Genomic Biomarkers of Metastasis in Breast Cancer Patients: A Machine Learning Approach

Yıl 2022, Cilt: 7 Sayı: 2, 29 - 32, 31.12.2022

Feyza İnceoğlu , Fatma Hilal Yağın

https://doi.org/10.52876/jcs.1211185

Cited By: 1

Öz

One of the cancers with the highest incidence in the world is breast cancer (BC). The aim of this study is to identify candidate biomarker genes to predict the risk of distant metastases in patients with BC and to compare the performance of machine learning (ML) based models. In the study; Genomic dataset containing 24,481 gene expression levels of 97 patients with BC was analyzed. Biomarker candidate genes were determined by ML approaches and models were created with XGBoost, naive bayes (NB) and multilayer perceptron (MLP) algorithms. The accuracy values of XGBoost, NB and MLP algorithms were obtained as 0.990, 0.907 and 0.979, respectively. Our results showed that XGBoost has higher performance. The top five genes associated with BC metastasis were AL080059, Ubiquilin 1, CA9, PEX12, and CCN4. In conclusion, when the ML method and genomic technology are used together, the distant metastasis risk of patients with BC can be successfully predicted. The developed XGBoost model can distinguish patients with distant metastases. Identified biomarker candidate genes may contribute to diagnostic, therapeutic and drug development research in patients with metastases.

