Identifying critical state of breast cancer cell differentiation based on landscape dynamic network biomarkers

Abstract

Breast cancer is a malignant tumor with the highest morbidity and mortality in female in recent years, and it is a complex disease that affects human health. Studies have shown that dynamic network biomarkers (DNB) can effectively identify critical states at which complex diseases such as breast cancer change from a normal state to a disease state. However, the traditional DNB method requires data from multiple samples in the same disease state, which is usually unachievable in clinical diagnosis. This paper quantitatively analyzes the time series data of MCF-7 breast cancer cells and finds the DNB module of a single sample in the time series based on landscape DNB (L-DNB) method. Then, a comprehensive index is constructed to detect its early warning signals to determine the critical state of breast cancer cell differentiation. The results of this study may be of great significance for the prevention and early diagnosis of breast cancer. It is expected that this paper can provide references for the related research of breast cancer.