An In Silico Analysis Identified FZD9 as a Potential Prognostic Biomarker in Triple-Negative Breast Cancer Patients

Daniel Rodrigues de Bastos; Mércia Patrícia Ferreira Conceição; Ana Paula Picaro Michelli; Jean Michel Rocha Sampaio Leite; Rafael André da Silva; Ricardo Cesar Cintra; Jeniffer Johana Duarte Sanchez; Cesar Augusto Sam Tiago Vilanova-Costa; Antonio Márcio Teodoro Cordeiro Silva

doi:10.4274/ejbh.2020.5804

An In Silico Analysis Identified FZD9 as a Potential Prognostic Biomarker in Triple-Negative Breast Cancer Patients

Eur J Breast Health. 2020 Dec 24;17(1):42-52. doi: 10.4274/ejbh.2020.5804. eCollection 2021 Jan.

Authors

Daniel Rodrigues de Bastos¹, Mércia Patrícia Ferreira Conceição¹, Ana Paula Picaro Michelli², Jean Michel Rocha Sampaio Leite³, Rafael André da Silva⁴, Ricardo Cesar Cintra⁵, Jeniffer Johana Duarte Sanchez⁶, Cesar Augusto Sam Tiago Vilanova-Costa⁷, Antonio Márcio Teodoro Cordeiro Silva⁸

Affiliations

¹ Department of Oncology, Universidade de São Paulo, São Paulo, Brazil.
² Department of Biological Sciences, Thyroid Molecular Science Laboratory, Universidade Federal de São Paulo, São Paulo, Brazil.
³ Department of Nutrition, Universidade de São Paulo, Faculty of Public Health, São Paulo, Brazil.
⁴ Department of Cellular & Developmental Biology, Universidade de São Paulo, Institute of Biomedical Sciences, São Paulo, Brazil.
⁵ Department of Biochemistry, Universidade de São Paulo, Institute of Chemistry, São Paulo, Brazil.
⁶ Department of Statistics and Applied Math, Universidade Federal do Ceará, Fortaleza, Brazil.
⁷ Laboratory of Tumor Biology and Oncogenetics Hospital, Araujo Jorge, Goiânia, Brazil.
⁸ Department of Medicine, Pontifícia Universidade Católica de Goiás, School of Medical Sciences, Biomedics and Pharmaceuticals, Goiânia, Brazil.

Abstract

Objective: Breast cancer (BC) is the main cause of cancer-related deaths in women across the world. It can be classified into different subtypes, including triple-negative (TN), which is characterized by the absence of hormone receptors for estrogen and progesterone and the lack of the human epidermal growth factor receptor 2. These tumors have high heterogeneity, acquire therapeutic resistance, and have no established target-driven treatment yet. The identification of differentially expressed genes in TN breast tumors and the in silico validation of their prognostic role in these tumors.

Materials and methods: We employed a microarray dataset and, by using the GEO2R tool, we identified a list of differentially expressed genes. The in silico validation was conducted using several online platforms including the KM Plotter, cBioPortal, bc-GenExMiner, Prognoscan, and Roc Plotter.

Results: We observed that FZD9 was among the top differentially expressed genes in a cohort of patients with different TNBC subtypes. The FZD9 expression was significantly different in TN breast tumors than in non-TN (nTN) breast tumors (p<0.0001), and the basal TN subtype showed the highest levels (p<0.0001). In addition, the FZD9 levels were significantly inversely and positively proportional (p<0.0001) to estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 clinical parameters. The high levels of FZD9 were associated with worse overall survival (p=0.007), relapse-free survival (p=5.8e-05), and worse survival in patients who received chemotherapy (p=3.2e-05; 0.007).

Conclusion: Our cumulative results demonstrated that FZD9 plays an important role in TNBC and may be a potential prognostic biomarker. Nevertheless, further in vitro and in vivo assays are necessary to confirm our findings and to strengthen the evidences about the mechanisms by which FZD9 functions in these tumors.

Keywords: FZD9; biomarkers; breast cancer; in silico analysis; triple-negative breast cancer.