Identifying Critical States of Hepatocellular Carcinoma Based on Single- Sample Dynamic Network Biomarkers Combined with Simulated Annealing Algorithm

Abstract

Background: Hepatocellular Carcinoma (HCC) is one of the most common malignant tumors. Due to the insidious onset and poor prognosis, most patients have reached the advanced stage at the time of diagnosis. Objective: Studies have shown that Dynamic Network Biomarkers (DNB) can effectively identify the critical state of complex diseases such as HCC from normal state to disease state. Therefore, it is very important to detect DNB efficiently and reliably. Methods: This paper selects a dataset containing eight HCC disease states. First, an individual-specific network is constructed for each sample and features are extracted. In the context of this network, a simulated annealing algorithm is used to search for potential dynamic network biomarker modules, and the evolution of HCC is determined. Results: In fact, in the period of Low-Grade Dysplasia (LGD) and High-Grade Dysplasia (HGD), DNB sends an indicative warning signal, which means that liver dysplasia is a very important critical state in the development of HCC disease. Compared with landscape dynamic network biomarkers method (LDNB), our method can not only describe the statistical characteristics of each disease state, but also yield better results including getting more DNBs enriched in HCC related pathways. Conclusion: The results of this study may be of great significance to the prevention and early diagnosis of HCC.