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Updated: Jan 28, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Bioinformatics analysis of prognostic value of TRIM13 gene in breast cancer
Wei-Xian Chen1,2, Lin Cheng3, Ling-Yun Xu3
1Department of Breast Surgery, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213000, Jiangsu Province, China chenweixian@njmu.edu.cn.
Abstract:
Background: Tripartite motif 13 (TRIM13) plays a significant role in various biological processes including cell growth, apoptosis, transcriptional regulation, and carcinogenesis. However, the prognostic significance of TRIM13 gene in breast cancer treatment remains largely unclear. Methods: We performed a bioinformatics analysis of the clinical parameters and survival data as it relates to TRIM13 in breast cancer patients using several online databases including Oncomine, bcGenExMiner, PrognoScan, and UCSC Xena. Results: We found that TRIM13 was lower-expressed in different subtypes of breast cancer with respect to normal tissues. Estrogen receptor and progesterone receptor status were positively correlated with TRIM13 level; whereas, the Scarff-Bloom-Richardson grade, Nottingham prognostic index, nodal status, basal-like status, and triple-negative status were negatively related to TRIM13 expression in breast cancer patients with respect to normal individuals. Lower TRIM13 expression correlated with worse distant metastasis free survival, relapse free survival, disease specific survival, and metastatic relapse free survival. We also confirmed a positive correlation between TRIM13 and RAB11FIP2 gene expression. Conclusion: Bioinformatics analysis revealed that TRIM13 may be adopted as a promising predictive biomarker for prognosis of breast cancer. More in-depth experiments and clinical trials are needed to validate the value of TRIM13 in breast cancer treatment.
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