Advertisement

Survival-Expression Map and Essential Forms of Survival-Expression Relations for Genes

  • Yajing Chen
  • Shikui Tu
  • Lei XuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10559)

Abstract

The relation between survival and gene expression has been investigated in many studies. Some used a univariate Cox model to detect genes with expression significantly related to survival. Some built a multivariate Cox model to analyze the influence of multiple genes on death risk. The original Cox model assumes a linear relation between survival and expression. But some evidence implied the existence of non-linear relation. Whether the survival-expression relations for different genes share some particular forms remain unknown. Here, we clustered the survival-expression (S-E) relations by k-means. We also developed a survival-expression (S-E) map to display the S-E relations for each cluster and summarized four essential forms of relations. We believe that the four essential S-E forms might assist the discovery of therapeutic targets and enhance the understanding of mechanisms in cancers.

Keywords

Cox regression Spline Survival-expression map Essential survival-expression relation 

Notes

Acknowledgments

This work was supported by the Zhi-Yuan chair professorship start-up grant (WF220103010) from Shanghai Jiao Tong University.

References

  1. 1.
    Aalen, O.: Nonparametric inference for a family of counting processes. Ann. Stat. 6, 701–726 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Dhanasekaran, S.M., Barrette, T.R., Ghosh, D., Shah, R., Varambally, S., Kurachi, K., Pienta, K.J., Rubin, M.A., Chinnaiyan, A.M.: Delineation of prognostic biomarkers in prostate cancer. Nature 412(6849), 822–826 (2001)CrossRefGoogle Scholar
  3. 3.
    Diamandis, E.P., Scorilas, A., Fracchioli, S., Van Gramberen, M., De Bruijn, H., Henrik, A., Soosaipillai, A., Grass, L., Yousef, G.M., Stenman, U.H., et al.: Human kallikrein 6 (hK6): a new potential serum biomarker for diagnosis and prognosis of ovarian carcinoma. J. Clin. Oncol. 21(6), 1035–1043 (2003)CrossRefGoogle Scholar
  4. 4.
    Goldman, M., Craft, B., Swatloski, T., Cline, M., Morozova, O., Diekhans, M., Haussler, D., Zhu, J.: The UCSC cancer genomics browser: update 2015. Nucleic Acids Res. 43, D812–D817 (2014)CrossRefGoogle Scholar
  5. 5.
    Li, H., Luan, Y.: Boosting proportional hazards models using smoothing splines, with applications to high-dimensional microarray data. Bioinformatics 21(10), 2403–2409 (2005)CrossRefGoogle Scholar
  6. 6.
    Luo, L.Y., Katsaros, D., Scorilas, A., Fracchioli, S., Bellino, R., van Gramberen, M., de Bruijn, H., Henrik, A., Stenman, U.H., Massobrio, M., et al.: The serum concentration of human kallikrein 10 represents a novel biomarker for ovarian cancer diagnosis and prognosis. Cancer Res. 63(4), 807–811 (2003)Google Scholar
  7. 7.
    Rini, B., Goddard, A., Knezevic, D., Maddala, T., Zhou, M., Aydin, H., Campbell, S., Elson, P., Koscielny, S., Lopatin, M., et al.: A 16-gene assay to predict recurrence after surgery in localised renal cell carcinoma: development and validation studies. Lancet Oncol. 16(6), 676–685 (2015)CrossRefGoogle Scholar
  8. 8.
    Rockova, V., Abbas, S., Wouters, B.J., Erpelinck, C.A., Beverloo, H.B., Delwel, R., van Putten, W.L., Löwenberg, B., Valk, P.J.: Risk stratification of intermediate-risk acute myeloid leukemia: integrative analysis of a multitude of gene mutation and gene expression markers. Blood 118(4), 1069–1076 (2011)CrossRefGoogle Scholar
  9. 9.
    Sotiriou, C., Neo, S.Y., McShane, L.M., Korn, E.L., Long, P.M., Jazaeri, A., Martiat, P., Fox, S.B., Harris, A.L., Liu, E.T.: Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc. Nat. Acad. Sci. 100(18), 10393–10398 (2003)CrossRefGoogle Scholar
  10. 10.
    Van’t Veer, L.J., Dai, H., Van De Vijver, M.J., He, Y.D., Hart, A.A., Mao, M., Peterse, H.L., van der Kooy, K., Marton, M.J., Witteveen, A.T., et al.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871), 530–536 (2002)CrossRefGoogle Scholar
  11. 11.
    Wang, Y., Klijn, J.G., Zhang, Y., Sieuwerts, A.M., Look, M.P., Yang, F., Talantov, D., Timmermans, M., Meijer-van Gelder, M.E., Yu, J., et al.: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365(9460), 671–679 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Center for Cognitive Machines and Computational Health, and Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Department of Computer Science and EngineeringThe Chinese University of Hong KongHong Kong SARChina

Personalised recommendations