Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and the third leading cause of cancer-related deaths around the world [1
]. Hepatocellular carcinoma is clinically characterized by a high incidence rate and very poor prognosis [2
]. Currently, it is generally accepted that persistent hepatitis B virus (HBV) or hepatitis C virus (HCV) infections is the primary cause of chronic liver disease leading to HCC [3
]. Hepatitis C virus infection is the main risk factor in western countries and Japan. Despite the progress made in numerous treatments, the survival rate of HCC patients remains low because HCC is not easily detected prior to the advanced stage. Thus, it is of utmost importance to clinically diagnose early HCC.
In recent years, the concept of multi-step human hepatocarcinogenesis has been well documented [4
]. The liver injury induced by HCV produces a progressive inflammatory milieu that results in a cycle of necrosis and regeneration leading to liver cirrhosis. Subsequently, cirrhosis patients often present dysplastic nodules. These lesions which are confirmed as precancerous lesions of HCC are classified as low-grade dysplastic nodules (LGDNs) and high-grade dysplastic nodules (HGDNs) based on presence of cytologic and architectural atypia [7
]. Although the morphology of these nodules is not sufficient to support a diagnosis of malignant tumor, these nodules are closely correlated with the occurrence of HCC. And, the HGDNs are more likely transformed into HCC than LGDNs based on clinical, pathological, molecular genetics, and radiological assessments [8
]. The sequence of HCC initiation and progression is shown in Figure 1
A, but the precise molecular events and their regulatory networks that underlie HCC formation remain largely unknown.
Recently, a novel, model-free approach based on nonlinear dynamic theory, termed dynamic network biomarkers (DNB), was developed to detect critical transitions or tipping points during the progression of complex diseases [12
]. Generally, a disease progression can be divided into three stages, i.e., normal state, critical state (or the tipping point), and disease state (Figure 2
A). After the tipping point moves gradually from the normal state, the system drastically deteriorates to a disease state. Specifically, DNB is a group of molecules (i.e., genes, RNAs, proteins, or metabolites) with strongly collective fluctuations. Based on nonlinear dynamical theory, DNB appears only at the tipping point of a homeostatic system and the molecules in DNB are strongly correlated and also fluctuated just before the critical transition (i.e., the tipping point or pre-disease state). Quantitative criteria for the DNB can be obtained by measuring the differential correlations and deviations of molecular expressions rather than the differential expressions adopted in the traditional methods. In contrast to the “disease diagnosis” by traditional biomarkers, DNB is for “disease prediction” (i.e., for the pre-disease diagnosis as the early-warning signals of the disease state). If the state of a system passes over the tipping point to the disease state, it becomes very difficult to reverse to the normal state even by advanced medical treatment. Therefore, it is crucial to identify the pre-disease state so as to prevent the irreversible deterioration of the disease. In addition to complex diseases, DNB theory had also been applied to detect the tipping points in cell fate decisions and immune checkpoint blockade processes [14
Given the difficulty to diagnose early HCC, it is a key problem to identify when and how the tipping point or the critical transition happens at a molecular level. In this work, from stage-wise gene expression profiles of HCC initiation (i.e., normal, cirrhosis, LGDNs, HGDNs, and very early HCC), we identified the tipping point or pre-HCC state of HCV-induced HCC by DNB model. The obtained DNB formed a specific module with 59 genes at the LGDNs stage to signal the tipping point just before the drastic deterioration in HCC progression. We also partially revealed molecular mechanism on the HCC initiation by functional analysis of DNB, which both provides biological insights into the dynamic regulations of the critical transitions and opens a new way for the identification of therapeutic targets. We further identified biological pathways responsible for the critical transition, including several pathways in immune system reactions, cell growth and death, and cell adhesion. And furthermore, DNB was validated as an effective predictor of prognosis for HCC patients by survival analysis on independent data.
