Dataset Information

Longitudinal Blood Transcriptomic Changes Predict Lung Function Decline in Idiopathic Pulmonary Fibrosis

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE132607 | GEO | 2019/09/30

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Chronic injury in kidney transplants remains a major cause of graft loss. The aim of this study was to identify a predictive gene set capable of classifying renal grafts at risk for progressive injury due to fibrosis.The Genomics of Chronic Allograft Rejection (GoCAR) study is a prospective, multicenter study. Biopsies obtained prospectively 3 months after transplantation from renal allograft recipients (n=159) with stable renal function were analyzed for gene expression by microarray. Genes were sought which correlated with subsequent 12-month Chronic Allograft Damage Index (CADI) but neither CADI in the 3 month biopsy nor other histological or clinical parameters. We identified a set of 13 genes that was independently predictive for the development of fi brosis at 1 year (ie, CADI-12 >=2). The gene set had high predictive capacity (area under the curve [AUC] 0·967), which was superior to that of baseline clinical variables (AUC 0·706) and clinical and pathological variables (AUC 0·806). Furthermore routine pathological variables were unable to identify which histologically normal allografts would progress to fi brosis (AUC 0·754), whereas the predictive gene set accurately discriminated between transplants at high and low risk of progression (AUC 0·916). The 13 genes also accurately predicted early allograft loss (AUC 0·842 at 2 years and 0·844 at 3 years). We validated the predictive value of this gene set in an independent cohort from the GoCAR study (n=45, AUC 0·866) and two independent, publically available expression datasets (n=282, AUC 0·831 and n=24, AUC 0·972). Biopsies obtained prospectively 3 months after transplantation from renal allograft recipients (n=159) with stable renal function were analyzed for gene expression by Affymetrix exon arrays. Genes that were specifically correlated with 12-month Chronic Allograft Damage Index (CADI) but neither CADI in the 3 month biopsy nor other histological or clinical parameters were identified by correlation analysis. The minimal geneset was further identified to predict the progressors/non progressors and validated by qPCR in an independent cohort and two public datasets. This dataset is part of the TransQST collection.

Project description:Background: In a previous study of patients with assumed benign IgA nephropathy (IgAN), we have shown that 18.6% develop a progressive clinical course, which was not predicted by any known clinical nor histopathological parameters (Knoop 2017). We aim to differentiate between future progressors and non-progressors with assumed benign IgAN by using glomerular transcriptomics from diagnostic kidney biopsies and to investigate whether this can identify drugs to be used in those patients. Methods: We included adult progressors (n=15) and patients with clinical remission (non-progressors, n=21) from our previously reported IgAN cohort. Glomerular cross-sections were obtained through laser-capture microdissection from 10µm archival kidney biopsy sections for mRNA sequencing, using the SMARTer Stranded Total RNA-Seq kit – Pico Input Mammalian (Clontech Laboratories, California, USA). Key results were validated in external cohorts and on the protein level with immunohistochemistry in the discovery cohort and an internal validation cohort. We also compared our transcriptomic findings with an external genome-wide association study (GWAS) dataset (Kiryluk 2021). Results: For the statistical analyses, we studied 8 progressors and 9 non-progressors. Based on the 1,240 differentially expressed glomerular genes from the initial kidney biopsies, we were able to identify future IgAN progressors and non-progressors using a two-component classifier. The classifier predicted disease progression with 88% accuracy, 75% sensitivity and 100% specificity (AUC=0.875), more accurate than the recommended International IgAN Prediction Tool in this specific patient cohort. Among our differentially expressed genes, 10 were also found in the GWAS dataset (Kiryluk 2021). We identified 45 drug targets in the DrugBank database, including angiotensinogen, a target of sparsentan, currently investigated as therapy in IgAN (NCT03762850 and NCT04663204). The findings on upregulation of angiotensinogen, which was independent of blood pressure, were validated in an internal IgAN validation cohort. Interestingly, complement activation did not play a major role in our data. Our work suggests that LXR, FXR and macrophage activation pathways could be critically involved in the inhibition of the progression of low-risk ccRCC. Furthermore, a 10-component classifier could support an early identification of apparently low-risk ccRCC patients.

Dataset Information

Longitudinal Blood Transcriptomic Changes Predict Lung Function Decline in Idiopathic Pulmonary Fibrosis

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets