Bioinformatics analysis of potential therapeutic targets among ARHGAP genes in breast cancer

Chen,Wei‑Xian; Lou,Ming; Cheng,Lin; Qian,Qi; Xu,Ling‑Yun; Sun,Li; Zhu,Yu‑Lan; Dai,Hong

doi:10.3892/ol.2019.10949

Bioinformatics analysis of potential therapeutic targets among ARHGAP genes in breast cancer

Authors:
- Wei‑Xian Chen
- Ming Lou
- Lin Cheng
- Qi Qian
- Ling‑Yun Xu
- Li Sun
- Yu‑Lan Zhu
- Hong Dai
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Affiliations: Department of Breast Surgery, The Second Affiliated Changzhou People's Hospital of Nanjing Medical University, Changzhou, Jiangsu 213000, P.R. China, Department of Graduate School, Nanjing Medical University, Nanjing, Jiangsu 210000, P.R. China
Published online on: October 2, 2019 https://doi.org/10.3892/ol.2019.10949
Pages: 6017-6025
Copyright: © Chen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

GTPase activating proteins (RhoGAPs) serve significant roles in multiple aspects of tumor biology. Genes encoding RhoGAPs (ARHGAP), which switch off Rho‑like GTPases, are responsible for breast cancer biogenesis. However, the identification of suitable and novel biomarkers for precision treatment and prognosis remains challenging. The present study aimed to evaluate the expression of ARHGAP family genes in breast cancer and investigate the survival data using the Oncomine, Kaplan‑Meier Plotter, bcGenExMiner and cBioPortal online databases. The results demonstrated low expression of ARHGAP6, 7, 10, 14, 19, 23 and 24 and high expression of ARHGAP9, 11, 15, 18 and 30 in patients with breast cancer compared with that in healthy individuals. The survival analysis revealed that low expression levels of ARHGAP6, 7 and 19 were associated with poor relapse‑free survival (RFS) and overall survival (OS), whereas high expression levels of ARHGAP9, 15 and 30 were associated with preferable RFS and OS. Metastatic relapse data demonstrated that higher expression of ARHGAP9, 15, 18, 19, 25 and 30 were associated with better prognosis and increased expression of ARHGAP11A and 14 exerted negative effects on patient prognosis. The overlapping genes ARHGAP9, 15, 19 and 30 obtained from these bioinformatics analysis tools exhibited significant association with clinical parameters including age, the presence of estrogen receptor, progesterone receptor and epidermal growth factor receptor‑2, Scarff‑Bloom‑Richardson grade and Nottingham prognostic index. In conclusion, bioinformatics analysis revealed that ARHGAP9, 15, 19 and 30, but not other ARHGAP family genes may be promising targets with prognostic value and biological function for precision treatment of breast cancer.

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