Identification of prognostic biomarkers for breast cancer brain metastases based on the bioinformatics analysis

Abstract

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.

Identification of prognostic biomarkers of invasive ductal carcinoma by an integrated bioinformatics approach

“…Finally, mRNA-seq data for BC was retrieved from the Cancer Genome Atlas (TCGA) database (RRID:SCR_003193) to confirm the significance of the hub genes. 33…”

“…[30][31][32] Nowadays, the analysis of gene expression profiles [verification of differentially expressed genes (DEGs)] using bioinformatics tools has represented a notable advance in research in clinical oncology aimed at the identification of genes related to tumors, new molecular markers of diagnosis and prognosis, and evaluation of therapeutic effects, among others. 33 DEGs and protein-protein interaction network (PPI-net) analysis have been widely used to identify biomarkers and potential drug targets. Open-access databases such as Gene Expression Omnibus (GEO) are broadly employed as microarray resources for this purpose.…”

Identification of prognostic biomarkers of invasive ductal carcinoma by an integrated bioinformatics approach

“…[10][11][12] Nowadays, the analysis of gene expression profiles [verification of differentially expressed genes (DEGs)] using bioinformatics tools has represented a notable advance in research in clinical oncology aimed at the identification of genes related to tumors, new molecular markers of diagnosis and prognosis, and evaluation of therapeutic effects among others. 13 DEGs and protein-protein interaction network (PPI-net) analysis have been widely used to identify biomarkers and potential drug targets. Open access databases such as Gene Expression Omnibus (GEO) are broadly employed as microarray resources for this purpose.…”