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Purpose The prognosis of breast cancer (BC) patients who develop into brain metastases (BMs) is very poor. Thus, it is of great significance to explore the etiology of BMs in BC and identify the key genes involved in this process to improve the survival of BC patients with BMs. Patients and methods The gene expression data and the clinical information of BC patients were downloaded from TCGA and GEO database. Differentially expressed genes (DEGs) in TCGA-BRCA and GSE12276 were overlapped to find differentially expressed metastatic genes (DEMGs). The protein-protein interaction (PPI) network of DEMGs was constructed via STRING database. ClusterProfiler R package was applied to perform the gene ontology (GO) enrichment analysis of DEMGs. The univariate Cox regression analysis and the Kaplan-Meier (K-M) curves were plotted to screen DEMGs associated with the overall survival and the metastatic recurrence survival, which were identified as the key genes associated with the BMs in BC. The immune infiltration and the expressions of immune checkpoints for BC patients with brain relapses and BC patients with other relapses were analyzed respectively. The correlations among the expressions of key genes and the differently infiltrated immune cells or the differentially expressed immune checkpoints were calculated. The gene set enrichment analysis (GSEA) of each key gene was conducted to investigate the potential mechanisms of key genes involved in BC patients with BMs. Moreover, CTD database was used to predict the drug-gene interaction network of key genes. Results A total of 154 DEGs were identified in BC patients at M0 and M1 in TCGA database. A total of 667 DEGs were identified in BC patients with brain relapses and with other relapses. By overlapping these DEGs, 17 DEMGs were identified, which were enriched in the cell proliferation related biological processes and the immune related molecular functions. The univariate Cox regression analysis and the Kaplan-Meier curves revealed that CXCL9 and GPR171 were closely associated with the overall survival and the metastatic recurrence survival and were identified as key genes associated with BMs in BC. The analyses of immune infiltration and immune checkpoint expressions showed that there was a significant difference of the immune microenvironment between brain relapses and other relapses in BC. GSEA indicated that CXCL9 and GPR171 may regulate BMs in BC via the immune-related pathways. Conclusion Our study identified the key genes associated with BMs in BC patients and explore the underlying mechanisms involved in the etiology of BMs in BC. These findings may provide a promising approach for the treatments of BC patients with BMs.

Background The relationship between the combined hematological parameters and echocardiography and long‐term prognosis in patients with coronary artery disease (CAD) remains unclear. Methods We examined the ability of hematological parameters to predict all‐cause death and major adverse cardiovascular events (MACE) based on Lasso Cox regression analysis. The significant predictors of hematological parameters from the Lasso Cox model were analyzed via multivariate Cox regression analysis and by adjusting for echocardiographic data. We calculated the continuous net reclassification improvement (cNRI) and integrated discrimination improvement (IDI) of the hematological parameters to assess the improvement in prediction. Results A low hemoglobin and lymphocyte ratio and high hematocrit, red blood cell distribution width‐coefficient of variation, and monocyte ratio significantly increased the risk of MACE and death in CAD patients. Neutrophil‐to‐lymphocyte ratio was associated with MACE but not death in CAD patients. After adjustment for echocardiographic parameters, hemoglobin, hematocrit, and lymphocyte ratio remained independently related to death and MACE. The addition of hematological and echocardiographic parameters to the Framingham risk score model significantly improved the area under the curve of mortality (0.794 vs. 0.713, p = 0.0007) and reclassification with cNRI of 30.6% (p = 0.002) and IDI of 0.055 (p < 0.001). Mendelian randomization analyses identified that fibrinogen and neutrophil‐to‐lymphocyte ratio were associated with increased brain natriuretic peptide and decreased left ventricular ejection fraction. Conclusions These findings suggest that the blood immune inflammatory indicators fibrinogen and neutrophil‐to‐lymphocyte ratio were causally associated with the risk of heart failure after CAD. The combination of hematological biomarkers and echocardiography parameters as predictor variables is a useful predictive tool for all‐cause mortality in patients with CAD.