Found in Translation: Multi-omics Assessment of the Chronic Obstructive Pulmonary Disease-Lung Cancer Interaction.
Ontology highlight
ABSTRACT:
SUBMITTER: Christenson S
PROVIDER: S-EPMC6680298 | biostudies-literature | 2019 Aug
REPOSITORIES: biostudies-literature
Publications
Found in Translation: Multi-omics Assessment of the Chronic Obstructive Pulmonary Disease-Lung Cancer Interaction.
Christenson Stephanie S Hersh Craig P CP
American journal of respiratory and critical care medicine 20190801 3
Similar Datasets
Project description:Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are molecularly defined clusters with distinct clinical phenotypes that may underlie the clinical heterogeneity. Subjects included 3,278 subjects from the COPDGene cohort with at least one of the following profiles: whole blood transcriptomes (2,650 subjects); plasma proteomes (1,013 subjects); and plasma metabolomes (1,136 subjects). 489 subjects had all three contemporaneous -omics profiles. Autoencoder embeddings were performed individually for each -omics dataset. Embeddings underwent subspace clustering using MineClus, either individually by -omics or combined, followed by recursive feature selection based on Support Vector Machines. Clusters were tested for associations with clinical variables. Optimal single -omics clustering typically resulted in two clusters. Although there was overlap for individual -omics cluster membership, each -omics cluster tended to be defined by unique molecular pathways. For example, prominent molecular features of the metabolome-based clustering included sphingomyelin, while key molecular features of the transcriptome-based clusters were related to immune and bacterial responses. We also found that when we integrated the -omics data at a later stage, we identified subtypes that varied based on age, severity of disease, in addition to diffusing capacity of the lungs for carbon monoxide, and precent on atrial fibrillation. In contrast, when we integrated the -omics data at an earlier stage by treating all data sets equally, there were no clinical differences between subtypes. Similar to clinical clustering, which has revealed multiple heterogenous clinical phenotypes, we show that transcriptomics, proteomics, and metabolomics tend to define clusters of COPD patients with different clinical characteristics. Thus, integrating these different -omics data sets affords additional insight into the molecular nature of COPD and its heterogeneity.
| S-EPMC8386883 | biostudies-literature
Project description:Despite recent advances in chronic obstructive pulmonary disease (COPD) research, few studies have identified the potential therapeutic targets systematically by integrating multiple-omics datasets. This project aimed to develop a systems biology pipeline to identify biologically relevant genes and potential therapeutic targets that could be exploited to discover novel COPD treatments via drug repurposing or de novo drug discovery. A computational method was implemented by integrating multi-omics COPD data from unpaired human samples of more than half a million subjects. The outcomes from genome, transcriptome, proteome, and metabolome COPD studies were included, followed by an in silico interactome and drug-target information analysis. The potential candidate genes were ranked by a distance-based network computational model. Ninety-two genes were identified as COPD signature genes based on their overall proximity to signature genes on all omics levels. They are genes encoding proteins involved in extracellular matrix structural constituent, collagen binding, protease binding, actin-binding proteins, and other functions. Among them, 70 signature genes were determined to be druggable targets. The in silico validation identified that the knockout or over-expression of SPP1, APOA1, CTSD, TIMP1, RXFP1, and SMAD3 genes may drive the cell transcriptomics to a status similar to or contrasting with COPD. While some genes identified in our pipeline have been previously associated with COPD pathology, others represent possible new targets for COPD therapy development. In conclusion, we have identified promising therapeutic targets for COPD. This hypothesis-generating pipeline was supported by unbiased information from available omics datasets and took into consideration disease relevance and development feasibility.
