Identifying critical transitions and their leading biomolecular networks in complex diseases

Li, Rui; Li, Meiyi; Liu, Zhiping; Wu, Jiarui; Chen, Luonan; Aihara, Kazuyuki

doi:10.1038/srep00813

Cited by 158 publications

(178 citation statements)

References 31 publications

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“…Equation (3) is the same as the one used in [9], but with a negative sign to obtain positive values of entropy and to have units of information in bits. Gene expression entropies (Figure 2, Tables 1-4) were calculated using all selected genes by the double filter to create a single matrix for every stage in each cancer, without using the local networks data.…”

Section: Methodsmentioning

confidence: 99%

“…In the normal state, the disease is under control (immune system) and dynamically it has high resilience and robustness to perturbations. The pre-disease state is defined as the limit of the normal state, which occurs before the imminent phase transition point is reached, but it has low resilience and robustness due to its dynamical structure [9]. The disease state represents a seriously deteriorated stage possibly with high resilience and robustness, where the system usually finds it difficult to recover or return to the normal state even after treatment, which contrasts with the pre-disease state.…”

Section: Introductionmentioning

confidence: 99%

“…The detection of early-warning signals can involve a myriad of genetic factors. There are leading networks in critical transitions, which make the first move from a normal state to a disease state [9]. The leading network is the first subnetwork that breaks down the limit of a normal state to move into a disease state, which means that they are clearly related to the causal or driving genes in a disease network, in contrast to the differential gene expression that results from the disease.…”

Section: Introductionmentioning

confidence: 99%

“…Sorafenib is the recommended treatment with patients in advanced stages, but due to its side effects it is difficult for the patient to tolerate it [8]. It is a disease with 10 to 20% recurrence even in patients with liver transplantation, In general, cancer progression can be divided into three states [9]: a normal state, a pre-disease state (or a critical state), and a disease state. In the normal state, the disease is under control (immune system) and dynamically it has high resilience and robustness to perturbations.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate Entropy Characterizes the Gene Expression and Protein-Protein Networks in Four Types of Cancer

Juarez-Flores

José

2018

Entropy

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Abstract:There is an important urgency to detect cancer at early stages to treat it, to improve the patients' lifespans, and even to cure it. In this work, we determined the entropic contributions of genes in cancer networks. We detected sudden changes in entropy values in melanoma, hepatocellular carcinoma, pancreatic cancer, and squamous lung cell carcinoma associated to transitions from healthy controls to cancer. We also identified the most relevant genes involved in carcinogenic process of the four types of cancer with the help of entropic changes in local networks. Their corresponding proteins could be used as potential targets for treatments and as biomarkers of cancer.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multivariate Entropy Characterizes the Gene Expression and Protein-Protein Networks in Four Types of Cancer

Juarez-Flores

José

2018

Entropy

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“…Traditional node and edge biomarkers can distinguish between disease and normal states, but usually cannot diagnose the pre-disease state [7] because the molecular expressions of normal and early disease states are not significantly different. DNBs do distinguish between the pre-disease and normal states because they detect the early-warning signals of complex diseases regardless of sample size [7,8]. Identification of DNB molecules (a group of molecules) is based on the following theory: as the system approaches the pre-disease state or the critical transition, (i) the expression of the DNB molecules dramatically deviates from that of the normal state; (ii) the expression correlation between any two DNB molecules increases; (iii) the expression correlation between any molecule in the DNB and any molecule in the non-DNB decreases.…”

Section: Dynamical Network Biomarkers and Dynamical Edge Biomarkersmentioning

confidence: 99%

Edge biomarkers for classification and prediction of phenotypes

Zeng

Zhang

et al. 2014

Sci. China Life Sci.

Self Cite

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In general, a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network, which can be considered as a set of interactions or edges among molecules. Thus, instead of individual molecules, networks or edges are stable forms to reliably characterize complex diseases. This paper reviews both traditional node biomarkers and edge biomarkers, which have been newly proposed. These biomarkers are classified in terms of their contained information. In particular, we show that edge and network biomarkers provide novel ways of stably and reliably diagnosing the disease state of a sample. First, we categorize the biomarkers based on the information used in the learning and prediction steps. We then briefly introduce conventional node biomarkers, or molecular biomarkers without network information, and their computational approaches. The main focus of this paper is edge and network biomarkers, which exploit network information to improve the accuracy of diagnosis and prognosis. Moreover, by extracting both network and dynamic information from the data, we can develop dynamical network and edge biomarkers. These biomarkers not only diagnose the immediate pre-disease state but also detect the critical molecules or networks by which the biological system progresses from the healthy to the disease state. The identified critical molecules can be used as drug targets, and the critical state indicates the critical point of disease control. The paper also discusses representative biomarker-based methods.biomarker, edge biomarker, dynamical network biomarker, classification, prediction, phenotype, disease diagnosis, disease prognosis Citation:

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Early Diagnosis of Complex Diseases by Molecular Biomarkers, Network Biomarkers, and Dynamical Network Biomarkers

Liu

Wang

Aihara

et al. 2013

Medicinal Research Reviews

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261

157

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Many studies have been carried out for early diagnosis of complex diseases by finding accurate and robust biomarkers specific to respective diseases. In particular, recent rapid advance of high-throughput technologies provides unprecedented rich information to characterize various disease genotypes and phenotypes in a global and also dynamical manner, which significantly accelerates the study of biomarkers from both theoretical and clinical perspectives. Traditionally, molecular biomarkers that distinguish disease samples from normal samples are widely adopted in clinical practices due to their ease of data measurement. However, many of them suffer from low coverage and high false-positive rates or high false-negative rates, which seriously limit their further clinical applications. To overcome those difficulties, network biomarkers (or module biomarkers) attract much attention and also achieve better performance because a network (or subnetwork) is considered to be a more robust form to characterize diseases than individual molecules. But, both molecular biomarkers and network biomarkers mainly distinguish disease samples from normal samples, and they generally cannot ensure to identify predisease samples due to their static nature, thereby lacking ability to early diagnosis. Based on nonlinear dynamical theory and complex network theory, a new concept of dynamical network biomarkers (DNBs, or a dynamical network of biomarkers) has been developed, which is different from traditional static approaches, and the DNB is able to distinguish a predisease state from normal and disease states by even a small number of samples, and therefore has great potential to achieve "real" early diagnosis of complex diseases. In this paper, we comprehensively review the recent advances and developments on molecular biomarkers, network biomarkers, and DNBs in particular, focusing on the biomarkers for early diagnosis of complex diseases considering a small number of samples and high-throughput data (or big data). Detailed comparisons of various types of biomarkers as well as their applications are also discussed.

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Identifying critical transitions and their leading biomolecular networks in complex diseases

Cited by 158 publications

References 31 publications

Multivariate Entropy Characterizes the Gene Expression and Protein-Protein Networks in Four Types of Cancer

Multivariate Entropy Characterizes the Gene Expression and Protein-Protein Networks in Four Types of Cancer

Edge biomarkers for classification and prediction of phenotypes

Early Diagnosis of Complex Diseases by Molecular Biomarkers, Network Biomarkers, and Dynamical Network Biomarkers

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