Exploring immune-related genes with prognostic value in microenvironment of breast cancer from TCGA database.

Abstract

Breast cancer is one of the most common malignancies in women worldwide. Many studies have shown that tumor microenvironment cells, immune cells, and stromal cell infiltration have an important impact on prognosis, so it is important to identify biomarkers for achieving better treatment and prognosis.To better understand the relationship between immune and stromal cell-related genes and prognosis, we screened patients with breast cancer in The Cancer Genome Atlas (TCGA) database and divided them into high and low groups based on immune/stromal scores. We next identified differentially expressed immune-related genes that are significantly associated with the prognosis of patients with breast cancer for functional enrichment analysis and protein-protein interaction networks, respectively. Finally, we selected a separate breast cancer cohort in gene expression synthesis (GEO) for validation.Both immune scores and stromal scores are meaningful in the correlation of subtype classification. Disease-free survival of cases with the high score group of immune scores is statistically longer than the cases in the low score group. Differentially expressed immune-related genes extracted from the comparison can effectively evaluate the prognosis of patients with breast cancer and these genes are primarily involved in immune responses, extracellular matrix, and chemokine activity. At last, we obtained a series of verified tumor immune-related genes that predict the prognosis of patients with breast cancer.Combining the Estimation of Stromal and Immune Cells in Malignant Tumor Tissues using Expression database and the TCGA database to extract the list of tumor microenvironment related genes which may help to outline the prognosis of patients with breast cancer. Some previously overlooked genes have the potential to become additional biomarkers for breast cancer. Further research on these genes can reveal a new understanding of the potential relationship between tumor microenvironment and breast cancer prognosis.