Cancer Relapse Prediction from Microrna Expression Data Using Machine Learning


Eliza Razak,Faridah Yusof,Raha Ahmad Raus,



Mirna,Cancer Relapse Prediction,Marker Selection,


Cancer is a major deadliest disease globally that involve uncontrolled cell growth and invasion-metastasis events. It accounts for around 13% of all deaths worldwide. Statistical reports have pointed out that the cancer occurrence rate is increasing at an alarming rate in the world. Furthermore, cancer relapse rate is also rising mostly due to late cancer diagnosis. Some cancers can recur at the site of origin or the distant site after years of anti-cancer treatment. Therefore, cancer relapse prediction process is of paramount important so that early specific treatments can be sought. Nevertheless, conventional methods for diagnosing cancer relapse rely on invasive and labor intensive biopsy examinations. Circulating miRNAs have gained great interest in medical field because of their higher sensitivity, specificity and potential for minimally invasive sampling procedures. Furthermore, miRNA expression profiling from body fluid samples using high-throughput approaches is a promising technology that could predict cancer relapse. This paper describes a machine learning based approach called one-dependent estimator to predict cancer relapse from miRNA expression data. The proposed framework will predict whether a particular cancer will relapse within cancer recurrence time frame, which is usually 5 years. To select relevant cancer recurrence associated miRNAs, we employ an entropy-based miRNA marker selection approach. This proposed system has achieved an average accuracy of 92.82% in predicting cancer relapse over three datasets, namely glioblastoma, ovarian cancer, and hepatocellular carcinoma (HCC). The experimental results exhibit the efficacy of the proposed framework.


I.Al-Ibrahim, A. (2011). Discretization of Continuous Attributes in Supervised Learning algorithms. The Research Bulletin of Jordan ACM-ISWSA, 7952.

II.Bashiri, A., Ghazisaeedi, M., Safdari, R., Shahmoradi, L., & Ehtesham, H. (2017). Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review. Iranian journal of public health, 46(2), 165-172.

III.Fleming, N. H., Zhong, J., da Silva, I. P., de Miera, E. V.-S., Brady, B., Han, S. W., . . . Osman, I. (2015). Serum-based miRNAs in the prediction and detection of recurrence in melanoma patients. Cancer, 121(1), 51-59. doi: 10.1002/cncr.28981

IV.García-Giménez, J. L. (2015). Epigenetic biomarkers and diagnostics: Academic Press.

V.Hu, Y.,Yu, C.-Y., Wang, J.-L., Guan, J., Chen, H.-Y., & Fang, J.-Y. (2014). MicroRNA sequence polymorphisms and the risk of different types of cancer. Scientific reports, 4, 3648.

VI.Huang, K.-H., Lan, Y.-T., Fang, W.-L., Chen, J.-H., Lo, S.-S., Li, A. F.-Y., . . . Shyr, Y.-M. (2015). The Correlation between miRNA and Lymph Node Metastasis in Gastric Cancer. BioMed research international, 2015, 543163. doi: 10.1155/2015/543163

VII.Kaneda, A., & Tsukada, Y.-i. (2017). DNA and Histone Methylation as Cancer Targets: Springer.

VIII.Kumar, V., Abbas, A. K., & Aster, J. C. (2017). Robbins Basic Pathology E-Book: Elsevier Health Sciences.
IX.Mäbert, K., Cojoc, M., Peitzsch, C., Kurth, I., Souchelnytskyi, S., & Dubrovska, A. (2014). Cancer biomarker discovery: current status and futureperspectives. International journal of radiation biology, 90(8), 659-677.
X.Mo, M.-H., Chen, L., Fu, Y., Wang, W., & Fu, S. W. (2012). Cell-free circulating miRNA biomarkers in cancer. Journal of Cancer, 3, 432.
XI.Moten, A., Schafer, D., & Ferrari, M. (2014). Redefining global health priorities: Improving cancer care in developing settings. Journal of Global Health, 4(1), 010304. doi: 10.7189/jogh.04.010304
XII.Natrella, M. G. (2013). Experimental statistics: Courier Dover Publications.
XIII.Pritchard, C. C., Cheng, H. H., & Tewari, M. (2012). MicroRNA profiling: approaches and considerations. Nature Reviews Genetics, 13(5), 358-369.
XIV.Ramírez‐Gallego, S., García, S., Mouriño‐Talín, H., Martínez‐Rego, D., Bolón‐Canedo, V., Alonso‐Betanzos, A., . . . Herrera, F. (2016). Data discretization: taxonomy and big data challenge. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 6(1), 5-21.
XV.Schulte, J. H., Schowe, B., Mestdagh, P., Kaderali, L., Kalaghatgi, P., Schlierf, S., . . . Thor, T. (2010). Accurate prediction of neuroblastoma outcome based on miRNA expression profiles. International journal of cancer, 127(10), 2374-2385.
XVI.Seyfried, T. N., & Huysentruyt, L. C. (2013). On the Origin of Cancer Metastasis. Critical reviews in oncogenesis, 18(1-2), 43-73.
XVII.Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms: Cambridge University Press.
XVIII.Wei, Q., Lei, R., & Hu, G. (2015). Roles of miR-182 in sensory organ development andcancer. Thoracic Cancer, 6(1), 2-9. doi: 10.1111/1759-7714.12164
View | Download