Project description:Lung tissue of COPD patients and tissue of non-smokers was investigated in transcriptome analysis with regard to differences in RNA expression levels to identify target genes for COPD treatment.
Project description:Chronic obstructive pulmonary disease (COPD) is a progressive and irreversible chronic inflammatory lung disease. The abnormal inflammatory response of the lung, mainly to cigarette smoke, causes multiple cellular and structural changes affecting all of its compartments, which leads to disease progression. The molecular mechanisms underlying these pathological changes are still not fully understood The aim of this study was to identify genes and molecular pathways potentially involved in the pathogenesis of COPD Peripheral lung tissue samples from moderate COPD patients, smokers and nonsmokers were obtained. All patients were undergoing lung resection for localized carcinomas. RNA was extracted and processed for further hybridization on Affymetrix microarrays
Project description:Exosomal miRNAs have been studied in relation to many diseases. However, there is little to no knowledge regarding the miRNA population of BALF or the lung tissue derived exosomes in COPD and IPF. Considering this, we determined and compared the miRNA profiles of BALF and lung tissue-derived exosomes from healthy non-smokers, healthy smokers, and patients with COPD and IPF. NGS results identified three differentially expressed miRNAs in the BALF, while one in the lung-derived exosomes from COPD patients as compared to healthy non-smokers. Of these, we found three- and five-fold downregulation of miR-122-5p amongst the lung tissue-derived exosomes from COPD patients as compared to healthy non-smokers and smokers, respectively. Interestingly, there were key 55 differentially expressed miRNAs in the lung tissue-derived exosomes of IPF patients compared to non-smoking controls.
Project description:Chronic obstructive pulmonary disease (COPD) is a progressive and irreversible chronic inflammatory lung disease. The abnormal inflammatory response of the lung, mainly to cigarette smoke, causes multiple cellular and structural changes affecting all of its compartments, which leads to disease progression. The molecular mechanisms underlying these pathological changes are still not fully understood The aim of this study was to identify genes and molecular pathways potentially involved in the pathogenesis of COPD
Project description:Rationale: Chronic Obstructive Pulmonary Disease (COPD) is associated with a complex pulmonary and systemic immune response. Objective: To characterize and relate the lung tissue and circulating blood network immune response in COPD. Methods: Lung tissue and circulating blood samples were simultaneously obtained from COPD patients (current smokers n=28 and former smokers n=16) and controls (current smokers n=9 and non-smokers n=12) undergoing thoracic surgery. We used flow cytometry to assess the immune cell composition, Affymetrix arrays to determine whole lung mRNA expression, and Weighted Gene Co-expression Network Analysis (WGCNA) to characterize and compare the pulmonary and systemic immune responses in patients and controls. Results: In lung tissue of current smokers with COPD (vs. non-smokers and former smokers with COPD) we observed a significant increase in the proportion of intermediated phenotype macrophages (Mphage) expressing both M1 and M2 markers, whereas that of M1 Mphage (pro-inflammatory) and CD4+ and CD8+ T-lymphocytes were decreased. These changes were not mirrored in circulating blood but WGCNA identified three modules of co-expressed genes that related, respectively to: (1) the total proportion of lung Mphage (extracellular matrix and angiogenesis genes) ; (2) active smoking (T cell and apoptosis related genes); and, (3) severity of airflow limitation (cilium organization genes). Conclusions: In mild/moderate COPD, the main pulmonary immune cell alterations relate to current smoking, involve changes in the proportion of Mphage and T cells and are associated with changes in whole lung tissue transcriptome. These cellular pulmonary changes are not mirrored in the systemic circulation.
Project description:We analyzed gene expression profiling of lung tissue to define molecular pathway of COPD using recent RNA sequencing technology.Lung tissue was obtained from 98 COPD subjects and 91 subjects with normal spirometry. RNA isolated from these samples was processed with RNA-seq using HiSeq 2000. Gene expression measurements were calculated using Cufflinks software. Differentially expressed genes and isoforms were chosen using t-test. Some of differentially expressed genes were validated by quantitative real-time PCR. Examination of lung tissue in COPD patients versus normal control
Project description:Using integrated proteomic and RNA sequencing analysis of COPD and control lung tissues, we identified molecular signatures in COPD.
Project description:We analyzed gene expression profiling of lung tissue to define molecular pathway of COPD using recent RNA sequencing technology.Lung tissue was obtained from 98 COPD subjects and 91 subjects with normal spirometry. RNA isolated from these samples was processed with RNA-seq using HiSeq 2000. Gene expression measurements were calculated using Cufflinks software. Differentially expressed genes and isoforms were chosen using t-test. Some of differentially expressed genes were validated by quantitative real-time PCR.
Project description:Background: Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD, thus the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Methods: Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Results: Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, Random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. Conclusion: We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel.