The impact of chemokine receptor CXCR4 on breast cancer prognosis: A meta-analysis

Background: C-X-C chemokine receptor type 4 (CXCR4) has been implicated in the invasiveness and metastasis of diverse cancers. However, the published data remain controversial on the correlation between CXCR4 expression level, as well as its subcellular distribution in tumor cells, and the clinical outcome of patients with breast cancer. Methods: To identify the precise role of CXCR4 in the clinical outcome of breast cancer, we performed a meta-analysis including 15 published studies. Original data included the hazard ratios (HRs) of overall survival (OS) and disease-free survival (DFS) in breast cancer with high CXCR4 expression versus low expression. We pooled hazard ratios (HRs) with 95% confidence intervals (CIs) to estimate the hazard. Results: A total of 15 published studies (including 3104 patients) were eligible. Overall survival (OS) and disease-free survival (DFS) of breast cancer were found to be significantly related to CXCR4 expression level, with the HR being 1.65 (95%CI: 1.34–2.03; P < 0.00001) and 1.94 (95%CI: 1.42–2.65; P < 0.00001) respectively. Stratified analysis according to subcellular distribution of CXCR4 showed that high expression in whole cells, cytoplasm and nucleus could predict unfavorable OS, with the HR of 2.02 (95%CI: 1.43–2.85; P < 0.0001), 1.57 (95%CI: 1.13–2.18; P = 0.007), and 1.47 (95%CI: 1.19–1.81; P = 0.0004) respectively. As for DFS, elevated expression level of CXCR4 both in whole cells and cytoplasm predicted a poor outcome, with the HR being 2.23 (95%CI: 1.48–3.37; P = 0.0001) and 1.76 (95%CI: 1.11–2.80; P = 0.02), while high expression in the nucleus had no statistical significance, with HR 1.15 (95%CI: 0.52–2.55; P = 0.73). Conclusions: Increased CXCR4 expression, especially in whole cells and cytoplasm, may serve as a poor prognostic indicator in patients with breast cancer. Future studies are warranted to investigate the relationship between CXCR4 expression and survival of patients with breast carcinoma, which could help predict the clinical outcome and guide clinical decision-making for therapy.