Construction of a prognostic survival model with tumor immune-related genes for breast cancer.

Background: Numerous studies have demonstrated that immune cell infiltration is a significant predictor in the prognosis of those with breast cancer. This study aimed to develop a prognostic model for undifferentiated breast cancer using immune-related markers.

Methods: Differentially expressed genes (DEGs) and prognostic factors were identified from The Cancer Genome Atlas (TCGA) database. Cancer immune-associated genes were filtered using the GeneCards database. Least absolute shrinkage and selection operator (LASSO) and Cox proportional hazards regression were employed to select prognostic indicators. The single-sample gene set enrichment analysis (ssGSEA) algorithm and the CIBERSORT algorithm were used to analyze the correlation of prognostic indicators with immune cells in breast cancer.

Results: We identified six tumor immune-related genes, including zic family member 2 (), solute carrier family 7 member 5 (), forkhead box J1 (), C-X-C motif chemokine ligand 9 (), tumor necrosis factor receptor superfamily member 18 (), and serine protease 2 (), for the development of a prognostic model for patients with breast cancer. Notably, the results of the correlation analysis indicated that was associated with antitumor immune cells, including CD8 T cells, cytotoxic cells, M1 macrophages, and activated memory CD4 T cells, and with the enrichment of natural killer (NK) CD56dim cells. Furthermore, exhibited a significant negative association with the tumor-promoting M2 macrophage phenotype.

Conclusions: Our study established a six-gene model for predicting breast cancer prognosis. Furthermore, we unexpectedly discovered that is integral to immune infiltration in breast cancer and may serve as a critical biomarker for evaluating immune response and therapeutic efficacy in breast cancer treatment.