Identification of a super enhancer associated gene signature for the prognosis prediction and regulatory mechanism exploration in breast cancer.

This study aims to preliminarily explore the impact of super-enhancer (SE)-associated genes on breast cancer (BC) and their potential regulatory mechanisms. We first identified differentially expressed SE-associated genes between BC patients and healthy controls. Subsequently, Cox regression analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm were applied to evacuate SE-associated genes that related to the overall survival (OS) of patients. The Super-Enhancer-Related Score (SERS) was then calculated based on the LASSO coefficients and incorporated with clinical features to construct a clinical prediction model. Besides, we applied multiple bioinformatic approaches to investigate the potential regulatory mechanism of SE-associated genes using bulk RNA and single-cell RNA sequencing data. Finally, the hub gene was identified using various machine learning methods and Immunohistochemistry (IHC) assay. We found that SERS was negatively correlated with OS of BC patients. Both time-dependent ROC analysis and calibration curves demonstrated the strong predictive ability and high accuracy of the prediction model we built. Significant differences were observed between the two groups in terms of tumor mutation burden, tumor immune microenvironment, and drug sensitivity. Importantly, we identified TFF1 as the core gene, and the immunohistochemical (IHC) assay using the tissue microarray revealed that TFF1-positive patients had significantly better OS than their counterparts. We constructed a highly precise clinical prediction model based on SERS, demonstrating the impact of SE-associated genes on the prognosis of BC patients and their regulatory effects on somatic mutations, tumor immune microenvironment, and drug sensitivity in BC.