Identification of Whole Blood Gene Expression Signature to Predict the Therapeutic Effect of Nintedanib in Idiopathic Pulmonary Fibrosis
RNA-seq (gene expression) · human · 177 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 177 samples: 177 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Direct links to NCBI and ENA, 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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE215274_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA889505 and SRA study SRP402036. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 177 sample titles: 177 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM6630654 Responder 0M [0M_1]
- GSM6630655 Nonresponder 0M [0M_2]
- GSM6630656 Nonresponder 0M [0M_3]
- GSM6630657 Responder 0M [0M_4]
- GSM6630658 Responder 0M [0M_5]
- GSM6630659 Responder 0M [0M_6]
- GSM6630660 0M in AE case [0M_7]
- GSM6630661 Responder 0M [0M_8]
- GSM6630662 Responder 0M [0M_9]
- GSM6630663 Nonresponder 0M [0M_10]
- GSM6630664 Responder 0M [0M_11]
- GSM6630665 Nonresponder 0M [0M_12]
- GSM6630666 Responder 0M [0M_13]
- GSM6630667 0M in AE case [0M_14]
- GSM6630668 Nonresponder 0M [0M_15]
- GSM6630669 Responder 0M [0M_16]
- GSM6630670 Responder 0M [0M_17]
- GSM6630671 Responder 0M [0M_18]
- GSM6630672 Responder 0M [0M_19]
- GSM6630673 Responder 0M [0M_20]
- GSM6630674 Nonresponder 0M [0M_21]
- GSM6630675 Responder 0M [0M_22]
- GSM6630676 Nonresponder 0M [0M_23]
- GSM6630677 Responder 0M [0M_24]
- GSM6630678 Responder 0M [0M_25]
- GSM6630679 0M in AE case [0M_26]
- GSM6630680 Responder 0M [0M_27]
- GSM6630681 Responder 0M [0M_28]
- GSM6630682 Responder 0M [0M_29]
- GSM6630683 Responder 0M [0M_30]
- GSM6630684 Responder 0M [0M_31]
- GSM6630685 Responder 0M [0M_32]
- GSM6630686 Nonresponder 0M [0M_33]
- GSM6630687 0M in AE case [0M_34]
- GSM6630688 Responder 0M [0M_35]
- GSM6630689 Responder 0M [0M_36]
- GSM6630690 Responder 0M [0M_37]
- GSM6630691 Responder 0M [0M_38]
- GSM6630692 Responder 0M [0M_39]
- GSM6630693 Responder 0M [0M_40]
+ 137 more — browse all 177 samples with per-sample file links →
- GSE233844 Single-cell profiling reveals immune aberrations in progressive idiopathic pulmonary fibrosis 38 samples shares idiopathic, pulmonary, fibrosis
- GSE326573 T-cell Multiomic Analysis Identifies Subsets and Mechanisms of Interaction with Epithelial Cells in Idiopathic Pulmonary Fibrosis 59 samples shares idiopathic, pulmonary, fibrosis
- GSE310479 Targeting Fused in Sarcoma (FUS): A Novel Antisense Strategy for Treating Idiopathic Pulmonary Fibrosis 38 samples shares idiopathic, pulmonary, fibrosis
- GSE277709 Whole blood transcriptome profile identifies motor neurone disease (MND) RNA biomarker signatures 84 samples shares signature, whole
- GSE291471 Integrative blood profiling uncovers inflammatory network signatures in high-altitude pulmonary edema 83 samples shares signature, pulmonary
- GSE325674 Leveraging experimental evolution to extract predictive collateral drug response signatures in Ewings Sarcoma 73 samples shares signature, predict
- GSE288704 Whole transcriptome analysis of unmutated sporadic ALS patients’ peripheral blood reveals phenotype-specific gene expression signature 66 samples shares signature, whole
- GSE273848 Definition of a multi-omics signature for Esophageal Adenocarcinoma prognosis prediction [totalRNA-seq] 40 samples shares signature, predict
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.