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ABSTRACT: Background and objective: Molecular biomarkers are needed to refine prognostication and phenotyping of pulmonary hypertension (PH) patients. S100A12 is an emerging biomarker of various inflammatory diseases. This study aims to determine the prognostic value of S100A12 in PH. Methods: Exploratory microarray analysis performed on peripheral blood mononuclear cells (PBMC) collected from idiopathic pulmonary fibrosis (IPF) patients suggested an association between S100A12 and both PH and mortality. So the current study was designed to evaluate for an association between S100A12 in peripheral blood collected from two well‐phenotyped PH cohorts in two other centres to derive and validate an association between S100A12 protein serum concentrations and mortality. Results: The majority of the patients in the discovery and validation cohorts were either World Health Organization (WHO) group 1 (pulmonary arterial hypertension (PAH)) or 3 (lung disease‐associated) PH. In the discovery PH cohort, S100A12 was significantly increased in patients with PH (n = 51) compared to controls (n = 22) (29.8 vs 15.7 ng/mL, P < 0.001) and negatively correlated with cardiac output (r = −0.58, P < 0.001) in PH patients. When S100A12 data were pooled from both cohorts, PAH and non‐PAH PH patients had higher S100A12 compared to healthy external controls (32.6, 30.9, 15.7 ng/mL; P < 0.001). S100A12 was associated with an increased risk in overall mortality in PH patients in both the discovery (n = 51; P = 0.008) and validation (n = 40; P < 0.001) cohorts. Conclusion: S100A12 levels are increased in PH patients and are associated with increased mortality. [ABSTRACT FROM AUTHOR]

Subgroup identification (clustering) is an important problem in biomedical research. Gene expression profiles are commonly utilized to define subgroups. Longitudinal gene expression profiles might provide additional information on disease progression than what is captured by baseline profiles alone. Therefore, subgroup identification could be more accurate and effective with the aid of longitudinal gene expression data. However, existing statistical methods are unable to fully utilize these data for patient clustering. In this article, we introduce a novel clustering method in the Bayesian setting based on longitudinal gene expression profiles. This method, called BClustLonG, adopts a linear mixed-effects framework to model the trajectory of genes over time, while clustering is jointly conducted based on the regression coefficients obtained from all genes. In order to account for the correlations among genes and alleviate the high dimensionality challenges, we adopt a factor analysis model for the regression coefficients. The Dirichlet process prior distribution is utilized for the means of the regression coefficients to induce clustering. Through extensive simulation studies, we show that BClustLonG has improved performance over other clustering methods. When applied to a dataset of severely injured (burn or trauma) patients, our model is able to identify interesting subgroups. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background and objective Idiopathic pulmonary fibrosis ( IPF) is a progressive disease with poor prognosis and variable clinical course. Although matrix metalloproteinase-7 ( MMP-7) is emerging as an important IPF biomarker, reproducibility across studies is unclear. We aimed to determine whether a previously reported prognostic threshold for MMP-7 was predictive of mortality in an independent cohort of IPF patients. Methods MMP-7 concentrations obtained from heparinized plasma samples were determined by ELISA in 97 patients with IPF and 41 healthy controls. The association of the previously published heparin plasma MMP-7 threshold of 12.1 ng/mL with all-cause mortality or transplant-free survival ( TFS) was determined, either as an independent biomarker or as part of the modified personal clinical and molecular mortality index (m- PCMI). Results MMP-7 plasma concentrations were significantly higher in IPF patients compared to healthy controls (14.40 ± 6.55 ng/mL vs 6.03 ± 2.51 ng/mL, P < 0.001). The plasma MMP-7 threshold of 12.1 ng/mL was significantly associated with both all-cause mortality and TFS (unadjusted Cox proportional hazard ratio ( HR) = 25.85 and 15.49, 95% CI: 10.91-61.23 and 5.41-44.34, respectively, P < 0.001). MMP-7 concentrations, split by 12.1 ng/mL, were significantly ( P < 0.05) predictive of mortality and TFS after adjusting for age, gender, smoking and baseline pulmonary function parameters, in a multivariate Cox proportional hazards model. MMP-7 concentrations were negatively correlated with diffusing lung capacity of carbon monoxide (DLCO) (r = −0.21, P = 0.02), and positively with a mortality risk scoring system ( GAP) that combines age, gender, forced vital capacity ( FVC) and DLCO (r = 0.32, P = 0.001). Conclusion This study confirms that MMP-7 concentrations could be used to accurately predict outcomes across cohorts and centres, when similar collection protocols are applied. [ABSTRACT FROM AUTHOR]