Gastric cancer biomarkers; A systems biology approach

Gastric cancer is one of the most fatal cancers in the world. Many efforts in recent years have attempted to find effective proteins in gastric cancer. By using a comprehensive list of proteins involved in gastric cancer, scientists were able to retrieve interaction information. The study of protein-protein interaction networks through systems biology based analysis provides appropriate strategies to discover candidate proteins and key biological pathways.In this study, we investigated dominant functional themes and centrality parameters including betweenness as well as the degree of each topological clusters and expressionally active sub-networks in the resulted network. The results of functional analysis on gene sets showed that neurotrophin signaling pathway, cell cycle and nucleotide excision possess the strongest enrichment signals. According to the computed centrality parameters, HNF4A, TAF1 and TP53 manifested as the most significant nodes in the interaction network of the engaged proteins in gastric cancer. This study also demonstrates pathways and proteins that are applicable as diagnostic markers and therapeutic targets for future attempts to overcome gastric cancer.     

Adjusting confounders in ranking biomarkers: a model-based ROC approach

High-throughput studies have been extensively conducted in the research of complex human diseases. As a representative example, consider gene-expression studies where thousands of genes are profiled at the same time. An important objective of such studies is to rank the diagnostic accuracy of biomarkers (e.g. gene expressions) for predicting outcome variables while properly adjusting for confounding effects from low-dimensional clinical risk factors and environmental exposures. Existing approaches are often fully based on parametric or semi-parametric models and target evaluating estimation significance as opposed to diagnostic accuracy. Receiver operating characteristic (ROC) approaches can be employed to tackle this problem. However, existing ROC ranking methods focus on biomarkers only and ignore effects of confounders. In this article, we propose a model-based approach which ranks the diagnostic accuracy of biomarkers using ROC measures with a proper adjustment of confounding effects. To this end, three different methods for constructing the underlying regression models are investigated. Simulation study shows that the proposed methods can accurately identify biomarkers with additional diagnostic power beyond confounders. Analysis of two cancer gene-expression studies demonstrates that adjusting for confounders can lead to substantially different rankings of genes.     

Discovery of biomarkers for gastric cancer: a proteomics approach

Gastric cancer is the second leading cause of cancer-related deaths worldwide. Although many treatment options exist for patients with gastric tumors, the incidence and mortality rate of gastric cancer are on the rise. The early stages of gastric cancer are non-symptomatic, and the treatment response is unpredictable. This situation is further aggravated by a lack of diagnostic biomarkers that can aid in the early detection and prognosis of gastric cancer and in the prediction of chemoresistance. Moreover, clinical surgical specimens are rarely obtained, and traditional biomarkers of gastric cancer are not very effective. Many studies in the field of proteomics have contributed to the discovery and establishment of powerful diagnostic tools (e.g., ProteinChip array) in the management of cancer. The evolution in proteomic technologies has not only enabled the screening of a large number of samples but also enabled the identification of pathologically significant proteins, such as phosphoproteins, and the quantitation of difference in protein expression under different conditions. Multiplexed assays are used widely to accurately fractionate various complex samples such as blood, tissue, cells, and Helicobacter pylori-infected specimens to identify differentially expressed proteins. Biomarker detection studies have substantially contributed to the areas of secretome, metabolome, and phosphoproteome. Here, we review the development of potential biomarkers in the natural history of gastric cancer, with specific emphasis on the characteristics of target protein convergence.     

Platelet biomarkers for a descending cognitive function: A proteomic approach

Memory loss is the most common clinical sign in Alzheimer''s disease (AD); thus, searching for peripheral biomarkers to predict cognitive decline is promising for early diagnosis of AD. As platelets share similarities to neuron biology, it may serve as a peripheral matrix for biomarkers of neurological disorders. Here, we conducted a comprehensive and in‐depth platelet proteomic analysis using TMT‐LC‐MS/MS in the populations with mild cognitive impairment (MCI, MMSE = 18–23), severe cognitive impairments (AD, MMSE = 2–17), and the age‐/sex‐matched normal cognition controls (MMSE = 29–30). A total of 360 differential proteins were detected in MCI and AD patients compared with the controls. These differential proteins were involved in multiple KEGG pathways, including AD, AMP‐activated protein kinase (AMPK) pathway, telomerase RNA localization, platelet activation, and complement activation. By correlation analysis with MMSE score, three positively correlated pathways and two negatively correlated pathways were identified to be closely related to cognitive decline in MCI and AD patients. Partial least squares discriminant analysis (PLS‐DA) showed that changes of nine proteins, including PHB, UQCRH, CD63, GP1BA, FINC, RAP1A, ITPR1/2, and ADAM10 could effectively distinguish the cognitively impaired patients from the controls. Further machine learning analysis revealed that a combination of four decreased platelet proteins, that is, PHB, UQCRH, GP1BA, and FINC, was most promising for predicting cognitive decline in MCI and AD patients. Taken together, our data provide a set of platelet biomarkers for predicting cognitive decline which may be applied for the early screening of AD.     

