Original Article
Applying gene set analysis to characterize the activities of immune cells in estrogen receptor positive breast cancer
Abstract
Background: Estrogen receptor (ER) is a crucial biomarker for subtyping breast cancer. The present study aimed to understand the influence of infiltrated immune cells to patients’ outcome in estrogen receptor positive (ER+) breast cancer.
Methods: Gene expression profiles of three breast cancer cohorts downloaded from Gene Expression Omnibus (GEO) were used in this study. We utilized gene set enrichment analysis (GSEA) to estimate the activities of immune cell infiltration based on 31 published immune gene sets. Each gene set was tested for ER+ associated prognostic value. GSEA was applied to identify biological functions associated with prognostic immune gene sets.
Results: Nine subtypes of immune cells showed ER+ specific association with patient survival; seven of them formed two co-activation clusters, including: (I) activated CD4, CD8, effector memory CD4, and (II) regulatory T cell, dendritic cell, eosinophil, and mast cell, substantially representing innate and adaptive immunity. Among them, activated CD8 and mast cell were independent prognostic factors in multivariate Cox regression. Functional annotation analysis revealed their involvement in breast cancer subtyping, relapse, and metastasis.
Conclusions: We devised a gene set analysis to comprehensively investigate the involvement of ER specific immune cell activities and prognosis in breast cancer. Our work provides hints of the interaction between infiltrated immune cells and activated oncogene in ER+ breast cancer and may contribute to the biological basis for the development of immunotherapy.
Methods: Gene expression profiles of three breast cancer cohorts downloaded from Gene Expression Omnibus (GEO) were used in this study. We utilized gene set enrichment analysis (GSEA) to estimate the activities of immune cell infiltration based on 31 published immune gene sets. Each gene set was tested for ER+ associated prognostic value. GSEA was applied to identify biological functions associated with prognostic immune gene sets.
Results: Nine subtypes of immune cells showed ER+ specific association with patient survival; seven of them formed two co-activation clusters, including: (I) activated CD4, CD8, effector memory CD4, and (II) regulatory T cell, dendritic cell, eosinophil, and mast cell, substantially representing innate and adaptive immunity. Among them, activated CD8 and mast cell were independent prognostic factors in multivariate Cox regression. Functional annotation analysis revealed their involvement in breast cancer subtyping, relapse, and metastasis.
Conclusions: We devised a gene set analysis to comprehensively investigate the involvement of ER specific immune cell activities and prognosis in breast cancer. Our work provides hints of the interaction between infiltrated immune cells and activated oncogene in ER+ breast cancer and may contribute to the biological basis for the development of immunotherapy.
