Inflammatory-related Genes Predicts Prognosis in Breast Cancer

Background: Breast cancer has become the most common malignant tumor around the world. Although most breast cancer patients have a good prognosis, due to the significant heterogeneity of breast cancer and results in the difference in prognosis, it is urgent to find new prognostic biomarkers. Recently, inflammatory-related genes have been proven to play an important role in the development and progression of breast cancer, we aimed to investigate the prognostic value of inflammatory-related genes in breast cancers. Methods: We evaluated the relationship between inflammatory-related genes (IRGs) and breast cancer by analyzing the TCGA database. Difference and univariate Cox regression analysis were performed and then screened out prognosis related differentially expressed inflammatory genes. The prognostic model was constructed through the Least Absolute Shrinkage and Selector Operation (LASSO) regression based on the IRGs. Then we used the Kaplan-Meier curves and receiver operating characteristic (ROC) curve to evaluate the accuracy of the prognostic model. And further, the nomogram model was established to clinically predict the survival rate of breast cancer patients. We also analyzed the immune cell infiltration and the activity of the immune-related pathways based on the expression of prognostic. The CellMiner database was used to study drug sensitivity. Finally, we performed functional cellular experiments to determine the biological characteristics of IRGs in breast cancer cells. Results: In this study, seven IRGs were selected from eleven prognosis-related differential inflammation genes through LASSO Cox regression analysis to construct a prognostic risk model. The risk score was negatively correlated with the prognosis of breast cancer patients through further analysis. ROC curve demonstrated the accuracy of the prognostic model and the nomogram could also accurately predict the survival rate. The ssGSEA analysis was used to quantify the differences between the low- and high-risk groups for the scores of tumor-infiltrating immune cells and immune-related pathways. Then we explored the relationship between the genes which were a researcher in the model and the susceptibility of drugs. Finally, we performed cellular functional experiments and validated the accuracy of the model.