Ventilatory support: a dynamical systems approach

Misunderstanding of the dynamical behavior of the ventilatory system, especially under assisted ventilation, may explain the problems encountered in ventilatory support monitoring. Proportional assist ventilation (PAV) that theoretically gives a breath by breath assistance presents instability with high levels of assistance. We have constructed a mathematical model of interactions between three objects: the central respiratory pattern generator modelled by a modified Van der Pol oscillator, the mechanical respiratory system which is the passive part of the system and a controlled ventilator that follows its own law. The dynamical study of our model shows the existence of two crucial behaviors, i.e. oscillations and damping, depending on only two parameters, namely the time constant of the mechanical respiratory system and a cumulative interaction index. The same result is observed in simulations of spontaneous breathing as well as of PAV. In this last case, increasing assistance leads first to an increase of the tidal volume (V_T), a further increase in assistance inducing a decrease in V_T, ending in damping of the whole system to an attractive fixed point. We conclude that instabilities observed in PAV may be explained by the different possible dynamical behaviors of the system rather than changes in mechanical characteristics of the respiratory system.     

Gustatory processing: a dynamic systems approach

Recent gustatory studies have provided a growing body of evidence that taste processing is dynamic and distributed, and the taste system too complex to be adequately described by traditional feed-forward models of taste coding. Current research demonstrates that neuronal responses throughout the gustatory neuroaxis are broad, variable and temporally structured, as a result of the fact that the taste network is extensive and heavily interconnected, containing modulatory pathways, many of which are reciprocal. Multimodal influences (e.g. olfactory and somatosensory) and effects of internal state (e.g. attention and expectation), shown in both behavioral and neuronal responses to taste stimuli, add further complexity to neural taste responses. Future gustatory research should extend to more brain regions, incorporate more connections, and analyze behaviors and neuronal responses in both time- and state-dependent manners.     

Cancer Vaccines '98: a reductionistic approach

Cancer Vaccines '98Bethesda, MD, USA, 27–28 April 1998     

Functional landscapes in cities: a systems approach

Human enterprise and endeavour increasingly influence global processes of change, from the planetary scale down to the very local. Cities are hubs of human activity, and as the places where the majority of the world’s population live we must, when looking into an uncertain future, consider how we think about urban design. Cities are densely inhabited, lived-in landscapes where human presence and perceptions are deeply enmeshed with biophysical and built infrastructures. As such, they present complex mosaics of different habitats and competing uses, ever changing in response to human and physical drivers. If designed properly, green infrastructure can contribute many important functions to a city. Efforts to strategically make use of green infrastructure can benefit considerably from a systems perspective where linkages and cross-boundary dynamics are at the very least as important as individual components. Design, planning and governance of requirements for green infrastructure also extend far beyond biophysical elements and components. Recognition of interconnections between individual green spaces, green infrastructure and the built environment, the physical environment and diverse actors, and formal and informal governance arrangements—as outlined in the four design principles in this article—is a first important step towards a more comprehensive and inclusive approach, not least to green infrastructure planning and design.     

Taking a systems approach to ecological systems

Increasingly, there is interest in a systems‐level understanding of ecological problems, which requires the evaluation of more complex, causal hypotheses. In this issue of the Journal of Vegetation Science, Soliveres et al. use structural equation modeling to test a causal network hypothesis about how tree canopies affect understorey communities. Historical analysis suggests structural equation modeling has been under‐utilized in ecology.     

Early warning biomarkers in major depressive disorder: a strategic approach to a testing question

Purpose: Identification of biomarkers in major depressive disorder (MDD) has proceeded in an extemporised manner. No single biomarker has been identified with utility in screening, diagnosis, prognosis, or monitoring, and screening tests have different characteristics than the other functions. Using chaos, bifurcation, and perturbation (CBP) theories, the aim is to identify biomarkers to aid clinicians in screening for MDD.

