Exploration of Tumor Microenvironment-Related Biomarkers for the Prognosis of Triple Negative Breast Cancer

Abstract Background: Triple negative breast cancer (TNBC) is one of the most disastrous breast cancer subtypes world widely. The tumor microenvironment (TME), especially the infiltration of immune and stromal cells, are highly related to the occurrence, development and prognosis of breast cancer. Therefore, exploration of TME-related biomarkers is greatly important for improving overall survival rate of TNBC patients. Methods: The open-assess Cancer Genome Atlas (TCGA) database provides gene expression profile for a variety of malignant tumors allowing researchers to explore genes demonstrating TME in the prognosis prediction of TNBC. In our present study, ESTIMATE algorithm was used to calculate the immune and stromal scores in accordance with the characteristics of specific genes in immune and stromal cells, and divide them into high and low-score groups. Limma R package was then utilized to screen differentially expressed genes (DEGs). After that, functional enrichment analysis and protein-protein interaction (PPI) network were performed to explore the bio-information of the DEGs and their encoded proteins. Subsequently, the identified these genes were further verified in the Gene Expression Omnibus (GEO) datasets. Results: Eight genes, including ACAP1, DUSP1, LYZGZMA, SASH3, CCL5, CD74, and DPT , were explored to closely related to the TME of TNBC. A prognostic model containing these selected genes was established with a high efficiency in the prediction of the poor prognosis of TNBC patients. Conclusion: An eight-gene prognostic model was a considerably reliable approach for predicting the overall survival of TNBC patients, and could help clinicians selecting personalized treatments for their TNBC patients.