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Identification of novel genes for triple-negative breast cancer with semiparametric gene-based analysis

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  • Xiaotong Liu
  • Guoliang Tian
  • Zhenqiu Liu

Abstract

Triple-negative breast cancer (TNBC) is generally considered an aggressive breast cancer subtype associated with poor prognostic outcomes. Up to now, the molecular and cellular mechanisms underlying TNBC pathology have not been fully understood. In this manuscript, we propose a novel semiparametric model with kernel for gene-based analysis with a breast cancer GWAS data. The software of SPMGBA (semiparametric method for gene-based analysis) in MATLAB is available at GitHub (https://github.com/zliu3/SPMGBA). Genetic signatures associated with breast cancer are discovered. We further validate the prognostic power of the identified genes with a large cohort of expression data from the European Genome-Phenome Archive, and discover that SEL1L is associated with the overall survival of TNBC with the p-value of .0002. We conclude that gene SEL1L is down-regulated in TNBC and the expression of SEL1L is positively associated with patient survival.

Suggested Citation

  • Xiaotong Liu & Guoliang Tian & Zhenqiu Liu, 2023. "Identification of novel genes for triple-negative breast cancer with semiparametric gene-based analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 50(3), pages 691-702, February.
    • Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:691-702
    DOI: 10.1080/02664763.2021.1973387
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