Single-cell expression and immune infiltration analysis of polyamine metabolism in breast cancer

Abstract

Abstract Breast cancer is one of the most threatening women health diseases worldwide and its molecular heterogeneity offers a range of response to therapy. The role of polyamine metabolism is receiving increasing attention. Polyamine metabolism not only plays an important role in the occurrence and development of breast cancer, but also interacts with tumor immune microenvironment. In this work, we applied single-cell RNA-sequencing (scRNA-seq) and systems immunological approaches to interrogate immune cell infiltration gene-to-gene co-expressions in the bulk tumor transcriptomes of breast cancer. We acquired breast cancer sample data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), evaluated the infiltration status of 22 immune cell types using CIBERSORTx tool, respectively. By leveraging the Retrospective Breast sample of various technologies including gene expression and methylation, we identified 46 breast cancer proliferation-associated co-expression modules using weighted gene coexpression network analysis (WGCNA) approach along with machine learning models which in turn delineated single cell level expressions features that these selected module possessed. We observed substantial cellular heterogeneity in the breast cancer microenvironment, where lineage-specific gene expression patterns were highly associated with tumor progression. Moreover, we also identified the gene modules correlated with immune cell infiltration level that could function as regulators in response to tumors for immune therapy. Moreover, risk scores were correlated with immune cell function in different patient groups defined by high- and low-risk. The findings of this study shed a new light upon molecular classification prognostic assessment and personalized treatment in breast cancer.

Keywords

• Breast cancer
• Single-cell RNA sequencing
• Immune microenvironment
• Gene co-expression network
• Machine learning
• Personalized treatment