Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development

Abstract

The dynamic network biomarker (DNB) method has advanced since it was first proposed. This review discusses advances in the DNB method that can identify the dynamic change in the expression signature related to the critical time point of disease progression by utilizing different kinds of transcriptome data. The DNB method is good at identifying potential biomarkers for cancer and other disease development processes that are represented by a limited molecular profile change between the normal and critical stages. We highlight that the cancer tipping point or premalignant state has been widely discovered for different types of cancer by using the DNB method that utilizes bulk or single-cell RNA sequencing data. This method could also be applied to other dynamic research studies and help identify early warning signals, such as the prediction of a pre-outbreak of COVID-19. We also discuss how the identification of reliable biomarkers of cancer and the development of new methods can be utilized for early detection and intervention and provide insights into emerging paths of the widespread biomarker candidate pool for further validation and disease/health management.

Keywords: Cancer; Complex disease; Dynamic network biomarker; Tipping point.

Fig. 1

Introduction of dynamic network biomarkers. (A) Key theory of dynamic network biomarkers. (B) Algorithm and basic principle of dynamic network biomarkers. (C) Dynamic network biomarkers have an early warning function for the critical stage before the occurrence of disease. (D) The schematic illustration for identifying the dynamical network biomarkers (DNBs).