Development of a random survival forest model based on cuproptosis-related genes (CRGs) for predicting overall survival in patients with invasive breast carcinoma

Background: The clinical decision-making of invasive cancer (BRCA) depends on the prediction of overall survival rate. To predict the overall survival rate of BRCA patients, a random survival forest (RSF) model based on copper poisoning related genes (CRGs) was established. Methods: We analyzed the expression level of CRG using cell lines. The Cancer Genome Map (TCGA)-BRCA data is used to develop and evaluate RSF models. We analyzed the relationships between various clinical parameters, functional enrichment, immune cell ratio and RSF scores, as well as the IC50 of various drugs in the Cancer Drug Sensitivity Genome 2 (GDSC2) database. Results: Compared with normal control cell lines, CRG in BRCA cell lines is upregulated. The RSF model performs well in predicting the overall survival rate of BRCA patients. There were significant differences in RSF scores among BRCA patients in terms of age, radiation status, staging, T staging, and N staging (p-value < 0.05). In BRCA samples with higher RSF scores, hypoxia, glycolysis, mechanism target of rapamycin complex 1 (mTORC1) signaling, DNA replication, and cell cycle were all enhanced; On the contrary, inflammatory responses, natural killer cells, mature B cell differentiation, mediated cytotoxicity, and autophagy regulation are all inhibited. The proportion of immature B cells, activated dendritic cells, resting memory differentiation cluster 4 (CD4) T cells, and follicle helper T cells was significantly correlated with RSF scores (p-value < 0.05), while M2 macrophages, neutrophils, and immature CD4 T cells were negatively correlated. Higher RSF scores were associated with increased resistance to VX-11e_2096 and ERK_6604_1714 but greater sensitivity to Acetalax_1804, WEHI-539_1997 and AZD5991_1720. Conclusions: The RSF score is related to various clinical features, immune cell ratio, and drug sensitivity. It is an effective tool for predicting the overall survival rate of BRCA patients.