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

Autor(es)

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

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: GENETICS AND MOLECULAR BIOLOGY; v. 43, n. 1 2020-03-02.

Resumo

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)

• Processo FAPESP: 15/01587-0 - Armazenagem, modelagem e análise de sistemas dinâmicos para aplicações em e-Science
• Beneficiário: João Eduardo Ferreira
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Temático
• Processo FAPESP: 18/21934-5 - Estatística de redes: teoria, métodos e aplicações
• Beneficiário: André Fujita
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 16/13422-9 - Métodos estatísticos para grafos com aplicações em ciências da vida
• Beneficiário: André Fujita
• Modalidade de apoio: Auxílio à Pesquisa - Regular