The biomarkers related to immune infiltration to predict distant metastasis in patients with breast cancer

Abstract

Abstract Background:The development of distant metastasis (DM) results in poor prognosis of breast cancer (BC) patients, however, it is difficult to predict the risk of distant metastasis. Methods: differentially expressed gene (DEGs) were screened out using GSE184717 and GSE183947.GSE20685 were randomly assigned to the training and the internal validation cohort. A signature was developed according to the results of univariate and multivariate Cox regression analysis,which was validated by using internal and external(GSE6532) validation cohort. Gene set enrichment analysis (GSEA) was used for functional analysis.Finally, a nomogram was constructed and calibration curves and concordance index (C-index) were compiled to determine predictive and discriminatory capacity.The clinical benefit of this nomogram was revealed by decision curve analysis (DCA). Finally, we explored the relationships between candidate genes and immune cell infiltration, and the possible mechanism. Results: A signature containing CD74 and TSPAN7 was developed according to the results of univariate and multivariate Cox regression analysis,which was validated by using internal and external(GSE6532) validation cohort. Mechanistically,the signature reflect the overall level of immune infiltration in tissues, especially myeloid immune cells. The expression of CD74 and TSPAN7 is heterogeneous, low expression levels of TSPAN7 is caused by methylation modification in breast cancer cells, which is negatively correlated with CD74 expression level.CD74 is mainly derived from myeloid immune cells and do not affect the proportion of CD8+T cells. This signature could act as an independent predictive factor in patients with BC (P = 0.01, HR = 0.63), and it has been validated in internal (P = 0.023, HR = 0.58) and external (P = 0.0065, HR = 0.67) cohort. Finally, we constructed a individualized prediction nomogram based on our signature. The model showed good discrimination in training, internal and external cohort, with a C-index of 0.742, 0.801, 0.695 respectively, and good calibration. DCA demonstrated that the prediction nomogram was clinically useful. Conclusion: A new immune infiltration related signature developed for predicting metastatic risk will improve the treatment and management of BC patients