Predicting the Risk of Breast Cancer Recurrence and Metastasis based on miRNA Expression
- Authors: Lv Y.1, Wang Y.2, Zhang Y.1, Chen S.1, Yao Y.1
-
Affiliations:
- School of Mathematics and Statistics, Hainan Normal University
- Department of Pathology, Beidahuang Industry Group General Hospital
- Issue: Vol 19, No 5 (2024)
- Pages: 482-489
- Section: Life Sciences
- URL: https://jdigitaldiagnostics.com/1574-8936/article/view/643922
- DOI: https://doi.org/10.2174/1574893618666230914105741
- ID: 643922
Cite item
Full Text
Abstract
Background:Even after surgery, breast cancer patients still suffer from recurrence and metastasis. Thus, it is critical to predict accurately the risk of recurrence and metastasis for individual patients, which can help determine the appropriate adjuvant therapy.
Methods:The purpose of this study is to investigate and compare the performance of several categories of molecular biomarkers, i.e., microRNA (miRNA), long non-coding RNA (lncRNA), messenger RNA (mRNA), and copy number variation (CNV), in predicting the risk of breast cancer recurrence and metastasis. First, the molecular data (miRNA, lncRNA, mRNA, and CNV) of 483 breast cancer patients were downloaded from the Cancer Genome Atlas, which were then randomly divided into the training and test sets with a ratio of 7:3. Second, the feature selection process was applied by univariate Cox and multivariate Cox variance analysis on the training set (e.g., 15 miRNAs). According to the selected features (e.g., 15 miRNAs), a random forest classifier and several other classification methods were established according to the label of recurrence and metastasis. Finally, the performances of the classification models were compared and evaluated on the test set.
Results:The area under the ROC curve was 0.70 for miRNA, better than those using other biomarkers.
Conclusion:These results indicated that miRNA has important guiding significance in predicting recurrence and metastasis of breast cancer.
About the authors
Yaping Lv
School of Mathematics and Statistics, Hainan Normal University
Email: info@benthamscience.net
Yanfeng Wang
Department of Pathology, Beidahuang Industry Group General Hospital
Email: info@benthamscience.net
Yumeng Zhang
School of Mathematics and Statistics, Hainan Normal University
Email: info@benthamscience.net
Shuzhen Chen
School of Mathematics and Statistics, Hainan Normal University
Email: info@benthamscience.net
Yuhua Yao
School of Mathematics and Statistics, Hainan Normal University
Author for correspondence.
Email: info@benthamscience.net
References
- Abe O, Abe R, Enomoto K, Kikuchi K, Mace-LesecH J. Effects of chemotherapy and hormonal therapy forearly breast cancer on recurrence and 15-year survival: An overview of the randomised trials. Lancet 2005; 2005(365): 1687-717.
- Ferlay J, Héry C, Autier P, Sankaranarayanan R. Global burden of breast cancer. In: Breast Cancer Epidemiology. New York, NY: Springer 2010. doi: 10.1007/978-1-4419-0685-4_1
- Group E, Godwin J. Treatment of Early Breast Cancer. Vol 1: Worldwide Evidence 19851990. Oxford University Press. 1990.
- Rakha EA, Reis-Filho JS, Baehner F, et al. Breast cancer prognostic classification in the molecular era: The role of histological grade. Breast Cancer Res 2010; 12(4): 207. doi: 10.1186/bcr2607 PMID: 20804570
- Rivenbark AG, OConnor SM, Coleman WB. Molecular and cellular heterogeneity in breast cancer: Challenges for personalized medicine. Am J Pathol 2013; 183(4): 1113-24. doi: 10.1016/j.ajpath.2013.08.002 PMID: 23993780
- Walters-Salas ET. The challenge of patient adherence. Bariatr Nurs Surg Patient Care 2012; 7(4): 186-6. doi: 10.1089/bar.2012.9960
- Sun Y, Goodison S, Li J, Liu L, Farmerie W. Improved breast cancer prognosis through the combination of clinical and genetic markers. Bioinformatics 2007; 23(1): 30-7. doi: 10.1093/bioinformatics/btl543 PMID: 17130137
- Gevaert O, Smet FD, Timmerman D, Moreau Y, Moor BD. Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks. Bioinformatics 2006; 22(14): e184-90. doi: 10.1093/bioinformatics/btl230 PMID: 16873470
- Xu X, Zhang Y, Liang Z, Wang M, Ao L. A gene signature for breast cancer prognosis using support vector machine. 5th International Conference on BioMedical Engineering and Informatics. Chongqing, China. 2012.16-18 Oct;
- Garmpis N, Damaskos C, Garmpi A, et al. Molecular classification and future therapeutic challenges of triple-negative breast cancer. In Vivo 2020; 34(4): 1715-27. doi: 10.21873/invivo.11965 PMID: 32606140
- Chen L. Overview of triple negative breast cancer. J Xuzhou Med Uni 2019.
