Identification of the Molecular Subtype and Prognostic Characteristics of Breast Cancer Based on Tumor-Infiltrating Regulatory T Cells

Abstract

Background: T regulatory cells (Tregs) are essential for preserving immune tolerance. They are present in large numbers in many tumors, hindering potentially beneficial antitumor responses. However, their predictive significance for breast cancer (BC) remains ambiguous. This study aimed to explore genes associated with Tregs and develop a prognostic signature associated with Tregs.

Methods: The gene expression and clinical data on BC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The integration of CIBERSORT and weighted correlation network analysis (WGCNA) algorithms was utilized to identify modules associated with Tregs. The consensus cluster algorithm was utilized to create molecular subtypes determined by genes associated with Tregs. Then, a prognostic signature associated with Tregs was constructed and its relationship to tumor immunity and the prognosis was evaluated.

Results: The blue module genes exhibited the most significant correlation with Tregs, and 1080 genes related to Tregs were acquired. A total of 93 genes from the TCGA dataset were found to have a significant impact on patient prognosis. Samples from BC were categorized into two clusters by consensus cluster analysis. The overall survival, immune checkpoint genes, molecular subtype, and biological behaviors varied significantly between these two subtypes. A 10-gene signature developed from differentially expressed genes between two subtypes demonstrated consistent prognostic accuracy in both TCGA and GEO datasets. It functioned as a standalone prognostic marker for individuals with BC. In addition, patients with low risk are more inclined to exhibit increased immune cell infiltration, TME score, and tumor mutation burden (TMB). Meanwhile, Individuals classified within the low-risk group showed better responses to immunotherapies compared to their counterparts in the high-risk group.

Conclusions: The prognostic model derived from Tregs-related genes could aid in assessing the prognosis, guiding personalized treatment, and potentially enhancing the clinical outcomes for patients with BC.