Materials and methods: MDD is a complex disorder; consequently, a reductionist approach to characterize the complex system changes found in MDD will be inchoate and unreliable. A holistic approach is used to identify biomarkers reflecting the tipping points seen before the catastrophic bifurcation that results in MDD.

Results: Applying CBP theories revealed skew, resistance to change, flickering, increased variance and autocorrelation as patterns of biomarkers. Integrals and differentials of extracellular and intracellular biomarkers were identified, specifically focussed on hypothalamo-pituitary axis (HPA) dysfunction, metabolic dysfunction, inflammation and mitochondrial oxidative stress, and tryptophan metabolism.

Conclusions: Applying CBP theories to the dysfunctional complex biological systems in MDD led to development of integrals and differentials of biomarkers that can be used in screening for MDD and planning future biomarker research, targeting intracellular and extracellular inflammation, HPA axis dysfunction, and tryptophan metabolism.     

GATHER: a systems approach to interpreting genomic signatures

MOTIVATION: Understanding the full meaning of the biology captured in molecular profiles, within the context of the entire biological system, cannot be achieved with a simple examination of the individual genes in the signature. To facilitate such an understanding, we have developed GATHER, a tool that integrates various forms of available data to elucidate biological context within molecular signatures produced from high-throughput post-genomic assays. RESULTS: Analyzing the Rb/E2F tumor suppressor pathway, we show that GATHER identifies critical features of the pathway. We further show that GATHER identifies common biology in a series of otherwise unrelated gene expression signatures that each predict breast cancer outcome. We quantify the performance of GATHER and find that it successfully predicts 90% of the functions over a broad range of gene groups. We believe that GATHER provides an essential tool for extracting the full value from molecular signatures generated from genome-scale analyses. AVAILABILITY: GATHER is available at http://gather.genome.duke.edu/     

Development of motor systems: a comparative approach.

Anurans offer a unique opportunity to study the development of neuronal connections. Transition from the aquatic limbless tadpole to the juvenile occurs over a protracted period of time during which the animal is accessible for experimental studies. Moreover, tract-tracing studies have demonstrated that their descending brain stem pathways show remarkable similarities in origin, course and site of termination to those of mammals. A developmental sequence in the formation of descending pathways to the spinal cord has been shown implying that reticulospinal and vestibulospinal fibers innervate spinal segments very early in development, whereas the red nucleus projects spinalwards definitely later in development. In anurans, this developmental sequence parallels the changes observed in locomotor pattern. The ingrowth of descending pathways into the spinal cord possibly occurs along so-called 'pre-existing' tracts. Several hypotheses on guidance cues in axonal pathfinding will be discussed.     

Variable complementary network: a novel approach for identifying biomarkers and their mutual associations

Biological variables involved in a disease process often correlate with each other through for example shared metabolic pathways. In addition to their correlation, these variables contain complementary information that is particularly useful for disease classification and prediction. However, complementary information between variables is rarely explored. Therefore, establishing methods for the investigation of variable??s complementary information is very necessary. We propose a model population analysis approach that aggregates information of a number of classification models obtained with the help of Monte Carlo sampling in variable space for quantitatively calculating the complementary information between variables. We then assemble these complementary information to construct a variable complementary network (VCN) to give an overall visualization of how biological variables complement each other. Using a simulated dataset and two metabolomics datasets, we show that the complementary information is effective in biomarker discovery and that mutual associations of metabolites revealed by this method can provide information for exploring altered metabolic pathways. (The source codes for implementing VCN in MATLAB are freely available at: http://code.google.com/p/vcn2011/.)     

Complex systems in pulmonary medicine: a systems biology approach to lung disease

The lung is a highly complex organ that can only be understood by integrating the many aspects of its structure and function into a comprehensive view. Such a view is provided by a systems biology approach, whereby the many layers of complexity, from the molecular genetic, to the cellular, to the tissue, to the whole organ, and finally to the whole body, are synthesized into a working model of understanding. The systems biology approach therefore relies on the expertise of many disciplines, including genomics, proteomics, metabolomics, physiomics, and, ultimately, clinical medicine. The overall structure and functioning of the lung cannot be predicted from studying any one of these systems in isolation, and so this approach highlights the importance of emergence as the fundamental feature of systems biology. In this paper, we will provide an overview of a systems biology approach to lung disease by briefly reviewing the advances made at many of these levels, with special emphasis on recent work done in the realm of pulmonary physiology and the analysis of clinical phenotypes.