Advertisement

Screening of Pathological Gene in Breast Cancer Based on Logistic Regression

  • Yun Zhao
  • Xu-Qing TangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

Abstract

Breast cancer has become the focus of the pathological gene screening research. In this paper, logistic regression and multiple hypothesis testing are used to screen the pathological gene based on the existing breast cancer genetic data. Then referencing the confidence level, the p-value of logistic regression is used to screen the pathological gene initially. Furthermore, by considering the Type I error, the multiple hypothesis testing is used to make the result accurate. In addition, SVM is used to test the reliability of this paper’s methods. In order to illustrate the feasibility of this method, each gene which screened by this method is tested and verified by the literature of breast cancer.

Keywords

Logistic regression BH testing Bonferroni testing Gene screening 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grand No. 11371174 and 11271163).

References

  1. 1.
    Collins, F.S., Morgan, M., Patrinos, A.: The human genome project: lessons from large-scale biology. Science 300(5617), 286 (2003)CrossRefGoogle Scholar
  2. 2.
    Peng, X., Sun, L., Huo, H.: The effect of apelin-13 on breast MCF-7 cell proliferation and invasion via activate ERK1/2 signaling pathways. J. Northeast Normal Univ. (Nat. Sci.) 47(3), 127–131 (2015)Google Scholar
  3. 3.
    Tang, X., Zhou, Y., Zhang, W.: Correlation between expression of TOP2A and HER2 signaling pathway in breast cancer. J. Xi‘an Jiaotong Univ. Med. Sci. 36(4), 519–522 (2015)Google Scholar
  4. 4.
    Edition, S.: Applied logistic regression analysis. Technometrics 38(2), 184–186 (2017)Google Scholar
  5. 5.
    Zhang, Y., Kwon, D., Pohl, K.M.: Computing group cardinality constraint solutions for logistic regression problems. Med. Image Anal. 35, 58–69 (2016)CrossRefGoogle Scholar
  6. 6.
    Zou, J., Liang, Q.: Progress in data mining techniques of diagnosis of breast cancer. J. Biomed. Eng. 29(2), 375–378 (2012)Google Scholar
  7. 7.
    Jin, H., Zhou, L.: Reconsideration on hypothesis test and P value. J. Environ. Occup. Med. 34(2), 95–98 (2017)Google Scholar
  8. 8.
    Hwang, Y.T., Lai, J.J., Ou, S.T.: Evaluations of FWER-controlling methods in multiple hypothesis testing. J. Appl. Stat. 37(10), 1681–1694 (2010)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Pau Ni, I.B., Zakaria, Z., Muhammad, R., et al.: Gene expression patterns distinguish breast carcinomas from normal breast tissues: the Malaysian context. Pathol. Res. Pract. 206(4), 223–228 (2010)CrossRefGoogle Scholar
  10. 10.
    Spizzo, G., Went, P.S., Obrist, P., et al.: High Ep-CAM expression is associated with poor prognosis in node-positive breast cancer. Breast Cancer Res. Treat. 86(3), 207–213 (2004)CrossRefGoogle Scholar
  11. 11.
    Ambrogi, F., Fornili, M., Boracchi, P., et al.: Trop-2 is a determinant of breast cancer survival. PLoS ONE 9(5), e96993 (2014)CrossRefGoogle Scholar
  12. 12.
    Thomassen, M., Tan, Q., Kruse, T.A.: Gene expression meta-analysis identifies chromosomal regions and candidate genes involved in breast cancer metastasis. Breast Cancer Res. Treat. 113(2), 239–249 (2009)CrossRefGoogle Scholar
  13. 13.
    Wang, W., Yuan, P., Yu, D., et al.: A single-nucleotide polymorphism in the 3′-UTR region of the adipocyte fatty acid binding protein 4 gene is associated with prognosis of triple-negative breast cancer. Oncotarget 7(14), 18984–18998 (2016)Google Scholar
  14. 14.
    Ji, Q., Aoyama, C., Nien, Y.D., et al.: Selective loss of AKR1C1 and AKR1C2 in breast cancer and their potential effect on progesterone signaling. Cancer Res. 64(20), 7610 (2004)CrossRefGoogle Scholar
  15. 15.
    Chen, L., Ye, C., Huang, Z., et al.: Differentially expressed genes and potential signaling pathway in Asian people with breast cancer by preliminary analysis of a large sample of the microarray data. J. South. Med. Univ. 34(6), 807–812 (2014)Google Scholar
  16. 16.
    Gao, J., Zhang, J., Dong, Y.: Clinical significance of detection of serum Leptin, Insulin_like growth factor-1 and Tunor necrosis factor α in breast cancer patients. Lab. Med. 20(1), 28–29 (2005)Google Scholar
  17. 17.
    Reddy, N.M., Kalyani, P., Kaiser, J.: Adiponectin and Leptin molecular actions and clinical significance in breast cancer. Int. J. Hematol.-Oncol. Stem Cell Res. 8(1), 31 (2014)Google Scholar
  18. 18.
    Lai, G., Jiang, B.: Progress in the relationship between the differential expression of thrombospondin-1 and breast cancer. J. Gannan Med. Univ. 32(3), 481–482 (2012)MathSciNetGoogle Scholar
  19. 19.
    Wang, Z., Wang, Q., Wang, Q., et al.: Prognostic significance of CD24 and CD44 in breast cancer: a meta-analysis. Int. J. Biol. Mark. 32(1), e75–e82 (2016)CrossRefGoogle Scholar
  20. 20.
    Ghaneie, A., Zembapalko, V., Itoh, H., et al.: Bin1 attenuation in breast cancer is correlated to nodal metastasis and reduced survival. Cancer Biol. Ther. 6(2), 192–194 (2007)CrossRefGoogle Scholar
  21. 21.
    Jones, J.E.C., Esler, W.P., Patel, R., et al.: Inhibition of acetyl-CoA carboxylase 1 (ACC1) and 2 (ACC2) reduces proliferation and de novo lipogenesis of EGFRvIII human glioblastoma cells. PLoS ONE 12(1), e0169566 (2017)CrossRefGoogle Scholar
  22. 22.
    Mukherjee, B., Mcellin, B., Camacho, C.V., et al.: EGFRvIII and DNA double-strand break repair: a molecular mechanism for radioresistance in glioblastoma. Can. Res. 69(10), 4252–4259 (2009)CrossRefGoogle Scholar
  23. 23.
    Nello, C., John, S.-T.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, 1st edn. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of ScienceJiangnan UniversityWuxiChina
  2. 2.Wuxi Engineering Research Center for BiocomputingWuxiChina

Personalised recommendations