Longitudinal Blood Transcriptomic Changes Predict Lung Function Decline in patients with Idiopathic Pulmonary Fibrosis
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ABSTRACT:
PROVIDER: PRJNA551014 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR9601180_1.fastq.gz | Fastqsanger.gz | |||
| SRR9601180_2.fastq.gz | Fastqsanger.gz | |||
| SRR9601190_1.fastq.gz | Fastqsanger.gz | |||
| SRR9601190_2.fastq.gz | Fastqsanger.gz | |||
| SRR9601200_1.fastq.gz | Fastqsanger.gz |
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Project description:Longitudinal Blood Transcriptomic Changes Predict Lung Function Decline in Idiopathic Pulmonary Fibrosis
| PRJNA548528 | ENA
Project description:The Rationale: Molecular markers of disease activity that are predictive of forced vital capacity (FVC) progression in idiopathic pulmonary fibrosis are needed. Objectives: Develop a predictor using longitudinal within-patient gene expression differences (ΔGE) in peripheral blood mononuclear cells (PBMC) to predict of FVC progression. Methods: Patients in the training cohort (n=74) experiencing ≥10% relative reduction in FVC% of predicted over 12 months were categorized as progressors in contrast to the remaining stable patients. Baseline to 4-month within-patient ΔGE were correlated with FVC status. FVC-predictor genes were prioritized by two-group comparison with FDR<5%, logistic LASSO regression with p<0.05, and 10-Fold Cross-Validation with ≥50% support. Receiver operating characteristic with area under the curve (AUC) analyses were conducted in training subsets and independent validation cohorts from UChicago (n=27), UPMC (n=35), and Imperial (n=24) where different transcriptome assay platforms and varying transcriptome sampling times were used to derive ΔGE. Results: Intra-subject Compared to cross-sectional analysis of baseline GE, our longitudinal ΔGE variation approach demonstratedlargely reduced within-group sample variation and increased statistic power fin progressor and stable groups for prediction model development. A 25-gene FVC-predictor separated “progressors” from “stable” by Principal Component Analysis in the training and subsets of the training cohort. The resulting FVC-predictor consistently demonstrated high discriminatory performance independent of transcriptome assay platforms and varying sampling times in validation cohorts (AUC= 0.77-0.80). TGF beta was the highest-ranking canonical pathway by Gene Set Enrichment Analysis. Conclusions: Our novel short-term longitudinal within-patient ΔGE approach identified a FVC-predictor which may reflect disease activity and prove to be a reliable biomarker predictive of future FVC decline.
2019-09-30 | GSE132607 | GEO
Project description:Rationale: Molecular markers of disease progression in idiopathic pulmonary fibrosis are needed. Objective: Derive and validate a blood transcriptomic predictor of forced vital capacity (FVC) decline. Methods: A training cohort (n=74) of IPF patients was stratified according to the presence of progressive disease, defined as ≥10% relative decline in FVC over 12 months. Baseline to 4-month within-patient changes in gene expression were correlated with categorical FVC decline. Genes predictive of FVC decline were identified by two-group comparison with false discovery rate <5% followed by logistic LASSO regression and 10-Fold Cross-Validation for gene list prioritization. Independent validation cohorts with differing transcriptome assay platforms and blood transcriptome sampling times from UChicago (n=27), UPMC (n=35), and Imperial (n=24) underwent receiver operating characteristic with area under the curve (AUC) analyses for validation. Results: A longitudinally-derived FVC-gene predictor accurately discriminated most patients with stable and progressive IPF across four independent IPF cohorts with variable transcriptomic assay platforms and sampling times. The FVC-gene predictorand demonstrated sensitivity and specificity of 74.3% and 82.4% in the combined replication cohort. The likelihood ratio, LR+ and LR- were 4.11 and 0.32, respectively. TGF-beta was the highest-ranking canonical pathway by Gene Set Enrichment Analysis. An approach using longitudinal gene expression changes approach dramatically reduced within-group variation compared to cross-sectional expression for improved prediction modeling. Conclusions: This novel FVC-gene predictor developed from short-term longitudinal gene expression changes successfully discriminates most patients with high likelihood of one-year 10% FVC decline. This tool may better reflect disease activity and prove useful for predictive enrichment of clinical trial populations.
2019-09-30 | GSE133298 | 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
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.
2014-06-30 | GSE38958 | GEO
Project description:Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive and highly lethal lung disease with unknown etiology and poor prognosis.
2019-05-29 | GSE116086 | GEO
Project description:RNAseq in Idiopathic Pulmonary Fibrosis
| PRJNA476994 | ENA
Project description:Changes in the respiratory microbiome are associated with disease progression in Idiopathic pulmonary fibrosis (IPF). The role of the host response to the respiratory microbiome however remains unknown. The role of this study is to explore the host-microbial interaction in IPF. Network analysis of gene expression data identified two gene modules that strongly associate with a diagnosis of IPF, BAL bacterial burden (determined by 16S quantitative PCR) and specific microbial OTUs, as well as lavage and peripheral blood neutrophilia. Genes within these modules that are involved in the host defence response include NLRC4, PGLYRP1, MMP9, DEFA4. The modules also contain two genes encoding specific antimicrobial peptides (SLPI and CAMP). Many of these particular transcripts were associated with survival and showed longitudinal over expression in subjects experiencing disease progression, further strengthening their relationship with disease. Integrated analysis of the host transcriptome and microbial signatures demonstrates an apparent host response to the presence of an altered or more abundant microbiome. These responses remain elevated on longitudinal follow up, suggesting that the bacterial communities of the lower airways may be acting as persistent stimuli for repetitive alveolar injury in IPF. Sixty patients diagnosed with IPF were prospectively enrolled, together with 20 matched controls. Subjects underwent bronchoalveolar lavage (BAL) and peripheral whole blood was collected into PAXgene tubes for all subjects at baseline. For IPF subjects additional samples were taken at 1, 3, and 6 months and (if alive) a year. Gene expression profiles were generated using Affymetrix Human Gene1.1ST Arrays.
2017-01-14 | GSE93606 | 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