Low-Grade Dysplastic Nodules Revealed as the Tipping Point during Multistep Hepatocarcinogenesis by Dynamic Network Biomarkers

Abstract

Hepatocellular carcinoma (HCC) is a complex disease with a multi-step carcinogenic process from preneoplastic lesions, including cirrhosis, low-grade dysplastic nodules (LGDNs), and high-grade dysplastic nodules (HGDNs) to HCC. There is only an elemental understanding of its molecular pathogenesis, for which a key problem is to identify when and how the critical transition happens during the HCC initiation period at a molecular level. In this work, for the first time, we revealed that LGDNs is the tipping point (i.e., pre-HCC state rather than HCC state) of hepatocarcinogenesis based on a series of gene expression profiles by a new mathematical model termed dynamic network biomarkers (DNB)—a group of dominant genes or molecules for the transition. Different from the conventional biomarkers based on the differential expressions of the observed genes (or molecules) for diagnosing a disease state, the DNB model exploits collective fluctuations and correlations of the observed genes, thereby predicting the imminent disease state or diagnosing the critical state. Our results show that DNB composed of 59 genes signals the tipping point of HCC (i.e., LGDNs). On the other hand, there are a large number of differentially expressed genes between cirrhosis and HGDNs, which highlighted the stark differences or drastic changes before and after the tipping point or LGDNs, implying the 59 DNB members serving as the early-warning signals of the upcoming drastic deterioration for HCC. We further identified the biological pathways responsible for this transition, such as the type I interferon signaling pathway, Janus kinase–signal transducers and activators of transcription (JAK–STAT) signaling pathway, transforming growth factor (TGF)-β signaling pathway, retinoic acid-inducible gene I (RIG-I)-like receptor signaling pathway, cell adhesion molecules, and cell cycle. In particular, pathways related to immune system reactions and cell adhesion were downregulated, and pathways related to cell growth and death were upregulated. Furthermore, DNB was validated as an effective predictor of prognosis for HCV-induced HCC patients by survival analysis on independent data, suggesting a potential clinical application of DNB. This work provides biological insights into the dynamic regulations of the critical transitions during multistep hepatocarcinogenesis.

Two Novel Methods for Extracting Synchronously Fluctuated Genes

“…The development of methods for transcriptome data analysis is one of the important research topics in the field of bioinformatics. In this study, I focus on the extraction of synchronously fluctuated genes (SFGs), which were also called dynamical network biomarkers in previous studies [1], [2], [3], [4], [5], [6], [7], [8], [9]. SFGs are similar to but different from differentially expressed genes (DEGs).…”

“…SFGs have been investigated by several research groups [1], [2], [3], [4], [5], [6], [7], [8], [9] based on an expectation that they are useful for characterizing the predisease state [1], that is, a state before the disease onset with lowered stability and abnormal homeostasis. The characterization of the predisease state may lead to rational and efficient disease prevention.…”

“…In addition, SFGs are also expected to be useful for the diagnosis of the predisease state [1], although the cost of DNA microarray or RNA-seq measurements seems to be too high at present for the purpose of health monitoring. Putative predisease states before several diseases have been claimed to be identified and characterized using SFGs: acute respiratory distress syndrome [1], [5], [6], hepatocellular carcinoma [8], [9], diabetes mellitus [2], [3], [5], and influenza [5]. In addition, preceding oku@inm.u-toyama.ac.jp states before cell differentiation in in-vitro experiments have also been characterized using SFGs [4], [7].…”

“…It is because if the inherent homeostatic functions of a living organism is impaired, it would become dif- c 2019 Information Processing Society of Japan ficult for some genes to keep constant expressions, and those genes' expressions may begin to fluctuate synchronously due to interactions [1]. Although this hypothesis has not been proved by rigorous experiments, it is gaining attention, and several studies have been conducted based on it [1], [2], [3], [4], [5], [6], [7], [8], [9].…”

“…Previous studies used various methods for extracting SFGs [1], [2], [3], [4], [5], [6], [7], [8], [9]. A standard method has not been established, and almost all studies used mutually distinct procedures.…”