Proceedings of International Conference on Applied Innovation in IT
2023/03/09, Volume 11, Issue 1, pp.143-148

Random Forest Algorithm in Unravelling Biomarkers of Breast Cancer Progression

Nadiia Kasianchuk, Dmytro Tsvyk, Eduard Siemens and Halina Falfushynska

Abstract: Breast cancer is the leading cause of cancer death among women. As its development involves a multidimensional network of gene-environment interactions, advanced data analysis tools and bioinformatics are vital to uncover the nature of cancer. The initial database contained the expression values of 19737 genes in 1082 patients. Random Forest algorithm was used to distil the genes with the strongest influence on four substantial prognostic factors (survival period, tumour size, lymph node seizure, and metastasis). The obtained set consists of 230 potential biomarkers that facilitate the critical cancer-related pathways, such as p53, Wnt, VEGF, UPP, thereby influencing cell proliferation, tumouri- and angiogenesis. A considerable contrast in the expression was shown between the patients at different stages of cancer progression. The obtained set will simplify the diagnostics and prediction of tumour progression, enhance treatment outcomes and elaborate better strategies for curing breast cancer.

Keywords: Breast Cancer, K-Means, Random Forest, Genes, Prognostic Factors, Classification, Biomarkers.

DOI: 10.25673/101930

Download: PDF


  1. S.C. Shah, V. Kayamba, R.M. Jr. Peek, and D. Heimburger, “Cancer Control in Low- and Middle-Income Countries: Is It Time to Consider Screening?” vol. 5, pp. 1-8, 2019, doi: 10.1200/JGO.18.00200.
  2. J. Li, X. Guan, and et al., “Non-Invasive Biomarkers for Early Detection of Breast Cancer,” Cancers (Basel), vol. 12(10), pp. 2767, 2020, doi: 10.3390/cancers12102767.
  3. B.K. Banin Hirata, J.M. Oda, R. Losi Guembarovski, C.B. Ariza, C.E. de Oliveira, and M.A. Watanabe, “Molecular markers for breast cancer: prediction on tumor behavior,” Dis. Markers, vol. 2014, pp. 513158, 2014, doi: 10.1155/2014/513158.
  4. A.N. Richter and T.M. Khoshgoftaar, “A review of statistical and machine learning methods for modeling cancer risk using structured clinical data,” Artif. Intell. Med., vol. 90, pp. 1-14, 2018, doi: 10.1016/j.artmed. 2018.06.002.
  5. A. Zaremba, P. Zaremba, S. Siry, Y. Shermolovich, and S. Zagorodnya, “In vitro and in silico study of anti-influenza activity of 2-dioxopyrimidin-5-trifluoromethyl-tetrahydrothiophene with subsequent increase in its affinity for the target protein,” Proceedings of the 7th International Electronic Conference on Medicinal Chemistry, 1-30 November 2021, MDPI: Basel, Switzerland, doi:10.3390/ ECMC2021-11439.
  6. L. Peng, W. Chen, W. Zhou, F. Li, J. Yang, and J. Zhang, “An immune-inspired semi-supervised algorithm for breast cancer diagnosis,” Comput. Methods Programs Biomed., vol. 134, pp. 259-265, 2016, doi: 10.1016/j.cmpb.2016.07.020.
  7. H. Falfushynska, O. Lushchak, and E. Siemens, “The Application of Multivariate Statistical Methods in Ecotoxicology and Environmental Biochemistry,” Proceedings of International Conference on Applied Innovation in IT, vol. 10 (1), pp. 99-104, 2022.
  8. P. Rzymski, N. Kasianchuk, D. Sikora, and B. Poniedziałek, “COVID‐19 Vaccinations and Rates of Infections, Hospitalizations, ICU Admissions, and Deaths in Europe during SARS‐CoV‐2 Omicron wave in the first quarter of 2022,” Journal of Medical Virology, vol. 95(14), 2022, doi: 10.1002/jmv.28131.
  9. P.S. Rana, A. Alkrekshi, W. Wang, V. Markovic, and K. Sossey-Alaoui, “The Role of WAVE2 Signaling in Cancer”, Biomedicines, vol. 9(9), pp. 1217, 2021, doi: 10.3390/biomedicines9091217.
  10. Y.H. Kuo, A.L. Shiau, and et al., “Expression of prothymosin α in lung cancer is associated with squamous cell carcinoma and smoking”, Oncol. Lett., vol. 17(6), pp. 5740-5746, 2019, doi: 10.3892/ol.2019.10248.
  11. S. Roy, S. Saha, and et al., “Molecular crosstalk between CUEDC2 and ERα influences the clinical outcome by regulating mitosis in breast cancer”, Cancer Gene Ther., vol. 29(11), pp. 1697-1706, 2022, doi: 10.1038/s41417-022-00494-x.
  12. C. Tao, W. Liu, and et al., “PAQR5 Expression Is Suppressed by TGFβ1 and Associated With a Poor Survival Outcome in Renal Clear Cell Carcinoma”, Front. Oncol., vol. 11, pp. 827344, 2022, doi: 10.3389/fonc.2021.827344.
  13. X. Chen and H. Ishwaran, “Random forests for genomic data analysis,” Genomics, vol. 99(6), pp. 323-9, 2012, doi: 10.1016/j.ygeno.2012.04.003.
  14. N.E. Reticker-Flynn, W. Zhang, and et al., “Lymph node colonization induces tumor-immune tolerance to promote distant metastasis”, Cell, vol. 185(11), pp. 1924-1942.e23, 2022, doi: 10.1016/j.cell.2022. 04.019.
  15. Y. Song, K. Sun, and et al., “CPSF4 promotes tumor-initiating phenotype by enhancing VEGF/NRP2/TAZ signaling in lung cancer”, Med. Oncol., vol. 40(1), pp. 62, 2022, doi: 10.1007/s12032-022-01919-1.
  16. Y. Zhou, Y. Che, Z. Fu, H. Zhang, and H. Wu, “Triple-Negative Breast Cancer Analysis Based on Metabolic Gene Classification and Immunotherapy”, Front. Public Health, vol. 10, pp. 902378, 2022, doi: 10.3389/fpubh.2022.902378.
  17. V.A. Kraft, C.T. Bezjian, and et al., “GTP Cyclohydrolase 1/Tetrahydrobiopterin Counteract Ferroptosis through Lipid Remodeling”, ACS Cent Sci. vol. 6(1), pp. 41-53, 2020, doi: 10.1021/acscentsci.9b01063.
  18. M. Saxena, R.K.R. Kalathur, and et al., “2-Histone Interaction Is Critical for Cancer Cell Dedifferentiation and Progression in Malignant Breast Cancer”, Cancer Res. vol. 80(17), pp. 3631-3648, 2020, doi: 10.1158/0008-5472.CAN-19-2910.
  19. S. Satheesha, V.J. Cookson, and et al., “Response to mTOR inhibition: activity of eIF4E predicts sensitivity in cell lines and acquired changes in eIF4E regulation in breast cancer”, Mol. Cancer, vol. 10, pp. 19, 2011, doi: 10.1186/1476-4598-10-19.
  20. L. Li, X. Liu, L. He, and et al., “ZNF516 suppresses EGFR by targeting the CtBP/LSD1/CoREST complex to chromatin”, Nat. Commun., vol. 8(1), pp. 691, 2017, doi: 10.1038/s41467-017-00702-5.



