Abstract
Although research into immunotherapy is growing, its use in the treatment of breast cancer remains limited. Thus, identification and evaluation of prognostic biomarkers of tissue microenvironments will reveal new immune-based therapeutic strategies for breast cancer. Using an in silico bioinformatic approach, we investigated the tumor microenvironmental and genetic factors related to breast cancer. We calculated the Immune score, Stromal score, Estimate score, Tumor purity, TMB (Tumor mutation burden), and MATH (Mutant-allele tumor heterogeneity) of Breast cancer patients from the Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm and Maftools. Significant correlations between Immune/Stromal scores with breast cancer subtypes and tumor stages were established. Importantly, we found that the Immune score, but not the Stromal score, was significantly related to the patient's prognosis. Weighted correlation network analysis (WGCNA) identified a pattern of gene function associated with Immune score, and that almost all of these genes (388 genes) are significantly upregulated in the higher Immune score group. Protein-protein interaction (PPI) network analysis revealed the enrichment of immune checkpoint genes, predicting a good prognosis for breast cancer. Among all the upregulated genes, FPR3, a G protein-coupled receptor essential for neutrophil activation, is the sole factor that predicts poor prognosis. Gene set enrichment analysis analysis showed FRP3 upregulation synergizes with the activation of many pathways involved in carcinogenesis. In summary, this study identified FPR3 as a key immune-related biomarker predicting a poor prognosis for breast cancer, revealing it as a promising intervention target for immunotherapy.
Highlights
Breast cancer, the most common gynecological cancer worldwide (Ghoncheh et al, 2016), is becoming a majorpublic health crisis, and the number of new cases diagnosed each year is everincreasing (Cheng et al, 2018)
We found significant correlations between Immune score and Stromal score with breast cancer subtypes
Using the PAM50 classification method, we used the genefu package (Gendoo et al, 2016) in R language to divide the breast cancer subtypes into luminal A (LumA), luminal B (LumB), Her2+, and Basal
Summary
The most common gynecological cancer worldwide (Ghoncheh et al, 2016), is becoming a majorpublic health crisis, and the number of new cases diagnosed each year is everincreasing (Cheng et al, 2018). Breast cancer is considered less immunogenic than melanoma or renal cell carcinoma, and the results of adoptive immunotherapy (interleukin-2, interferon) have been relatively disappointing (Burugu et al, 2017). With the increased understanding of the immune microenvironment of breast cancer tissues, immune escape has been considered an important feature of breast cancer development (Romaniuk and Lyndin, 2015; Takada et al, 2018). Targeting the tumor immune microenvironment in breast cancer is of high therapeutic interest (Zhao et al, 2017). The therapeutic effects of immune checkpoint inhibition may be limited. The evaluation of avelumab, an anti-PDL1 antibody, in various subtypes of breast cancer showed that the overall response rate (ORR) for the entire cohort was 4.8% (Emens, 2018), far from achieving the intended effect
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