- van de Vijver MJ, He YD, van t Veer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002; 347(25): 1999-2009. doi: 10.1056/NEJMoa021967 PMID: 12490681
- Wang Y, Klijn JG, Yi Z, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2006; 17(9460): 154-5. PMID: 15721472
- Nguyen C, Wang Y, Nguyen HN. Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic. J Biomed Sci Eng 2013; 6(5): 551-60. doi: 10.4236/jbise.2013.65070
- Zhou M, Zhong L, Xu W, et al. Discovery of potential prognostic long non-coding RNA biomarkers for predicting the risk of tumor recurrence of breast cancer patients. Sci Rep 2016; 6(1): 31038. doi: 10.1038/srep31038 PMID: 27503456
- Nahand JS, Karimzadeh MR, Nezamnia M, et al. The role of miR‐146a in viral infection. IUBMB Life 2020; 72(3): 343-60. doi: 10.1002/iub.2222 PMID: 31889417
- Pérez-Rodríguez D, López-Fernández H, Agís-Balboa RC. Application of miRNA-seq in neuropsychiatry: A methodological perspective. Comput Biol Med 2021; 135: 104603. doi: 10.1016/j.compbiomed.2021.104603 PMID: 34216893
- Davey MG, Casey MC, McGuire A, et al. Evaluating the role of circulating MicroRNAs to aid therapeutic decision making for neoadjuvant chemotherapy in breast cancer. Ann Surg 2022; 276(5): 905-12. doi: 10.1097/SLA.0000000000005613 PMID: 35876391
- Davey MG, Davies M, Lowery AJ, Miller N, Kerin MJ. The role of microrna as clinical biomarkers for breast cancer surgery and treatment. Int J Mol Sci 2021; 22(15): 8290. doi: 10.3390/ijms22158290 PMID: 34361056
- Davey MG, Lowery AJ, Miller N, Kerin MJ. MicroRNA expression profiles and breast cancer chemotherapy. Int J Mol Sci 2021; 22(19): 10812. doi: 10.3390/ijms221910812 PMID: 34639152
- Davies M, Davey MG, Miller N. The potential of MicroRNAs as clinical biomarkers to aid ovarian cancer diagnosis and treatment. Genes 2022; 13(11): 2054. doi: 10.3390/genes13112054 PMID: 36360295
- Davey MG, Feeney G, Annuk H, et al. MicroRNA expression profiling predicts nodal status and disease recurrence in patients treated with curative intent for colorectal cancer. Cancers 2022; 14(9): 2109. doi: 10.3390/cancers14092109 PMID: 35565239
- Davey MG, McGuire A, Casey MC, et al. Evaluating the role of circulating MicroRNAs in predicting long-term survival outcomes in breast cancer: A prospective, multicenter clinical trial. J Am Coll Surg 2023; 236(2): 317-27. doi: 10.1097/XCS.0000000000000465 PMID: 36648259
- Papadaki C, Stratigos M, Markakis G, et al. Circulating microRNAs in the early prediction of disease recurrence in primary breast cancer. Breast Cancer Res 2018; 20(1): 72. doi: 10.1186/s13058-018-1001-3 PMID: 29996899
- Du F, Yuan P, Zhao ZT, et al. Erratum: A miRNA-based signature predicts development of disease recurrence in HER2 positive breast cancer after adjuvant trastuzumab-based treatment. Sci Rep 2016; 6(1): 35509. doi: 10.1038/srep35509 PMID: 27739502
- Thomopoulou K, Papadaki C, Monastirioti A, et al. MicroRNAs regulating tumor immune response in the prediction of the outcome in patients with breast cancer. Front Mol Biosci 2021; 8: 668534. doi: 10.3389/fmolb.2021.668534 PMID: 34179081
- Giannoudis A, Clarke K, Zakaria R, et al. A novel panel of differentially-expressed microRNAs in breast cancer brain metastasis may predict patient survival. Sci Rep 2019; 9(1): 18518. doi: 10.1038/s41598-019-55084-z PMID: 31811234
