Abstract

BackgroundBreast cancer (BC) is one of the most common cancers with high mortality worldwide. In the present study, through bioinformatics analysis, we aimed to identify new biomarkers to predict the survival rate of BC patients.MethodsDifferentially expressed genes (DEGs) between low- and high-tumor mutation burden (TMB) groups were identified by using The Cancer Genome Atlas (TCGA) dataset and integrated analysis. Gene Ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, and the protein-protein interaction (PPI) network, were applied to predict the function of these above DEGs. Then, the Cox proportional hazard model was developed to screen DEGs. Based on the prognostic signature, survival analysis was used on The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Finally, the single-sample gene set enrichment (ssGSEA) analysis was employed to estimate immune cells related to this signature.ResultsTo create a prognostic signature, 6 DEGs were identified. The results revealed that the survival time of patients with high-risk scores based on the expression of the six-gene signature was dramatically shorter than that of patients with low-risk scores in BC. Furthermore, survival analysis and multivariate cox analysis indicated that the six-gene signature was an independent prognostic factor of BC. Then, we built a nomogram that integrated the clinicopathological factors with the six-gene signature to predict the survival probability of BC patients. We eventually predicted the 20 most vital small molecule drugs by CMap, and Nadolol was considered as the most promising small molecule to treat BC. Moreover, ssGSEA analysis showed that the 6 genes were closely associated with immune cells.ConclusionsWe constructed a six-gene signature associated with TMB that can improve the prognosis prediction and could be seen as a biomarker for BC patients.

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