In this study, we identified a critical stage (or pre-HCC) just before the crucial transition from preneoplastic lesions to HCC based on multi-stage gene expression profiles during HCC progression by DNB theory. Unlike traditional molecular biomarkers which usually distinguish disease state from normal state, DNB can detect the critical state just before the disease state to realize early. Although the DNB method is a model-free approach, it generally requires multiple samples in each sampling period so as to detect the tipping point. Based on the three conditions of DNB, LGDNs was revealed as the tipping point during multistep hepatocarcinogenesis. It was consistent with reports in the literature that found that after the LGDNs stage, the disease drastically deteriorated to HGDNs, which is the closest manifestation to HCC, based on histopathological features and clinical follow-up studies with a high risk of transformation [5
]. Although each step in HCC progression could be a target for prevention of HCC, intervention before the tipping point could be more practical.
It should be noted that the DNB subnetwork is not necessarily a set of driving factors [12
], but provides the early-warning signals of the pre-HCC state, thus predicting the upcoming HCC onset before the occurrence of the disease phenotype based on its dynamic features. Moreover, DNB can also be implemented to analyze the underlying molecular mechanisms of disease initiation at a network level. The functional enrichment of GO biological processes and pathways validated the significant correlation between hepatocarcinogenesis and the DNB. Most of the enriched terms were related to inflammation, immune responses, cell proliferation, differentiation, cell migration, and apoptosis, such as negative regulation of viral genome replication, viral carcinogenesis, the type I interferon signaling pathway, RIG-I-like receptor signaling, TGF-β signaling pathway, and JAK–STAT signaling pathway. These analyses indicate that these biological processes and pathways play a remarkable role resulting in hepatocarcinogenesis, and the genes in DNB make them move to the disease phenotype on HCC initiation.
In DNB, only two genes presented significantly differential expressions between cirrhosis and HGDNs. However, 43 DNB members were differently expressed between normal tissues and HCC tissues, implying the effectiveness of DNB members in HCC progression. The effect of DNB at HCC onset depended not on their differential expressions, but on collective fluctuations according to DNB theory. Thus, we detected the relation between DNB and DEGs during critical transition at a network level. The DEGs before and after the tipping point were significantly enriched in the DNB-associated network, further implying that DNB plays core functional roles in coordinating the critical transition from preneoplastic lesions to HCC, which results in drastic deterioration phenomena after LGDNs. We also found that in the HCC related pathways which contained genes from both DNB and DEGs, several DNB members stayed at relatively important places, such as receptors, upstream regulators and transcription factors. Hence, we hypothesized that the emergence of DNB modules made the expression of those closely related genes change considerably and caused several vital biological processes to become abnormal, ultimately leading to the drastic deterioration to the irreversible HCC state. Although only two DNB members (RBX1 and ISG20) belonged to DEGs, the pathways significantly regulated by DNB and DEGs were similar. During the critical transition, pathways in immune response and cell adhesion were down-regulated, such as the JAK–STAT signaling pathway, NF-κB signaling pathway, and RIG-I-like receptor signaling pathway. Meanwhile, pathways related to cell growth and death, such as cell cycle, the Notch signaling pathway, and Hedgehog signaling pathway were up-regulated. Moreover, it had been reported that immune response and cell adhesion were upregulated in cirrhosis and downregulated on HCC initiation, and cell proliferation was upregulated on HCC initiation [41
]. It could be inferred that the HGDNs stage was very close to HCC onset and demonstrated the LGDNs stage as the tipping point during hepatocarcinogenesis.
Furthermore, we performed survival analysis based on 122 HCV-induced patients for detecting whether or not DNB has clinical application value. The results show that DNB is effective as a clinical predictor of prognosis for HCV-induced HCC patients. In conclusion, DNB during hepatocarcinogenesis can be used as early-warning signals of HCC, and this work also opens a new way to understand the underlying mechanisms responsible for HCC initiation and provides a new method to facilitate the identification of molecular targets. This method can also be applied to the analysis of other diseases [42
] in a similar manner.