsalu T, Vilo J. ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Res. 2015; 43(W1): W566-W570. https://doi.org./10.1093/nar/gkv468
30. [30] Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Jang W, Karapetyan K, Katz K, Liu C, Maddipatla Z, Malheiro A, McDaniel K, Ovetsky M, Riley G, Zhou G, Holmes JB, Kattman BL, Maglott DR. ClinVar: Improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018; 46(D1): D1062-D1067. https://doi.org./10.1093/nar/gkx1153
31. [31] Fekete JT, Győrffy B. ROCplot.org: Validating predictive biomarkers of chemotherapy/hormonal therapy/anti HER2 therapy using transcriptomic data of 3,104 breast cancer patients. Int J Cancer. 2019; 145(11): 3140-3151. https://doi.org./10.1002/ijc.32369
32. [32] Bendl J, Stourac J, Salanda O, Pavelka A, Wieben ED, Zendulka J, Brezovsky J, Damborsky J. PredictSNP: Robust and accurate consensus classifier for prediction of disease-related mutations. PLoS Comput Biol. 2014; 10(1): e1003440. https://doi.org./10.1371/journal.pcbi.1003440
33. [33] Stone EA, Sidow A. Physicochemical constraint violation by missense substitutions mediates impairment of protein function and disease severity. Genom Res. 2005; 15(7): 978-986. https://doi.org./10.1101/gr.3804205
34. [34] Capriotti E, Fariselli P, Calabrese R, Casadio R. Predicting protein stability changes from sequences using support vector machines. Bioinformatics. 2005; 21 (Suppl 2):ii54-58. https://doi.org./10.1093/bioinformatics/bti1109
35. [35] Ramensky V, Bork P, Sunyaev S. Human non‐ synonymous SNPs: server and survey. Nucleic Acids Res. 2002; 30(17): 3894-3900. https://doi.org./10.1093/nar/gkf493
36. [36] Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR. A method and server for predicting damaging missense mutations. Nat Methods. 2010; 7(4): 248-249. https://doi.org./10.1038/nmeth0410-248
37. [37] Sim NL, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 2012; 40(Web Server issue): W452-457. https://doi.org./10.1093/nar/gks539
38. [38] Bromberg Y, Rost B. SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Res. 2007; 35(11): 3823-3835. https://doi.org./10.1093/nar/gkm238
39. [39] Tang H, Thomas PD. PANTHER-PSEP: predicting disease-causing genetic variants using position-specific evolutionary preservation. Bioinformatics. 2016; 32(14): 2230-2232. https://doi.org./10.1093/bioinformatics/btw222
40. [40] Pejaver V, Mooney SD, Radivojac P. Missense variant pathogenicity predictors generalize well across a range of function-specific prediction challenges. Hum Mutat. 2017; 38(9): 1092-1108. https://doi.org./10.1002/humu.23258
41. [41] Shihab HA, Gough J, Cooper DN, Stenson PD, Barker GL, Edwards KJ, Day INM, Gaunt TR. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum Mutat. 2013; 34(1): 57-65. https://doi.org./10.1002/humu.22225
42. [42] Cheng J, Randall A, Baldi P. Prediction of protein stability changes for single-site mutations using support vector machines. Proteins. 2006; 62(4): 1125-1132. https://doi.org./10.1002/prot.20810
43. [43] Capriotti E, Calabrese R, Casadio R. Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics. 2006; 22(22): 2729 2734. https://doi.org./10.1093/bioinformatics/btl423
44. [44] Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, Li B, Liu XS. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020; 48(W1): W509-w514. https://doi.org./10.1093/nar/gkaa407