Project description:Chronic obstructive pulmonary disease (COPD) is combination of progressive lung diseases. The diagnosis of COPD is generally based on the pulmonary function testing, however, difficulties underlie in prognosis of potential or early stage of COPD patients due to the complexity and heterogeneity of the pathogenesis. Transcriptomic technology is expected as one of the solution to resolve such complexities; therefore, we obtained transcriptomic data by in vitro testing with exposures of human 3D cultured bronchial epithelial tissues (MucilAir) to known inducible factors for early events of COPD to identify the potential descriptive marker genes. Fifteen potential biomarker genes were identified by transcriptomic analysis, and 10 out of 15 genes, as well as their coding proteins, have not been previously reported as biomarkers for chronic inflammatory lung diseases. The expression levels of these 15 genes with machine learning classification well distinguished between COPD and non-COPD patients with remarkable accuracy, suggesting these identified genes are potential descriptive marker genes for COPD.
Project description:Chronic obstructive pulmonary disease (COPD) is a highly prevalent disease leading to irreversible airflow limitation and is characterized by chronic pulmonary inflammation，obstructive bronchiolitis and emphysema. Etiologically, COPD is mediated by toxic gases and particles, e.g. cigarette smoke, while the pathogenesis of the disease is largely unknown. Several lines of evidence indicate a link between COPD and autoimmunity but comprehensive studies are lacking. By using a protein microarray assaying more than 19,000 human proteins we determined in this study the autoantibody profiles of COPD and non-COPD smokers.
Project description:Background: CD8 cells seem to play an important role in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, relatively little is known about their phenotype and function. Aims: To define the transcriptome of pulmonary CD8 cells in COPD and compare to paired circulating CD8 cells and smoker control pulmonary CD8 cells. COPD was defined according to the Global initiative for chronic Obstructive Lung Disease guidelines. Severity of disease was defined according to the patients lung function. In particular the forced evpiratroy volume in 1 second (FEV1).
Project description:Acquisition of a new strain of non-typeable Haemophilus influenzae (NTHi) is often associated with exacerbation of chronic obstructive pulmonary disease (COPD). We have previously reported that COPD patients who are homozygous null for SIGLEC14 gene is less susceptible to COPD exacerbation than those who have wild-type allele with functional SIGLEC14 gene. In order to gain insight into the mechanism behind the COPD exacerbation, and to find new clues that may lead to the discovery of objective biomarker of COPD exacerbation, Siglec-14/THP-1 and Siglec-5/THP-1 cell lines, which mimic monocytes from homozygous wild-type and homozygous SIGLEC14-null person, respectively, were incubated with or without NTHi, and their gene expression profiles were compared by using Affymetrix Human Genome U133 Plus 2.0 Array. Four samples (2 cell lines x 2 conditions) were analyzed. No replicates were made.
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.
Project description:We profiled genome-wide gene expression in non-tumorous human lung tissues. The overall goal of this project is to improve our molecular understanding of various lung diseases including lung cancer and chronic obstructive pulmonary disease (COPD).
