Identification of Immune Characteristics of 2 Subtypes of Breast Cancer by Combining Polyamine Metabolism-related Genes to Help With Immunotherapy.

This project aims to explore the clustering value of polyamine metabolism-related genes (PMRGs) in breast cancer (BC) to assist treatment. ConsensusClusterPlus R package was employed to cluster BC patients based on the expression of PMRGs. Using the edgeR R package, we analyzed differentially expressed genes (DEGs) of different molecular clusters. Core genes were screened and enriched by the PPI network. Univariate COX was applied to determine genes tightly linked with survival. ConsensusClusterPlus R package was employed to cluster PMRGs. Differences in immune infiltration and expression of immune checkpoints between 2 subgroups were analyzed. Response to immunotherapy was assessed based on the expression level of immunophenoscore (IPS). Drug sensitivity of different PMRG clusters was assessed by pRRophitic R package. We clustered BC patients into 2 different subtypes with different survival rates and biological functions based on the expression of 16 PMRGs. Application of univariate COX analysis identified genes greatly associated with survival and divided BC patients into 2 different PMRG clusters. Patients in the 2 clusters exhibited differences in overall survival rate and immune cell infiltration levels, with multiple immune cells displaying higher immune levels in PMRG cluster 2. PMRG cluster 2 demonstrated higher expression of HLA and IC as well as IPS. Cluster 1 exhibited higher sensitivity to (5Z)-7-Oxozeaenol, 5-Fluorouracil, and 681640, while cluster 2 exhibited higher sensitivity to A-443654 and A-770041. We identified 2 clusters of PMRG with significant differences in the immune microenvironment in BC and predicted potential drugs, aiming to find new directions for clinical treatment of BC.