Genes clustering selection to survival prediction in breast cancer patients

Authors

  • Khennedy Bacule dos Santos Instituto de Ciências Matemáticas e de Computação (ICMC), USP - São Carlos
  • Israel Tojal da Silva A.C.Camargo Cancer Center
  • Mariana Cúri Instituto de Ciências Matemáticas e de Computação (ICMC), USP - São Carlos

DOI:

https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1103

Keywords:

Machine learning, Breast cancer, Genes expression

Abstract

The risk stratification based on molecular data for predicting cancer progression or outcome is an important undertaking for supporting clinical decision making in oncology. In this work, we use Cox model and K-means to define a prognostic gene expression-based signature. Our approach reaches a better C-index (0.8341) and outperforms the Cox model by using clinical data alone (0.6348). Overall, this shows that the genetic signature found is related to the evolution of the patient's clinical condition, detecting molecular features related to prognosis in breast cancer.

Author Biographies

Khennedy Bacule dos Santos, Instituto de Ciências Matemáticas e de Computação (ICMC), USP - São Carlos

Instituto de Ciências Matemáticas e de Computação (ICMC), USP - São Carlos, São Paulo (SP), Brasil.

Israel Tojal da Silva, A.C.Camargo Cancer Center

A.C.Camargo Cancer Center, São Paulo (SP), Brasil.

Mariana Cúri, Instituto de Ciências Matemáticas e de Computação (ICMC), USP - São Carlos

Instituto de Ciências Matemáticas e de Computação (ICMC), USP - São Carlos, São Paulo (SP), Brasil.

References

Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., Bray, F.: Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 71(3), 209–249 (2021).

Lei, S., Zheng, R., Zhang, S., Chen, R., Wang, S., Sun, K., Zeng, H., Wei, W., He, J.: Breast cancer incidence and mortality in women in china: temporal trends and projections to 2030. Cancer biology & medicine 18(3), 900–909 (2021).

Vogelstein, B., Kinzler, K.W.: Cancer genes and the pathways they control. Nat Med 10(8), 789–799 (Aug 2004)

Mardis, E.R.: The Impact of Next-Generation Sequencing on Cancer Genomics: From Discovery to Clinic. Cold Spring Harb Perspect Med 9(9) (09 2019).

Abadi, A., Yavari, P., Dehghani-Arani, M., Alavi-Majd, H., Ghasemi, E., Aman- pour, F., Bajdik, C.: Cox models survival analysis based on breast cancer treat- ments. Iranian journal of cancer prevention 7(3), 124 (2014)

Bellera, C.A., MacGrogan, G., Debled, M., de Lara, C.T., Brouste, V., Mathoulin- P ́elissier, S.: Variables with time-varying effects and the cox model: Some statistical concepts illustrated with a prognostic factor study in breast cancer. BMC Medical Research Methodology 10(1) (Mar 2010).

Chen, Y., Zeng, W., Zhu, D.: Cox regression analysis on the survival rate of breast cancer patients. In: Yin, H.M., Chen, K., Meˇstrovi ́c, R., Oliveira, T.A., Lin, N. (eds.) International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021). vol. 12163, pp. 195 – 203. International Society for Optics and Photonics, SPIE (2022).

Husain, H., Thamrin, S.A., Tahir, S., Mukhlisin, A., Apriani, M.M.: The appli- cation of extended cox proportional hazard method for estimating survival time of breast cancer. Journal of Physics: Conference Series 979, 012087 (mar 2018).

Jiang, Q.: Cancer Classification and Gene Selection with Machine Learning Method, p. 122–127. Association for Computing Machinery, New York, NY, USA (2020)

Wang, W., Liu, W.: Integration of gene interaction information into a reweighted Lasso-Cox model for accurate survival prediction. Bioinformatics 36(22-23), 5405– 5414 (12/2020).

Xie, G., Dong, C., Kong, Y., Zhong, J.F., Li, M., Wang, K.: Group lasso regularized deep learning for cancer prognosis from multi-omics and clinical features. Genes 10(3) (2019).

Zeng, D., Zhou, R., Yu, Y., Luo, Y., Zhang, J., Sun, H., Bin, J., Liao, Y., Rao, J., Zhang, Y., Liao, W.: Gene expression profiles for a prognostic im-

Genes clustering selection to survival prediction in breast cancer patients 13 munoscore in gastric cancer. British Journal of Surgery 105(10), 1338–1348 (04 2018).

De Bin, R.: Boosting in cox regression: A comparison between the likelihoodbased and the model-based approaches with focus on the r-packages coxboost and mboost. Comput. Stat. 31(2), 513–531 (jun 2016).

Ching, T., Zhu, X., Garmire, L.X.: Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data. PLOS Computational Biology 14(4), 1–18 (04 2018).

Ishwaran, H., Kogalur, U.B., Blackstone, E.H., Lauer, M.S.: Random survival forests. The Annals of Applied Statistics 2(3), 841 – 860 (2008).

Network, T.C.G.A.: Comprehensive molecular portraits of human breast tumours. Nature 490(7418), 61–70 (Oct 2012)

Harrell, Frank E., J., Califf, R.M., Pryor, D.B., Lee, K.L., Rosati, R.A.: Evaluating the Yield of Medical Tests. JAMA 247(18), 2543–2546 (05 1982).

M ́enard, S., Fortis, S., Castiglioni, F., Agresti, R., Balsari, A.: HER2 as a prognostic factor in breast cancer. Oncology 61 Suppl 2, 67–72 (2001)

Guo, C., Liu, S., Wang, J., Sun, M.Z., Greenaway, F.T.: Actb in cancer. Clinica chimica acta 417, 39–44 (2013)

Ke H, Zhao L, Zhang H, et al. Loss of TDP43 inhibits progression of triple-negative breast cancer in coordination with SRSF3. Proc Natl Acad Sci U S A. 2018;115(15):E3426-E3435. doi:10.1073/pnas.1714573115

Dumax-Vorzet, A., Roboti, P., High, S.: Ost4 is a subunit of the mammalian oligosaccharyltransferase required for efficient n-glycosylation. Journal of cell science 126(12), 2595–2606 (2013)

Harada, Y., Ohkawa, Y., Kizuka, Y., Taniguchi, N.: Oligosaccharyltransferase: A gatekeeper of health and tumor progression. International journal of molecular sciences 20(23), 6074 (2019)

Published

2023-07-20

How to Cite

Santos, K. B. dos, Silva, I. T. da, & Cúri, M. (2023). Genes clustering selection to survival prediction in breast cancer patients. Journal of Health Informatics, 15(Especial). https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1103

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