A Novel Approach: Combining Prognostic Models and Network Pharmacology to Target Breast Cancer Necroptosis-Associated Genes - Yan, Congzhi; Liu, Conghui; Wu, Zhixuan; Dai, Yinwei; Xia, Erjie; Hu, Wenjing; Dai, Xuanxuan (Frontiers in Genetics, 2022)
Breast cancer (BC) accounts for the highest proportion of new cancers among women, and necrotizing apoptosis is recognized as a form of caspase-independent programmed cell death. This study aimed to identify a necroptosis-related gene signature, evaluate the associated overall survival, and treat diseases using targeted drugs. We created prognostic signatures using univariate, least absolute shrinkage, and selection operator Cox regression analyses to assess immune microenvironments between subgroups. We then used network pharmacology to bind our drugs to target differentially expressed genes. A signature comprising 38 necroptosis-related genes was established to predict patient outcomes based on median risk scores. Those above and below the median were classified as high and low risk, respectively. Our risk scores had strong predictive power. Patients at high risk had lower overall survival, and poorer predicted TNM stages. The novel prognostic signature was associated with necroptosis-predicted patient outcomes, and we docked differentially expressed genes using β,β-dimethyl acryloyl shikonin. We found that all current prognostic models do not offer suitable treatment options. Therefore, by docking drugs that have been initially validated in our laboratory to treat BC, we hope that this new combination model will facilitate the release of new cyberpharmacology articles, promoting the clinical application of our β,β-dimethyl acryloyl shikonin.