Large miRNA survival analysis reveals a prognostic four-biomarker signature for triple negative breast cancer

Author(s)

Fernando Andrade ^[1] ; Asuka Nakata ; Noriko Gotoh ^[3] ; André Fujita ^[4]

Affiliation

^[1] Universidade de São Paulo. Programa Internunidades de Pós-Graduação em Bioinformática - Brasil

^[3] Kanazawa University. Cancer Research Institute. Division of Cancer Cell Biology - Japão

^[4] Universidade de São Paulo. Instituto de Matemática e Estatística. Departamento de Ciência de Computação - Brasil

• Document type: Journal article
• Source: GENETICS AND MOLECULAR BIOLOGY; v. 43, n. 1 2020-03-02.

Abstract

Abstract Triple negative breast cancer (TNBC) is currently the only major breast tumor subtype without effective targeted therapy and, as a consequence, usually presents a poor outcome. Due to its more aggressive phenotype, there is an urgent clinical need to identify novel biomarkers that discriminate individuals with poor prognosis. We hypothesize that miRNAs can be used to this end because they are involved in the initiation and progression of tumors by altering the expression of their target genes. To identify a prognostic biomarker in TNBC, we analyzed the miRNA expression of a cohort composed of 185 patients diagnosed with TNBC using penalized Cox regression models. We identified a four-biomarker signature based on miR-221, miR-1305, miR-4708, and RMDN2 expression levels that allowed for the subdivision of TNBC into high- or low-risk groups (Hazard Ratio – HR = 0.32; 95% Confidence Interval - CI = 0.11–0.91; p = 0.03) and are also statistically associated with survival outcome in subgroups of postmenopausal status (HR = 0.19; 95% CI = 0.04–0.90; p= 0.016), node negative status (HR = 0.12; 95% CI = 0.01–1.04; p = 0.026), and tumors larger than 2cm (HR = 0.21; 95% CI = 0.05–0.81; p = 0.021). This four-biomarker signature was significantly associated with TNBC as an independent prognostic factor for survival. (AU)

• FAPESP's process: 15/01587-0 - Storage, modeling and analysis of dynamical systems for e-Science applications
• Grantee: João Eduardo Ferreira
• Support Opportunities: Research Grants - eScience and Data Science Program - Thematic Grants
• FAPESP's process: 18/21934-5 - Network statistics: theory, methods, and applications
• Grantee: André Fujita
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 16/13422-9 - Statistical methods in graphs with applications to life sciences
• Grantee: André Fujita
• Support Opportunities: Regular Research Grants