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Selection of disease-specific biomarkers by integrating inflammatory mediators with clinical informatics in AECOPD patients: a preliminary study.

ABSTRACT: Systemic inflammation is a major factor influencing the outcome and quality of patient with chronic obstructive pulmonary disease (COPD) and acute exacerbations (AECOPD). Because of the inflammatory complexity, a great challenge is still confronted to optimize the identification and validation of disease-specific biomarkers. This study aimed at developing a new protocol of specific biomarker evaluation by integrating proteomic profiles of inflammatory mediators with clinical informatics in AECOPD patients, understand better their function and signal networks. Plasma samples were collected from healthy non-smokers or patients with stable COPD (sCOPD) or AECOPD on days 1 and 3 of the admission and discharging day (day 7-10). Forty chemokines were measured using a chemokine multiplex antibody array. Clinical informatics was achieved by a Digital Evaluation Score System (DESS) for assessing severity of patients. Chemokine data was compared among different groups and its correlation with DESS scores was performed by SPSS software. Of 40 chemokines, 30 showed significant difference between sCOPD patients and healthy controls, 16 between AECOPD patients and controls and 13 between AECOPD patients and both sCOPD and controls, including BTC, IL-9, IL-18Bpa, CCL22,CCL23, CCL25, CCL28, CTACK, LIGHT, MSPa, MCP-3, MCP-4 and OPN. Of them, some had significant correlation with DESS scores. There is a disease-specific profile of inflammatory mediators in COPD and AECOPD patients which may have a potential diagnostics together with clinical informatics of patients. Our preliminary study suggested that integration of proteomics with clinical informatics can be a new way to validate and optimize disease-special biomarkers.

SUBMITTER: Chen H

PROVIDER: S-EPMC3823081 | biostudies-other | 2012 Jun

REPOSITORIES: biostudies-other

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Selection of disease-specific biomarkers by integrating inflammatory mediators with clinical informatics in AECOPD patients: a preliminary study.

Chen Hong H Song Zhenju Z Qian Mengjia M Bai Chunxue C Wang Xiangdong X

Journal of cellular and molecular medicine 20120601 6

Systemic inflammation is a major factor influencing the outcome and quality of patient with chronic obstructive pulmonary disease (COPD) and acute exacerbations (AECOPD). Because of the inflammatory complexity, a great challenge is still confronted to optimize the identification and validation of disease-specific biomarkers. This study aimed at developing a new protocol of specific biomarker evaluation by integrating proteomic profiles of inflammatory mediators with clinical informatics in AECOP ...[more]

PMID: 21883889

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Project description:The tremendous boost in next generation sequencing and in the "omics" technologies makes it possible to characterize the human gut microbiome-the collective genomes of the microbial community that reside in our gastrointestinal tract. Although some of these microorganisms are considered to be essential regulators of our immune system, the alteration of the complexity and eubiotic state of microbiota might promote autoimmune and inflammatory disorders such as diabetes, rheumatoid arthritis, Inflammatory bowel diseases (IBD), obesity, and carcinogenesis. IBD, comprising Crohn's disease and ulcerative colitis, is a gut-related, multifactorial disease with an unknown etiology. IBD presents defects in the detection and control of the gut microbiota, associated with unbalanced immune reactions, genetic mutations that confer susceptibility to the disease, and complex environmental conditions such as westernized lifestyle. Although some existing studies attempt to unveil the composition and functional capacity of the gut microbiome in relation to IBD diseases, a comprehensive picture of the gut microbiome in IBD patients is far from being complete. Due to the complexity of metagenomic studies, the applications of the state-of-the-art machine learning techniques became popular to address a wide range of questions in the field of metagenomic data analysis. In this regard, using IBD associated metagenomics dataset, this study utilizes both supervised and unsupervised machine learning algorithms, (i) to generate a classification model that aids IBD diagnosis, (ii) to discover IBD-associated biomarkers, (iii) to discover subgroups of IBD patients using k-means and hierarchical clustering approaches. To deal with the high dimensionality of features, we applied robust feature selection algorithms such as Conditional Mutual Information Maximization (CMIM), Fast Correlation Based Filter (FCBF), min redundancy max relevance (mRMR), Select K Best (SKB), Information Gain (IG) and Extreme Gradient Boosting (XGBoost). In our experiments with 100-fold Monte Carlo cross-validation (MCCV), XGBoost, IG, and SKB methods showed a considerable effect in terms of minimizing the microbiota used for the diagnosis of IBD and thus reducing the cost and time. We observed that compared to Decision Tree, Support Vector Machine, Logitboost, Adaboost, and stacking ensemble classifiers, our Random Forest classifier resulted in better performance measures for the classification of IBD. Our findings revealed potential microbiome-mediated mechanisms of IBD and these findings might be useful for the development of microbiome-based diagnostics.

Project description:Mechanisms that contribute to myocardial fibrosis, particularly in response to left ventricular pressure overload (LVPO), remain poorly defined. To test the hypothesis that a myocardial-specific profile of secreted factors is produced in response to PO, levels of 44 factors implicated in immune cell recruitment and function were assessed in a murine model of cardiac hypertrophy and compared with levels produced in a model of pulmonary fibrosis (PF). Mice subjected to PO were assessed at 1 and 4 wk. Protein from plasma, LV, lungs, and kidneys were analyzed by specific protein array analysis in parallel with protein from mice subjected to silica-instilled PF. Of the 44 factors assessed, 13 proteins were elevated in 1-wk PO myocardium, whereas 18 proteins were found increased in fibrotic lung. Eight of those increased in 1-wk LVPO were not found to be increased in fibrotic lungs (CCL-11, CCL-12, CCL-17, CCL-19, CCL-21, CCL-22, IL-16, and VEGF). Additionally, six factors were increased in plasma of 1-wk LVPO in the absence of increases in myocardial levels. In contrast, in mice with PF, no factors were found increased in plasma that were not elevated in lung tissue. Of those factors increased at 1 wk, only TIMP-1 remained elevated at 4 wk of LVPO. Immunohistochemistry of myocardial vasculature at 1 and 4 wk revealed similar amounts of total vasculature; however, evidence of activated endothelium was observed at 1 wk and, to a lesser extent, at 4 wk LVPO. In conclusion, PO myocardium generated a unique signature of cytokine expression versus that of fibrotic lung.NEW & NOTEWORTHY Myocardial fibrosis and the resultant increases in myocardial stiffness represent pivotal consequences of chronic pressure overload (PO). In this study, cytokine profiles produced in a murine model of cardiac fibrosis induced by PO were compared with those produced in response to silica-induced lung fibrosis. A unique profile of cardiac tissue-specific and plasma-derived factors generated in response to PO are reported.

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Selection of disease-specific biomarkers by integrating inflammatory mediators with clinical informatics in AECOPD patients: a preliminary study.

Publications

Selection of disease-specific biomarkers by integrating inflammatory mediators with clinical informatics in AECOPD patients: a preliminary study.

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