       - Call for Papers
       - Submission to the Journal
       - Paper Submission
       - Final Paper Submission
       - Important Dates
       - Conference Committee
       - Editorial Board
       - Reviewers
       - Last Proceedings


       - Volume 11, Issue 1 (ICAIIT 2023)
       - Volume 10, Issue 1 (ICAIIT 2022)
       - Volume 9, Issue 1 (ICAIIT 2021)
       - Volume 8, Issue 1 (ICAIIT 2020)
       - Volume 7, Issue 1 (ICAIIT 2019)
       - Volume 7, Issue 2 (ICAIIT 2019)
       - Volume 6, Issue 1 (ICAIIT 2018)
       - Volume 5, Issue 1 (ICAIIT 2017)
       - Volume 4, Issue 1 (ICAIIT 2016)
       - Volume 3, Issue 1 (ICAIIT 2015)
       - Volume 2, Issue 1 (ICAIIT 2014)
       - Volume 1, Issue 1 (ICAIIT 2013)


       ICAIIT 2023
         - Photos
         - Reports

       ICAIIT 2022
         - Message

       ICAIIT 2021
         - Photos
         - Reports

       ICAIIT 2020
         - Photos
         - Reports

       ICAIIT 2019
         - Photos
         - Reports

       ICAIIT 2018
         - Photos
         - Reports






Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0

                                                   This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

           ISSN 2199-8876
           Publisher: Anhalt University of Applied Sciences

Creative Commons License
Except where otherwise noted, all works and proceedings on this site is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.