Similar Datasets
Project description:Idiopathic Pulmonary Fibrosis (IPF) is a chronic progressive lung disease that affects more than 5 million people worldwide with a steady increase in both incidence and mortality. There is currently no effective therapy and the median survival without transplant is 2-5 years. The etiological factor is unknown, but several observational and pathogenesis studies suggest that environmental agents may cause IPF. DNA methylation is a type of chemical modification of DNA such environmental and occupational factors, that can induced a changes in the regulation of biological processes and link to diseases such as a cancer. We hypothesize that the global changes in methylation patterns of IPF lungs caused by environmental factors. In this study we will identify the global methylation signatures of the IPF lung and to compare to methylation signature of lung cancer. The DNA methylation profiles of IPF lung tissue differs from control lung but it shares great similarity with that of lung cancer.
2012-04-19 | GSE29895 | GEO
Project description:Genome-wide DNA methylation of IPF and normal lung tissue samples
2021-06-09 | GSE173355 | GEO
Project description:Idiopathic pulmonary fibrosis (IPF) is a chronic fibrosing lung disease that is difficult to diagnose and follows an unpredictable clinical course. The object of this study was to develop a predictive gene signature model of IPF from whole lung tissue. We collected whole lung samples from 11 IPF patients undergoing diagnostic surgical biopsy or transplantation. Whenever possible, samples were obtained from different lobes. Normals consisted of healthy organs donated for transplantation. We measured gene expression on microarrays. Data were analyzed by hierarchical clustering and Principal Component Analysis. By this approach, we found that gene expression was similar in the upper and lower lobes of individuals with IPF. We also found that biopsied and explanted specimens contained different patterns of gene expression; therefore, we analyzed biopsies and explants separately. Signatures were derived by fitting top genes to a Bayesian probit regression model. We developed a 153-gene signature that discriminates IPF biopsies from normal. We also developed a 70-gene signature that discriminates IPF explants from normal. Both signatures were validated on an independent cohort. The IPF Biopsy signature correctly diagnosed 76% of the validation cases (p < 0.01), while IPF Explant correctly diagnosed 78% (p < 0.001). Examination of differentially expressed genes revealed partial overlap between IPF Biopsy and IPF Explant and almost no overlap with previously reported IPF gene lists. However, several overlapping genes may provide a basis for developing therapeutic targets. 17 samples from 11 patients with IPF (6 patients provided a pair of samples from upper and lower lobes; 5 patients contributed singleton samples); 6 control specimens were obtained from routine lung volume reduction of healthy donor lungs at the time of lung transplantation.
2011-11-01 | E-GEOD-24206 | biostudies-arrayexpress
Project description:Idiopathic Pulmonary Fibrosis (IPF) is a chronic progressive lung disease that affects more than 5 million people worldwide with a steady increase in both incidence and mortality. There is currently no effective therapy and the median survival without transplant is 2-5 years. The etiological factor is unknown, but several observational and pathogenesis studies suggest that environmental agents may cause IPF. DNA methylation is a type of chemical modification of DNA such environmental and occupational factors, that can induced a changes in the regulation of biological processes and link to diseases such as a cancer. We hypothesize that the global changes in methylation patterns of IPF lungs caused by environmental factors. In this study we will identify the global methylation signatures of the IPF lung and to compare to methylation signature of lung cancer. The DNA methylation profiles of IPF lung tissue differs from control lung but it shares great similarity with that of lung cancer. Immunoprecipitated methylated DNA from 12 IPF lungs, 10 lung adenocarcinomas and 10 normal histology lungs obtained from the same group of adenocarcinoma patients was hybridized to Agilent human CpG Islands Microarrays. Only probes with a hybridization Tm value between 79 C and 93C were included in the analysis because these show higher quality signal. All probes were divided according to their Tm into 14 groups/bins differing by 1C. Probe signals in each bin were standardized to have an average of 0 and a standard deviation of 1. To work in a CpG island oriented manner, we scored each island for its likelihood to be methylated. For that purpose, each probe was mapped to the genome and the signals of the probes that were mapped to a single CpG island were averaged to obtain the islandM-bM-^@M-^Ys methylation score. Data analysis was performed using BRB-Array Tools and DAVID Bioinformatics Resources software packages.
2012-04-18 | E-GEOD-29895 | biostudies-arrayexpress
Project description:Molecular Signatures of Idiopathic Pulmonary Fibrosis [methylation array]
| PRJNA725236 | ENA
Project description:Idiopathic pulmonary fibrosis (IPF) is a chronic fibrosing lung disease that is difficult to diagnose and follows an unpredictable clinical course. The object of this study was to develop a predictive gene signature model of IPF from whole lung tissue. We collected whole lung samples from 11 IPF patients undergoing diagnostic surgical biopsy or transplantation. Whenever possible, samples were obtained from different lobes. Normals consisted of healthy organs donated for transplantation. We measured gene expression on microarrays. Data were analyzed by hierarchical clustering and Principal Component Analysis. By this approach, we found that gene expression was similar in the upper and lower lobes of individuals with IPF. We also found that biopsied and explanted specimens contained different patterns of gene expression; therefore, we analyzed biopsies and explants separately. Signatures were derived by fitting top genes to a Bayesian probit regression model. We developed a 153-gene signature that discriminates IPF biopsies from normal. We also developed a 70-gene signature that discriminates IPF explants from normal. Both signatures were validated on an independent cohort. The IPF Biopsy signature correctly diagnosed 76% of the validation cases (p < 0.01), while IPF Explant correctly diagnosed 78% (p < 0.001). Examination of differentially expressed genes revealed partial overlap between IPF Biopsy and IPF Explant and almost no overlap with previously reported IPF gene lists. However, several overlapping genes may provide a basis for developing therapeutic targets.
2011-11-01 | GSE24206 | GEO
Project description:The aim of the current study is to find plasma-based biomarker candidates for Idiopathic Pulmonary Fibrosis (IPF). Incidence of IPF seems to be increasing in Europe and there is significant mortality associated with IPF. There are no sensistive biomarkers for IPF and diagnosis is entirely clinical and/or histopathological which is often delayed. Minimally invasive biomarkers of IPF would be expected to aid clinicians perfrom early diagnosis of IPF enabling better management of the disease.
2020-05-13 | PXD010965 | Pride
Project description:Analysis of Idiopathic pulmonary fibrosis (IPF) at gene expression level. The hypothesis tested in the present study was that Epigenetic mechanisms are likely to be associated with pathogenesis in IPF. To determine the DNA methylation change, and their effects on gene expression, we compared microarray data of DNA methylation and RNA expression. Results provide that among the genes whose DNA methylation status and RNA expression were both significantly altered between IPF-rapid and normal controls.
2017-11-22 | GSE107226 | GEO
Project description:Idiopathic pulmonary fibrosis (IPF)
| PRJNA802122 | ENA
Project description:Idiopathic pulmonary fibrosis (IPF) is a specific form of chronic, progressive fibrosing interstitial disease of unknown cause. It remains impractical to conduct early diagnosis and predict IPF progression just based on gene expression information. Moreover, the relationship between gene expression and quantitative phenotypic value in IPF keeps controversial. To identify biomarkers to predict survival in IPF, we profiled protein-coding gene expression in peripheral blood mononuclear cells (PBMCs). We linked the gene expression level with the quantitative phenotypic variation in IPF, including diffusing capacity of the lung for carbon monoxide (DLCO) and forced vital capacity (FVC) percent predicted. In silico analyses on the expression profiles and quantitative phenotypic data allowed for the generation of a set of IPF molecular signature that predicted survival of IPF effectively. Total RNA was isolated from PBMCs using standard molecular biology protocols without DNA contamination or RNA degradation. Sample processing (e.g., cDNA generation, fragmentation, end labeling, hybridization to Affymetrix GeneChip Human Exon 1.0 ST arrays) was performed per manufacturer’s instructions. A total of 45 healthy controls and 70 IPF patients were included in the microarray analysis.
2014-06-30 | E-GEOD-38958 | biostudies-arrayexpress