A Novel Approach: Combining Prognostic Models and Network Pharmacology to Target Breast Cancer Necroptosis-Associated Genes

Abstract

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Breast cancer (BC) accounts for the highest proportion of the all cancers among women, and necroptosis is recognized as a form of caspase-independent programmed cell death. We created prognostic signatures using univariate survival analysis, and lasso regression, to assess immune microenvironments between subgroups. We then used network pharmacology to bind our drugs to target differentially expressed genes (DEGs). A signature comprising a set of necroptosis-related genes was established to predict patient outcomes based on median risk scores. Those above and below the median were classified as high-risk group (HRG) and low-risk group (LRG), respectively. Patients at high risk had lower overall survival, and poorer predicted tumor, nodes, and metastases stages (TNM). The novel prognostic signature can effectively predict the prognosis of breast cancer patients docking of β,β-dimethyl acryloyl shikonin (DMAS) to possible targets to cure breast cancer. We found that all current prognostic models do not offer suitable treatment options. In additional, by docking drugs DMAS that have been initially validated in our laboratory to treat breast cancer. We hope that this novel approach could contribute to cancer research.

Keywords

• immune microenvironment
• necroptosis
• network
• breast cancer
• treatment