| S-EPMC11507528 | biostudies-literature
Project description:BackgroundChronic obstructive pulmonary disease (COPD) is a highly morbid and heterogenous disease. While COPD is defined by spirometry, many COPD characteristics are seen in cigarette smokers with normal spirometry. The extent to which COPD and COPD heterogeneity is captured in omics of lung tissue is not known.MethodsWe clustered gene expression and methylation data in 78 lung tissue samples from former smokers with normal lung function or severe COPD. We applied two integrative omics clustering methods: (1) Similarity Network Fusion (SNF) and (2) Entropy-Based Consensus Clustering (ECC).ResultsSNF clusters were not significantly different by the percentage of COPD cases (48.8% vs. 68.6%, p = 0.13), though were different according to median forced expiratory volume in one second (FEV1) % predicted (82 vs. 31, p = 0.017). In contrast, the ECC clusters showed stronger evidence of separation by COPD case status (48.2% vs. 81.8%, p = 0.013) and similar stratification by median FEV1% predicted (82 vs. 30.5, p = 0.0059). ECC clusters using both gene expression and methylation were identical to the ECC clustering solution generated using methylation data alone. Both methods selected clusters with differentially expressed transcripts enriched for interleukin signaling and immunoregulatory interactions between lymphoid and non-lymphoid cells.ConclusionsUnsupervised clustering analysis from integrated gene expression and methylation data in lung tissue resulted in clusters with modest concordance with COPD, though were enriched in pathways potentially contributing to COPD-related pathology and heterogeneity.
| S-EPMC10091624 | biostudies-literature
Project description:Chronic obstructive pulmonary disease (COPD) is a known risk factor for developing lung cancer suggesting that the COPD stroma contains factors supporting tumorigenesis. Since cancer initiation is complex we used a multi-omic approach to identify gene expression patterns that distinguish COPD stroma in patients with or without lung cancer. We obtained lung tissue from patients with COPD and lung cancer (tumor and adjacent non-malignant tissue) and those with COPD without lung cancer for proteomic and mRNA (cytoplasmic and polyribosomal) profiling. We used the joint and individual variation explained (JIVE) method to integrate and analysis across the three datasets. JIVE identified eight latent patterns that robustly distinguished and separated the three groups of tissue samples. Predictive variables that associated with the tumor, compared to adjacent stroma, were mainly represented in the transcriptomic data, whereas, predictive variables associated with adjacent tissue compared to controls was represented at the translatomic level. Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis revealed extracellular matrix (ECM) and PI3K-Akt signaling pathways as important signals in the pre-malignant stroma. COPD stroma adjacent to lung cancer is unique and differs from non-malignant COPD tissue and is distinguished by the extracellular matrix and PI3K-Akt signaling pathways.
2018-10-05 | GSE106899 | GEO
Project description:Identifying protein biomarkers for chronic obstructive pulmonary disease (COPD) has been challenging. Most previous studies have utilized individual proteins or pre-selected protein panels measured in blood samples. To identify COPD protein biomarkers by applying comprehensive mass spectrometry proteomics in lung tissue samples. We utilized mass spectrometry proteomic approaches to identify protein biomarkers from 152 lung tissue samples representing COPD cases and controls.
2022-01-07 | PXD024124 | Pride
Project description:Age-related chronic inflammatory lung diseases impose a threat on public health, including idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD). However, their etiology and potential targets have not been clarified. We performed genome-wide meta-analysis for IPF with the largest sample size (2883 cases and 741,929 controls) and leveraged the summary statistics of COPD (17,547 cases and 617,598 controls). Transcriptome-wide and proteome-wide Mendelian randomization (MR) designs, together with genetic colocalization, were implemented to find robust targets. The mediation effect was assessed using leukocyte telomere length (LTL). The single-cell transcriptome analysis was performed to link targets with cell types. Individual-level data from UK Biobank (UKB) were used to validate our findings. Sixteen genetically predicted plasma proteins were causally associated with the risk of IPF and 6 proteins were causally associated with COPD. Therein, genetically-elevated plasma level of SCARF2 protein should reduce the risk of both IPF (odds ratio, OR = 0.9974 [0.9970, 0.9978]) and COPD (OR = 0.7431 [0.6253, 0.8831]) and such effects were not mediated by LTL. Genetic colocalization further corroborated these MR results of SCARF2. The transcriptome-wide MR confirmed that higher expression level of SCARF2 was associated with a reduced risk of both. However, the single-cell RNA analysis indicated that SCARF2 expression level was only relatively lower in epithelial cells of COPD lung tissue compared to normal lung tissue. UKB data implicated an inverse association of serum SCARF2 protein with COPD (hazard ratio, HR = 1.215 [1.106, 1.335]). The SCARF2 gene should be a novel target for COP.
