1.Al-Ali, B.M., Ress, A.L., Gerger, A. and Pichler, M. 2012. MicroRNAs in renal cell carcinoma: Implications for pathogenesis, diagnosis, prognosis and therapy. Anticancer Res., 32, 3727-3732.

2.Anon. 2014. Oncogenes and Tumor Suppressor Genes. American Cancer Society. Available from: https://www.cancer.org/cancer/ cancer-causes/genetics/genes-and-cancer/oncogenes-tumorsuppressor- genes.html. [Last accessed on 2021 Jan 28].

3.Anon. 2018. Key Statistics About Kidney Cancer. American Cancer Society. Available from: https://www.cancer.org/cancer/ kidney-cancer/about/key-statistics.html. [Last accessed on 2021 Jan 28].

4.Anon. 2019. Renal Cell Cancer Treatment (PDQ®)-Patient Version. National Cancer Institute. Available from: https://www.cancer. gov/types/kidney/patient/kidney-treatment-pdq. [Last accessed on 2021 Jan 28].

5.Arruebo, M., Vilaboa, N., Sáez-Gutierrez, B., Lambea, J., Tres, A., Valladares, M. and González-Fernández, Á. 2011. Assessment of the evolution of cancer treatment therapies. Cancers, 3, 3279- 3330.

6.Bhargava, N., Sharma, G., Bhargava, R. and Mathuria, M. 2013. Decision tree analysis on j48 algorithm for data mining. Proc. Int. J. Adv. Res. Comput. Sci. Softw. Eng., 3, 6.

7.Bhowmick, S.S., Saha, I., Bhattacharjee, D., Genovese, L.M. and Geraci, F. 2018. Genome-wide analysis of NGS data to compile cancer-specific panels of miRNA biomarkers. PLoS One, 13, e0200353.

8.Bifet, A. and Gavalda, R. 2009, August. Adaptive learning from evolving data streams. In: International Symposium on Intelligent Data Analysis. Springer, Berlin, Heidelberg. pp. 249-260.

9.Cairns, P. 2011. Renal cell carcinoma. Cancer Biomark., 9, 461-473.

10.Campbell, D. 2014. Almost Half of Cancer Patients Diagnosed Too Late, The Guardian. Available from: https://www.theguardian. com/society/2014/sep/22/cancer-late-diagnosis-half-patients. [Last accessed on 2021 Jan 29].

11.Chang, I., Mitsui, Y., Fukuhara, S., Gill, A., Wong, D.K., Yamamura, S., Shahryari, V., Tabatabai, Z.L., Dahiya, R., Shin, D.M. and Tanaka, Y. 2015. Loss of miR-200c upregulates CYP1B1 and confers docetaxel resistance in renal cell carcinoma. Oncotarget, 6, 7774.

12.Fedorko, M., Pacik, D., Wasserbauer, R., Juracek, J., Varga, G., Ghazal, M. and Nussir, M.I. 2016. MicroRNAs in the pathogenesis of renal cell carcinoma and their diagnostic and prognostic utility as cancer biomarkers. Int. J. Biol. Markers, 31(1), 26-37.

13.Luan, J., Wang, J., Su, Q., Chen, X., Jiang, G. and Xu, X. 2016. Meta-analysis of the differentially expressed microRNA profiles in nasopharyngeal carcinoma. Oncotarget, 7, 10513.

14.Martinez, A., Fullwood, P., Kondo, K., Kishida, T., Yao, M., Maher, E.R. and Latif, F. 2000. Role of chromosome 3p12-p21 tumour suppressor genes in clear cell renal cell carcinoma: Analysis of VHL dependent and VHL independent pathways of tumorigenesis. Mol. Pathol., 53, 137.

15.Morii, K., Yamasaki, S., Doi, S., Irifuku, T., Sasaki, K., Doi, T., Nakashima, A., Arihiro, K. and Masaki, T. 2019. microRNA- 200c regulates KLOTHO expression in human kidney cells under oxidative stress. PLoS One, 14, e0218468.

16.Moldovan, L., Batte, K.E., Trgovcich, J., Wisler, J., Marsh, C.B. and Piper, M. 2014. Methodological challenges in utilizing mi RNA s as circulating biomarkers. J. Cell. Mol. Med., 18, 371-390.

17.Muhamed Ali, A., Zhuang, H., Ibrahim, A., Rehman, O., Huang, M. and Wu, A. 2018. A machine learning approach for the classification of kidney cancer subtypes using miRNA genome data. Appl. Sci., 8, 2422.

18.Nakada, C., Matsuura, K., Tsukamoto, Y., Tanigawa, M., Yoshimoto, T., Narimatsu, T., Nguyen, L.T., Hijiya, N., Uchida, T., Sato, F. and Mimata, H. 2008. Genome?wide microRNA expression profiling in renal cell carcinoma: Significant down?regulation of miR?141 and miR?200c. J. Pathol., 216, 418-427.

19.O’Brien, J., Hayder, H., Zayed, Y. and Peng, C., 2018. Overview of microRNA biogenesis, mechanisms of actions, and circulation. Front. Endocrinol., 9, 402.

20.Paranjape, T., Slack, F.J. and Weidhaas, J.B. 2009. MicroRNAs: Tools for cancer diagnostics. Gut, 58, 1546-1554.

21.Rehman, O., Zhuang, H., Muhamed Ali, A., Ibrahim, A. and Li, Z. 2019. Validation of miRNAs as breast cancer biomarkers with a machine learning approach. Cancers, 11, 431.

22.Sanchez, D.J. and Simon, M.C. 2018. Genetic and metabolic hallmarks of clear cell renal cell carcinoma. Biochim. Biophys. Acta Rev. Cancer, 1870, 23-31.

23.Strimbu, K. and Tavel, J.A. 2010. What are biomarkers? Curr. Opin. HIV AIDS, 5, 463.

24.van IJzendoorn, D.G., Szuhai, K., Briaire-de Bruijn, I.H., Kostine, M., Kuijjer, M.L. and Bovée, J.V. 2019. Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas. PLoS Comput. Boil., 15, e1006826.

25.van IJzendoorn, D.G., Szuhai, K., Briaire-de Bruijn, I.H., Kostine, M., Kuijjer, M.L. and Bovée, J.V. 2019. Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas. PLoS Comput. Boil., 15, e1006826.

26.Wach, S., Nolte, E., Theil, A., Stöhr, C., Rau, T.T., Hartmann, A., Ekici, A., Keck, B., Taubert, H. and Wullich, B. 2013. MicroRNA profiles classify papillary renal cell carcinoma subtypes. Br. J. Cancer, 109, 714-722.

27.Witten, I.H., Frank, E., Hall, M.A. and Pal, C.J. 2005. Practical Machine Learning Tools and Techniques. Vol. 578. Morgan Kaufmann, United States. p. 1.

28.Zakrzewska, M., Gruszka, R., Stawiski, K., Fendler, W., Kordacka, J., Grajkowska, W., Daszkiewicz, P., Liberski, P.P. and Zakrzewski, K. 2019. Expression-based decision tree model reveals distinct microRNA expression pattern in pediatric neuronal and mixed neuronal-glial tumors. BMC Cancer, 19, 1-11.

29.Zheng, X., Fu, X., Wang, K. and Wang, M. 2020. Deep neural networks for human microRNA precursor detection. BMC Bioinform., 21, 1-7.