GEO series

Peripheral Blood Mononuclear Cell Gene Expression Profiles May Predict Poor Outcome in Idiopathic Pulmonary Fibrosis [Agilent]

GSE28042 Homo sapiens (human) Expression microarray 94 samples Submitted 2013/10/15 Platform GPL6480

At a glance

Expression microarray · human · 94 samples

What it is: Expression microarrayGene activity read on a chip (the older technology that RNA-seq replaced).
Organism: Homo sapiens (human)
Samples: 94 samples
Data you can get: A processed expression table (series matrix) and the array files, from NCBI.

Summary (written by the submitters)

Background: In this study we aimed to identify peripheral blood mononuclear cell (PBMC) gene expression profiles predictive of poor outcomes in idiopathic pulmonary fibrosis (IPF) Methods: Microarray analyses of PBMC were performed in 120 patients from discovery (n=45) and replication cohorts (n=75). Genes and pathways associated with transplant-free survival (TFS) were identified and confirmed by qRT-PCR.

Read the full summary ↓

Findings: 52 genes were predictive of TFS in a discovery cohort (FDR<5%, Cox score above 2.5 or below -2.5). Clustering the replication cohort samples using these genes distinguished two patient groups with significantly different TFS (hazard ratio 1.96, 95%CI 1.01-3.8, P=0.018). Decreased expression of “The co-stimulatory signaling during T cell activation” Biocarta pathway and in particular CD28, ICOS, LCK and ITK was associated with shorter TFS times in each cohort (FDR<5%). qRT-PCR expression of CD28, ICOS, LCK and ITK correlated with the microarray results in the discovery cohort (P<0.05) and their decreased expression was predictive of shorter TFS in the replication cohort (P<0.05). A genomic and clinical model demonstrated an area under the ROC curve of 78.5% at 2.4 months for death and lung transplant prediction. Interpretation: Our results suggest that CD28, ICOS, LCK and ITK are outcome biomarkers in IPF.

This dataset

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Download · which file do I need?

Direct links to NCBI, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need.

All supplementary files ↗

3 · The original raw files

Whole study · RAW.tar

Everything the authors deposited as one archive (GSE28042_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

Whole study · RAW.tar ↓

What the files usually look like: For arrays the series matrix is normally the normalised expression table you want; the raw array files (CEL and similar) are in the RAW.tar.

Also filed as BioProject PRJNA141911. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 24089408) ↗

Samples in this study

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+ 94 more — browse all 94 samples with per-sample file links →

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