| S-EPMC11464143 | biostudies-literature
Project description:Objective Osteoporosis, which is now recognized as a major comorbidity of chronic obstructive pulmonary disease (COPD), must be diagnosed by appropriate methods. The aims of this study were to clarify the relationships between bone mineral density (BMD) and COPD-related clinical variables and to explore the association of BMD with the updated Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification in men. Methods We enrolled 50 Japanese men with clinically stable COPD who underwent dual-energy X-ray absorptiometry (DEXA), pulmonary function testing, and computerized tomography (CT) and who had completed a questionnaire (COPD assessment test [CAT]). We determined the association between the T-score and other tested parameters and compared the BMD of patients in each GOLD category. Results Twenty-three of the 50 patients (46.0%) were diagnosed with osteopenia, and 7 (14.0%) were diagnosed with osteoporosis. The BMD findings were significantly correlated with the CAT score, forced expiratory volume in 1 second percentage predicted (FEV1% predicted), low attenuation volume percentage (LAV%), and percentage of cross-sectional area of small pulmonary vessels (%CSA) on CT images. Notably, the median T-score of the GOLD category D participants was significantly lower than that of the participants in each of the other categories (A [-0.98], B [-1.06], C [-1.05], and D [-2.19], p<0.05). Conclusion Reduced BMD was associated with airflow limitation, extent of radiographic findings, and a poor quality of life (QOL) in patients with COPD. The BMD of GOLD category D patients was the lowest of all of the patients evaluated, and category D patients may benefit from active intervention for osteoporosis.
| S-EPMC5548669 | biostudies-other
Project description:Diaphragm muscles in Chronic Obstructive Pulmonary Disease (COPD) patients undergo an adaptive fast to slow transformation that includes cellular adaptations. This project studies the signaling mechanisms responsible for this transformation. Keywords: other
2003-07-16 | GSE475 | GEO
Project description:BackgroundChronic obstructive pulmonary disease (COPD) is a well-known risk factor for postoperative pulmonary complications (PPCs), necessitating careful preoperative evaluation in patients undergoing lung resection surgery. The risk of PPCs in patients with COPD may vary with severity of symptoms, and the COPD Assessment Test (CAT) score is commonly used to assess patient quality of life and predict acute exacerbations. However, few studies have explored the correlation between CAT score and PPC incidence in COPD. This study aimed to assess the predictive value of CAT scores for PPCs and compare them with other established PPC predictors in mild to moderate COPD.MethodsWe retrospectively reviewed 83 patients with COPD who underwent preoperative evaluation before lung cancer surgery, including cardiopulmonary exercise tests (CPETs), between January 2020 and June 2022. We compared the predictive value of the following factors for the incidence of PPCs: spirometry, CPETs, 6-min walk tests, symptom-based scores (including CAT scores), the COPD composite severity index, surgery type, comorbidity index, and PPC prediction models.ResultsAmong the 83 patients, 16 (19.2%) developed PPCs, with persistent air leakage being the most common complication. CAT scores significantly differed between PPC and non-PPC groups (mean value 9.4 vs. 6.7, P=0.002). In multivariable logistic regression analysis, a CAT score of ≥7 was an independent risk factor for the incidence of PPCs (odds ratio =9.88; 95% confidence interval: 1.95-50.04; P=0.005), whereas other factors demonstrated no significant predictive value.ConclusionsCAT scores are valuable for evaluating patients with mild to moderate COPD before lung resection surgery, reliably predicting PPCs.
| S-EPMC11565318 | biostudies-literature
Project description:This experiment was carried out to see if there were any miRNA expression differences in pulmonary endothelial cells between patients with and without COPD. COPD is an inflammatory condition and although much work has previously been performed to investigate the inflammatory cells in COPD there has not been as much research looking at the endothelium through which inflammatory cells must pass through to reach the lung tissue. In this experiment pulmonary endothelial cells were extracted from whole lung tissue removed at the time of cardiothoracic surgery. This was performed for patients with and without COPD. RNA was extracted using the Qiagen microRNeasy kits prior to transferring to the University of Birmingham Biosciences department who performed RNA labelling and ran the microarrays. Once the microarrays were performed quality was checked using ArrayQualityMetrics and the COPD group was compared to the non-COPD group using SAM. The experiment was then repeated using another patient group. MiRNAs of interest were validated with qPCR initially before moving on to functional work.
2021-08-01 | E-MTAB-10311 | biostudies-arrayexpress