Anahtar Kelimeler

Breast Canser, Gene Analysis, Machine Learning, XGBoost

Kaynakça

• [1] Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 68(6), 394-424.
• [2] Cancer, I. A. f. R. o., & Organization, W. H. (2012). Breast Cancer Estimated Incidence, Mortality and Prevalence Worldwide. Globocan 2012: World Health Organization.
• [3] Sowunmi, A., Alabi, A., Fatiregun, O., Olatunji, T., Okoro, U. S., & Etti, A. F. D. (2018). Trend of cancer incidence in an oncology center in Nigeria: West African Journal of Radiology, 25(1), 52.
• [4] Momenimovahed, Z., & Salehiniya, H. (2019). Epidemiological characteristics of and risk factors for breast cancer in the world. Breast Cancer: Targets and Therapy, 11, 151.
• [5] Sun, Y.-S., Zhao, Z., Yang, Z.-N., Xu, F., Lu, H.-J., Zhu, Z.-Y., . . . Zhu, H.-P. (2017). Risk factors and preventions of breast cancer: International Journal of Biological Sciences, 13(11), 1387.
• [6] Holleczek, B., Stegmaier, C., Radosa, J. C., Solomayer, E.-F., & Brenner, H. (2019). Risk of loco-regional recurrence and distant metastases of patients with invasive breast cancer up to ten years after diagnosis–results from a registry-based study from Germany: Bmc Cancer, 19(1), 1-14.
• [7] Anwar, S. L., Avanti, W. S., Nugroho, A. C., Choridah, L., Dwianingsih, E. K., Harahap, W. A., . . . Wulaningsih, W. (2020). Risk factors of distant metastasis after surgery among different breast cancer subtypes: a hospital-based study in Indonesia: World Journal of Surgical Oncology, 18(1), 1-16.
• [8] Savas, P., Teo, Z. L., Lefevre, C., Flensburg, C., Caramia, F., Alsop, K., . . . Silva, M. J. (2016). The subclonal architecture of metastatic breast cancer: results from a prospective community-based rapid autopsy program “CASCADE”: PLoS medicine, 13(12), e1002204.
• [9] Xu, C., Meng, L. B., Duan, Y. C., Cheng, Y. J., Zhang, C. M., Zhou, X., & Huang, C. B. (2019). Screening and identification of biomarkers for systemic sclerosis via microarray technology: International journal of molecular medicine, 44(5), 1753-1770.
• [10] Ahmad, M. A., Eckert, C., & Teredesai, A. (2018). Interpretable machine learning in healthcare: Paper presented at the Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics.
• [11] Yağin, F. H., Yağin, B., Arslan, A. K., & Çolak, C. (2021). Comparison of Performances of Associative Classification Methods for Cervical Cancer Prediction: Observational Study: Turkiye Klinikleri Journal of Biostatistics, 13(3).
• [12] Khaire, U. M., & Dhanalakshmi, R. (2020). High-dimensional microarray dataset classification using an improved adam optimizer (iAdam): Journal of Ambient Intelligence and Humanized Computing, 11(11), 5187-5204.
• [13] Vaka, A. R., Soni, B., & Reddy, S. (2020). Breast cancer detection by leveraging Machine Learning: ICT Express, 6(4), 320-324.
• [14] Akbulut, S., Yağın, F. H., & Çolak, C. (2022). Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing (mNGS) Data using Artificial Intelligence Technology: Erciyes Medical Journal.
• [15] Van't Veer, L. J., Dai, H., Van De Vijver, M. J., He, Y. D., Hart, A. A., Mao, M., . . . Witteveen, A. T. (2002). Gene expression profiling predicts clinical outcome of breast cancer: nature, 415(6871), 530-536.
• [16] Lee, M., Lee, J.-H., & Kim, D.-H. (2022). Gender recognition using optimal gait feature based on recursive feature elimination in normal walking: Expert Systems with Applications, 189, 116040.
• [17] Yilmaz, R., & Yağin, F. H. (2022). Early detection of coronary heart disease based on machine learning methods: Medical Records, 4(1), 1-6.
• [18] Li, Z. (2022). Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost. Computers: Environment and Urban Systems, 96, 101845.
• [19] Paksoy, N., & Yagin, F. H. (2022). Artificial Intelligence-based Colon Cancer Prediction by Identifying Genomic Biomarkers: Medical Records, 4(2), 196-202.
• [20] Yilmaz, R., & Yagin, F. H. (2021). A comparative study for the prediction of heart attack risk and associated factors using MLP and RBF neural networks: The Journal of Cognitive Systems, 6(2), 51-54.
• [21] Akbulut, S., Yagin, F. H., & Colak, C. (2022). Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective: Istanbul Medical Journal, 23(3).
• [22] Perçin, İ., Yağin, F. H., Arslan, A. K., & Çolak, C. (2019). An interactive web tool for classification problems based on machine learning algorithms using java programming language: data classification software: Paper presented at the 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT).
• [23] Song, Q., Jing, H., Wu, H., Zou, B., Zhou, G., & Kambara, H. (2016). Comparative Gene Expression Analysis of Breast Cancer-Related Genes by Multiplex Pyrosequencing Coupled with Sequence Barcodes Advances and Clinical Practice in Pyrosequencing: Springer, 315-325.
• [24] Feng, X., Cao, A., Qin, T., Zhang, Q., Fan, S., Wang, B., . . . Li, L. (2021). Abnormally elevated ubiquilin‑1 expression in breast cancer regulates metastasis and stemness via AKT signaling: Oncology Reports, 46(5), 1-14.
• [25] Jantrapirom, S., Lo Piccolo, L., Pruksakorn, D., Potikanond, S., & Nimlamool, W. (2020). Ubiquilin networking in cancers: Cancers, 12(6), 1586.
• [26] Hu, Z., Li, X., Yuan, R., Ring, B. Z., & Su, L. (2010). Three common TP53 polymorphisms in susceptibility to breast cancer, evidence from meta-analysis: Breast cancer research and treatment, 120(3), 705-714.
• [27] Moelans, C. B., De Weger, R. A., & Van Diest, P. J. (2010). Absence of chromosome 17 polysomy in breast cancer: analysis by CEP17 chromogenic in situ hybridization and multiplex ligation-dependent probe amplification: Springer, 120, 1-7.
• [28] Smeets, A., Daemen, A., Vanden Bempt, I., Gevaert, O., Claes, B., Wildiers, H., . . . De Moor, B. (2011). Prediction of lymph node involvement in breast cancer from primary tumor tissue using gene expression profiling and miRNAs: Breast cancer research and treatment, 129(3), 767-776.
• [29] Daskalaki, I., Gkikas, I., & Tavernarakis, N. (2018). Hypoxia and selective autophagy in cancer development and therapy: Frontiers in Cell and Developmental Biology, 6, 104.
• [30] Nivison, M. P., & Meier, K. E. (2018). The role of CCN4/WISP-1 in the cancerous phenotype: Cancer Management and Research, 10, 2893.
• [31] Wu, Y., McRoberts, K., Berr, S., Frierson, H., Conaway, M., & Theodorescu, D. (2007). Neuromedin U is regulated by the metastasis suppressor RhoGDI2 and is a novel promoter of tumor formation, lung metastasis and cancer cachexia: Oncogene, 26(5), 765-773.
• [32] Garczyk, S., Klotz, N., Szczepanski, S., Denecke, B., Antonopoulos, W., Von Stillfried, S., . . . Dahl, E. (2017). Oncogenic features of neuromedin U in breast cancer are associated with NMUR2 expression involving crosstalk with members of the WNT signaling pathway: Oncotarget, 8(22), 36246.