Pathway mapping and development of disease‐specific biomarkers: protein‐based network biomarkers

Abstract

It is known that a disease is rarely a consequence of an abnormality of a single gene, but reflects the interactions of various processes in a complex network. Annotated molecular networks offer new opportunities to understand diseases within a systems biology framework and provide an excellent substrate for network-based identification of biomarkers. The network biomarkers and dynamic network biomarkers (DNBs) represent new types of biomarkers with protein–protein or gene–gene interactions that can be monitored and evaluated at different stages and time-points during development of disease. Clinical bioinformatics as a new way to combine clinical measurements and signs with human tissue-generated bioinformatics is crucial to translate biomarkers into clinical application, validate the disease specificity, and understand the role of biomarkers in clinical settings. In this article, the recent advances and developments on network biomarkers and DNBs are comprehensively reviewed. How network biomarkers help a better understanding of molecular mechanism of diseases, the advantages and constraints of network biomarkers for clinical application, clinical bioinformatics as a bridge to the development of diseases-specific, stage-specific, severity-specific and therapy predictive biomarkers, and the potentials of network biomarkers are also discussed.

Integrative topological analysis of mass spectrometry data reveals molecular features with clinical relevance in esophageal squamous cell carcinoma

“…In addition, the distinct molecular profile unveiled by different approaches may depict parts of a panorama of tumor and integrative indexes derived from both platforms would improve our understanding of tumor biology and the clinical performance of these individual molecules. Therefore, biomarkers comprising multiple genes identified by different algorithms, which represent a complexity of multiple functional dysregulation, would provide more insightful understanding of malignant disease biology and consistently outperform individual genes across different populations 5 7 8 9 10 16 22 23 24 25 28 29 43 44 . Since SDN defined in the present study was an integrated index of seven topological measures of a human PPI network, the nodes with large absolute SDN values can well reflect their overall importance in the PPI network.…”

Identifying Early Warning Signals for the Sudden Transition from Mild to Severe Tobacco Etch Disease by Dynamical Network Biomarkers

“…Using the growing amount of available time-course high-throughput omic data obtained during viral infections of individuals and taking advantage of the principle of critical slowing down, it would be possible to identify early warning signals of the transition from the healthy normal state to the irreversible disease state. Secondly, the recently developed dynamical network biomarker (DNB) theory [10,[14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], may help in identifying early warning signals indicating an imminent phase transition towards disease. Such DNBs should range from individual molecules (genes, proteins, metabolites) or more complex structures (subnetworks, modules, or pathways) in protein-protein interaction networks (PPINs), transcriptional regulatory networks (TRNs), metabolic networks, or noncoding RNA-mediated regulation of DNA epigenetic marks that are leading towards the critical transitions.…”

Identifying Early-Warning Signals for the Sudden Transition from Mild to Severe Tobacco Rtch Disease by Dynamical Network Biomarkers

“…Using the growing amount of available timecourse high-throughput omic data obtained during viral infections of individuals and taking advantage of the principle of critical slowing down, it would be possible to identify early warning signals of the transition from the healthy normal state to the irreversible disease state. Secondly, recently developed Dynamical Network Biomarker (DNB) theory [10,[14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], may help identifying early-warning signals indicating an imminent phase transition towards disease. Such DNBs should range from individual molecules (genes, proteins, metabolites) or more complex structures (subnetworks, modules or pathways) in protein-protein interaction networks (PPINs), transcriptional regulatory networks (TRNs), metabolic networks, or non-coding RNA-mediated regulation of DNA epigenetic marks that are leading towards the critical transitions.…”