Bioinformatics-based analysis of TCGA to identify the prognostic biomarkers in HR-positive/HER2-negative tamoxifen-treated breast cancer

Abstract Background Patients with hormone receptor-positive (HR+) breast cancer, which accounts for approximately 70% of all breast cancers, receive tamoxifen therapy to inhibit recurrence and improve overall survival. However, the cumulative risk of distant recurrence in the past 20 years is still as high as 22 to 52% after 5 years of endotherapy. TNM stage, histological grade and age are important clinical factors related to recurrence. Differential gene analysis based on matching clinical factors will be helpful for understanding the molecular mechanism of recurrence; however, there have been no reports yet.Results In our study, we identified 647 DEGs in a TCGA dataset that included 20 patients, of which 10 patients had relapsed after tamoxifen treatment and 10 did not. Ten genes were found by the CytoHubba APP in Cytoscape. After analysis of the GO terms and KEGG pathways, we found that the genes were mainly enriched in the inflammatory response and cytokine-receptor interaction, and then these results were verified in the GEPIA2 and KM-plot websites. Finally, we identified CXCL1, CXCL2 and CX3CL1 as prognostic factors, and their overexpression in HR-positive/HER2-negative breast cancer predicted a longer disease-free survival.Conclusion In conclusion, the high expression of CXCL1, CXCL2, and CX3CL1 can predict longer disease-free survival in breast cancer after tamoxifen treatment and may be a biomarker for tamoxifen therapy.