miR-155 in the progression of lung fibrosis in systemic sclerosis

Background: MicroRNA (miRNA) control key elements of mRNA stability and likely contribute to the dysregulated lung gene expression observed in systemic sclerosis associated interstitial lung disease (SSc-ILD). We analyzed the miRNA gene expression of tissue and cells from patients with SSc-ILD. A chronic lung fibrotic murine model was used. Methods: RNA was isolated from lung tissue of 12 patients with SSc-ILD and 5 controls. High-resolution computed tomography (HRCT) was performed at baseline and 2-3 years after treatment. Lung fibroblasts and peripheral blood mononuclear cells (PBMC) were isolated from healthy controls and patients with SSc-ILD. miRNA and mRNA were analyzed by microarray, quantitative polymerase chain reaction, and/or Nanostring; pathway analysis was performed by DNA Intelligent Analysis (DIANA)-miRPath v2.0 software. Wild-type and miR-155 deficient (miR-155ko) mice were exposed to bleomycin. Results: Lung miRNA microarray data distinguished patients with SSc-ILD from healthy controls with 185 miRNA differentially expressed (q < 0.25). DIANA-miRPath revealed 57 Kyoto Encyclopedia of Genes and Genomes pathways related to the most dysregulated miRNA. miR-155 and miR-143 were strongly correlated with progression of the HRCT score. Lung fibroblasts only mildly expressed miR-155/miR-21 after several stimuli. miR-155 PBMC expression strongly correlated with lung function tests in SSc-ILD. miR-155ko mice developed milder lung fibrosis, survived longer, and weaker lung induction of several genes after bleomycin exposure compared to wild-type mice. Conclusions: miRNA are dysregulated in the lungs and PBMC of patients with SSc-ILD. Based on mRNA-miRNA interaction analysis and pathway tools, miRNA may play a role in the progression of the disease. Our findings suggest that targeting miR-155 might provide a novel therapeutic strategy for SSc-ILD

Identification of lung cancer gene markers through kernel maximum mean discrepancy and information entropy

© 2019 The Author(s). Background: The early diagnosis of lung cancer has been a critical problem in clinical practice for a long time and identifying differentially expressed gene as disease marker is a promising solution. However, the most existing gene differential expression analysis (DEA) methods have two main drawbacks: First, these methods are based on fixed statistical hypotheses and not always effective; Second, these methods can not identify a certain expression level boundary when there is no obvious expression level gap between control and experiment groups. Methods: This paper proposed a novel approach to identify marker genes and gene expression level boundary for lung cancer. By calculating a kernel maximum mean discrepancy, our method can evaluate the expression differences between normal, normal adjacent to tumor (NAT) and tumor samples. For the potential marker genes, the expression level boundaries among different groups are defined with the information entropy method. Results: Compared with two conventional methods t-test and fold change, the top average ranked genes selected by our method can achieve better performance under all metrics in the 10-fold cross-validation. Then GO and KEGG enrichment analysis are conducted to explore the biological function of the top 100 ranked genes. At last, we choose the top 10 average ranked genes as lung cancer markers and their expression boundaries are calculated and reported. Conclusion: The proposed approach is effective to identify gene markers for lung cancer diagnosis. It is not only more accurate than conventional DEA methods but also provides a reliable method to identify the gene expression level boundaries

Immune Response to Mycobacterium tuberculosis Infection in the Parietal Pleura of Patients with Tuberculous Pleurisy

The T lymphocyte-mediated immune response to Mycobacterium tuberculosis infection in the parietal pleura of patients with tuberculous pleurisy is unknown. The aim of this study was to investigate the immune response in the parietal pleura of tuberculous pleurisy compared with nonspecific pleuritis. We have measured the numbers of inflammatory cells particularly T-cell subsets (Th1/Th2/Th17/Treg cells) in biopsies of parietal pleura obtained from 14 subjects with proven tuberculous pleurisy compared with a control group of 12 subjects with nonspecific pleuritis. The number of CD3+, CD4+ and CCR4+ cells and the expression of RORC2 mRNA were significantly increased in the tuberculous pleurisy patients compared with the nonspecific pleuritis subjects. The number of toluidine blue+ cells, tryptase+ cells and GATA-3+ cells was significantly decreased in the parietal pleura of patients with tuberculous pleurisy compared with the control group of nonspecific pleuritis subjects. Logistic regression with receiver operator characteristic (ROC) analysis for the three single markers was performed and showed a better performance for GATA-3 with a sensitivity of 75%, a specificity of 100% and an AUC of 0.88. There was no significant difference between the two groups of subjects in the number of CD8, CD68, neutrophil elastase, interferon (IFN)-γ, STAT4, T-bet, CCR5, CXCR3, CRTH2, STAT6 and FOXP3 positive cells. Elevated CD3, CD4, CCR4 and Th17 cells and decreased mast cells and GATA-3+ cells in the parietal pleura distinguish patients with untreated tuberculous pleurisy from those with nonspecific pleuritis