- Huo D, Clayton WM, Yoshimatsu TF, Chen J, Olopade OI. Identification of a circulating MicroRNA signature to distinguish recurrence in breast cancer patients. Oncotarget 2016; 7(34): 55231-48. doi: 10.18632/oncotarget.10485 PMID: 27409424
- Ding Z, Zu S, Gu J. Evaluating the molecule-based prediction of clinical drug responses in cancer. Bioinformatics 2016; 32(19): 2891-5. doi: 10.1093/bioinformatics/btw344 PMID: 27354694
- Cox BDR. Regression models and life-tables. J R Stat Soc 1972; 34(2)
- Lin D, Wei L. The robust inference for the proportional hazards model. J Am Stat Assoc 1989; 84(408): 1074-8. doi: 10.1080/01621459.1989.10478874
- Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15(12): 550. doi: 10.1186/s13059-014-0550-8 PMID: 25516281
- Wu T, Hu E, Xu S, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation 2021; 2(3): 100141. doi: 10.1016/j.xinn.2021.100141 PMID: 34557778
- Chun-zhi Z, Lei H, An-ling Z, et al. MicroRNA-221 and microRNA-222 regulate gastric carcinoma cell proliferation and radioresistance by targeting PTEN. BMC Cancer 2010; 10(1): 367. doi: 10.1186/1471-2407-10-367 PMID: 20618998
- Gu Z, Eleswarapu S, Jiang H. Identification and characterization of microRNAs from the bovine adipose tissue and mammary gland. FEBS Lett 2007; 581(5): 981-8. doi: 10.1016/j.febslet.2007.01.081 PMID: 17306260
- Goff ML, Weiss WJ, Said BN. Overexpression of miR-26a-2 in human liposarcoma is correlated with poor patient survival. Oncogenesis 2013; 2(5): e47.
- Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol 2010; 11(10): R106. doi: 10.1186/gb-2010-11-10-r106 PMID: 20979621
- Ashburner M, Catherine A, Judith A, et al. David, Gene Ontology: Tool for the unification of biology. Nat Genet 2000.
- Ott SM, Elder G. Osteoporosis associated with chronic kidney disease. Osteoporosis 2013; 1387-424. doi: 10.1016/B978-0-12-415853-5.00058-3
- Shi HY, Lee KT, Lee HH, et al. Comparison of artificial neural network and logistic regression models for predicting in-hospital mortality after primary liver cancer surgery. PLoS One 2012; 7(4): e35781. doi: 10.1371/journal.pone.0035781 PMID: 22563399
- McGuire A, Brown JAL, Kerin MJ. Metastatic breast cancer: The potential of miRNA for diagnosis and treatment monitoring. Cancer Metastasis Rev 2015; 34(1): 145-55. doi: 10.1007/s10555-015-9551-7 PMID: 25721950
- Kandettu A, Radhakrishnan R, Chakrabarty S, Sriharikrishnaa S, Kabekkodu SP. The emerging role of miRNA clusters in breast cancer progression. Biochim Biophy Acta (BBA)-. Rev Can 2020; 1874(2)
- Edge SB, Compton CC. The american joint committee on cancer: The 7th edition of the AJCC cancer staging manual and the future of TNM. Annals Sur Oncol 2010; 17(6): 1471-4.
- Li N, Miao Y, Shan Y, et al. MiR-106b and miR-93 regulate cell progression by suppression of PTEN via PI3K/Akt pathway in breast cancer. Cell Death Dis 2017; 8(5): e2796. doi: 10.1038/cddis.2017.119 PMID: 28518139
- Bertoli G, Cava C, Castiglioni I. MicroRNAs: New biomarkers for diagnosis, prognosis, therapy prediction and therapeutic tools for breast cancer. Theranostics 2015; 5(10): 1122-43. doi: 10.7150/thno.11543 PMID: 26199650
Supplementary files
