Distinguishing smoking related lung disease phenotypes via imaging and molecular features
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE132829 | GEO | 2020/12/07
REPOSITORIES: GEO
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Project description:BackgroundChronic tobacco smoke exposure results in a broad range of lung pathologies including emphysema, airway disease and parenchymal fibrosis as well as a multitude of extra-pulmonary comorbidities. Prior work using CT imaging has identified several clinically relevant subgroups of smoking related lung disease, but these investigations have generally lacked organ specific molecular correlates.Research questionCan CT imaging be used to identify clinical phenotypes of smoking related lung disease that have specific bronchial epithelial gene expression patterns to better understand disease pathogenesis?Study design and methodsUsing K-means clustering, we clustered participants from the COPDGene study (n = 5,273) based on CT imaging characteristics and then evaluated their clinical phenotypes. These clusters were replicated in the Detection of Early Lung Cancer Among Military Personnel (DECAMP) cohort (n = 360), and were further characterized using bronchial epithelial gene expression.ResultsThree clusters (preserved, interstitial predominant and emphysema predominant) were identified. Compared to the preserved cluster, the interstitial and emphysema clusters had worse lung function, exercise capacity and quality of life. In longitudinal follow-up, individuals from the emphysema group had greater declines in exercise capacity and lung function, more emphysema, more exacerbations, and higher mortality. Similarly, genes involved in inflammatory pathways (tumor necrosis factor-α, interferon-β) are more highly expressed in bronchial epithelial cells from individuals in the emphysema cluster, while genes associated with T-cell related biology are decreased in these samples. Samples from individuals in the interstitial cluster generally had intermediate levels of expression of these genes.InterpretationUsing quantitative CT imaging, we identified three groups of individuals in older ever-smokers that replicate in two cohorts. Airway gene expression differences between the three groups suggests increased levels of inflammation in the most severe clinical phenotype, possibly mediated by the tumor necrosis factor-α and interferon-β pathways.Clinical trial registrationCOPDGene (NCT00608764), DECAMP-1 (NCT01785342), DECAMP-2 (NCT02504697).
| S-EPMC8039011 | biostudies-literature
Project description:Distinguishing smoking related lung disease phenotypes via imaging and molecular features
| PRJNA549241 | ENA
Project description:Introduction: Lung cancer screening by computed tomography (CT) reduces mortality but exhibited high false-positive rates. We established a diagnostic classifier combining chest CT features with bronchial genomics. Materials and Methods: Patients with CT-detected suspected lung cancer were enrolled. The sample collected by bronchial brushing was used for RNA sequencing. R software was applied to build the model. Results: A total of 283 patients, including 183 with lung cancer and 100 with benign lesions, were included. When incorporating genomic data with radiological characteristics, the advanced model yielded 0.903 AUC with 81.1% NPV. Moreover, the classifier performed well regardless of lesion size, location, stage, histologic type, or smoking status. Pathway analysis showed enhanced epithelial differentiation, tumor metastasis, and impaired immunity were predominant in smokers with cancer, whereas tumorigenesis played a central role in non-smokers with cancer. Apoptosis and oxidative stress contributed critically in metastatic lung cancer; by contrast, immune dysfunction was pivotal in locally advanced lung cancer. Conclusions: We devised a minimal-to-noninvasive, efficient diagnostic classifier for smokers and non-smokers with lung cancer, which provides evidence for different mechanisms of cancer development and metastasis associated with smoking. A negative classifier result will help the physician make conservative diagnostic decisions.
2021-02-18 | GSE166926 | GEO
Project description:ABSTRACT: Paralleling the diversity of genetic and protein activities, pathologic human tissues also exhibit diverse radiographic features. Here we show that dynamic imaging traits in non-invasive computed tomography (CT) systematically correlate with the global gene expression programs of primary human liver cancer. Combinations of twenty-eight imaging traits can reconstruct 78% of the global gene expression profiles, revealing cell proliferation, liver synthetic function, and patient prognosis. Thus, genomic activity of human liver cancers can be decoded by noninvasive imaging, thereby enabling noninvasive, serial and frequent molecular profiling for personalized medicine. Keywords: disease_state_design
2007-04-07 | GSE7474 | GEO
Project description:The human bronchial epithelium is composed of multiple, distinct cell types that cooperate to defend against environmental insults. While studies have shown that smoking alters bronchial epithelial function and morphology, its precise effects on specific cell types and overall tissue composition are unclear. We used single-cell RNA sequencing to profile bronchial epithelial cells from six never- and six current smokers. Unsupervised analyses led to the characterization of a set of toxin metabolism genes that localized to smoker ciliated cells, tissue remodeling associated with a loss of club cells and extensive goblet cell hyperplasia, and a novel peri-goblet epithelial subpopulation in smokers that expressed a marker of bronchial premalignant lesions. Our data demonstrates that smoke exposure drives a complex landscape of cellular alterations that may prime the human bronchial epithelium for disease.
2019-09-27 | GSE131391 | GEO
Project description:The club cell, a small airway epithelial (SAE) secretory cell that uniquely expresses SCGB1A1, plays a central role in host defense in the human lung. Based on data demonstrating that ~50% of club cells express MUC5B, a secretory mucin critical for mucociliary clearance, we hypothesized that subpopulations of club cells with distinct functions may exist. To evaluate this, the SAE of normal nonsmokers and healthy cigarette smokers was sampled by bronchoscopy and brushing followed by single cell sequencing using Drop-seq technology. Subpopulations of SCGCB1A1+KRT5loMUC5AC- club cells were assessed by unsupervised clustering to evaluate club cell subpopulations. Immunostaining of SAE in lung sections, brushed SAE cells, and in vitro air-liquid interface culture was utilized to confirm the transcriptomic-based observations. Unsupervised clustering of SCGCB1A1+KRT5loMUC5AC‾ club cells in the SAE identified 3 unique club cell populations that differed by differentiation state and function, including: (1) progenitor; (2) proliferating; and (3) effector subpopulations. The progenitor club cell population was energetically active with high expression of mitochondrial and ribosomal proteins and the highest KRT5 levels vs other club cell populations. The proliferating population, defined by high expression of cyclins and proliferation markers, was the smallest, representing 2% of club cells. The effector club cell cluster expressed transcripts for host defense genes, xenobiotic metabo-lism, and barrier functions commonly associated with club cell function. Comparison of the club cell subpopulations in smokers vs nonsmokers demonstrated that the proportion of the club cell effector population was significantly decreased in smokers with a concomitant significant in-crease in the proliferating cell population. These observations provide novel insights into both the makeup of human SAE club cell subpopulations and smoking-induced changes in club cell biology.
2021-09-22 | GSE155515 | GEO
Project description:24 SAGE libraries comprising of 8 current smokers, 12 former smokers and 4 never smokers. Keywords: SAGE, gene expression, current, former, never, smokers
2007-08-08 | GSE5473 | GEO
Project description:Genome wide DNA methylation profiling of smokers and non-smokers in PBMC samples. The Illumina Infinium 450k Human DNA methylation Beadchip v1.1 was used to obtain DNA methylation profiles across 485,577 CpGs in PBMC samples. Samples included 50 smokers and 61 non-smokers.
2014-02-27 | GSE53045 | GEO
Project description:Optimized DNA sampling of ancient bones using Computed Tomography (CT) scans
| PRJEB25493 | ENA
Project description:Effect of X-ray computed tomography on maize root gene expression
| PRJNA605311 | ENA