Development of a risk model to predict prognosis in breast cancer based on cGAS-STING-related genes - Chen, Chen; Wang, Junxiao; Dong, Chao; Lim, David; Feng, Zhihui (Frontiers in Genetics, 2023)
Background: Breast cancer (BRCA) is regarded as a lethal and aggressive cancer with increasing morbidity and mortality worldwide. cGAS-STING signaling regulates the crosstalk between tumor cells and immune cells in the tumor microenvironment (TME), emerging as an important DNA-damage mechanism. However, cGAS-STING-related genes (CSRGs) have rarely been investigated for their prognostic value in breast cancer patients. Methods: Our study aimed to construct a risk model to predict the survival and prognosis of breast cancer patients. We obtained 1087 BRCA samples and 179 normal breast tissue samples from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEX) database, 35 immune-related differently expression genes (DEGs) from CSRGs were systematically assessed. The Cox regression was applied for further selection, and 11 prognostic-related DEGs were used to develop a machine learning-based risk assessment and prognostic model. Results: We successfully developed a risk model to predict the prognostic value of BRCA patients and its performance acquired effective validation. The results derived from Kaplan Meier analysis revealed that the low-risk score patients had better overall survival (OS). The nomogram that integrated the risk score and clinical information was established and has good validity in predicting the OS of BRCA patients. Significant correlations were observed between the risk score and tumor-infiltrating immune cells, immune checkpoints and the response to immunotherapy. The CSRGs risk score was also relevant to a serious of clinic prognostic indicators such as tumor staging, molecular subtype, tumor recurrence, and drug therapeutic sensibility in BRCA patients.