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Updated: Jan 2, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
Bioinformatics analysis of potential therapeutic targets among ARHGAP genes in breast cancer
Wei-Xian Chen1,2, Ming Lou3, Lin Cheng1
1Department of Breast Surgery, The Second Affiliated Changzhou People's Hospital of Nanjing Medical University, Changzhou, Jiangsu 213000, P.R. China.
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.
Insights
This study identifies specific GTPase activating protein (ARHGAP) genes as potential biomarkers for breast cancer. Certain ARHGAP genes show altered expression and correlate with patient survival, suggesting roles in prognosis and precision treatment.
Area of Science:
- Oncology
- Molecular Biology
- Genetics
Background:
- GTPase activating proteins (RhoGAPs) regulate Rho-like GTPases and are implicated in tumor biology.
- Genes encoding RhoGAPs (ARHGAP) are involved in breast cancer development.
- Identifying novel biomarkers for breast cancer prognosis and precision treatment remains a significant challenge.
Purpose of the Study:
- To evaluate the expression patterns of ARHGAP family genes in breast cancer.
- To investigate the association between ARHGAP gene expression and patient survival outcomes.
- To identify potential ARHGAP biomarkers for breast cancer prognosis and precision treatment.
Main Methods:
- Utilized bioinformatics databases: Oncomine, Kaplan-Meier Plotter, bcGenExMiner, and cBioPortal.
- Analyzed gene expression data in breast cancer patients versus healthy individuals.
- Correlated ARHGAP gene expression with overall survival (OS), relapse-free survival (RFS), and clinical parameters.
Main Results:
- Low expression of ARHGAP6, 7, 10, 14, 19, 23, 24 and high expression of ARHGAP9, 11, 15, 18, 30 observed in breast cancer.
- Low ARHGAP6, 7, 19 expression linked to poor RFS and OS.
- High ARHGAP9, 15, 30 expression correlated with better RFS and OS.
- ARHGAP9, 15, 18, 19, 25, 30 expression associated with better prognosis in metastatic relapse.
- ARHGAP11A and 14 expression negatively impacted patient prognosis.
Conclusions:
- ARHGAP9, 15, 19, and 30 show significant prognostic value in breast cancer.
- These specific ARHGAP genes are associated with key clinical parameters.
- ARHGAP9, 15, 19, and 30 represent promising targets for precision breast cancer treatment and prognosis.
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