Quantification of Systemic Inflammation by a Gene Score Uncovers Relevant Features on Acutely Decompensated Cirrhosis
RNA-seq (gene expression) · human · 1177 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
- 1177 samples: 1177 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.
- Paper
- Gene score to quantify systemic inflammation in patients with acutely decompensated cirrhosisGut · 2025
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 GSE239775_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 PRJNA1000950 and SRA study SRP452972. 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 the first 400 of 1177 sample titles: 400 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.
- GSM7671236 Whole-blood RNA-seq from AD(01_I_007_S00_09)
- GSM7671237 Whole-blood RNA-seq from ACLF(01015_A01)
- GSM7671238 Whole-blood RNA-seq from ACLF(01015_S00)
- GSM7671239 Whole-blood RNA-seq from ACLF(01020_A01)
- GSM7671240 Whole-blood RNA-seq from ACLF(01020_S00)
- GSM7671241 Whole-blood RNA-seq from AD(011011_S00)
- GSM7671242 Whole-blood RNA-seq from AD(011017_S00)
- GSM7671243 Whole-blood RNA-seq from AD(011017_S01)
- GSM7671244 Whole-blood RNA-seq from AD(02_I_022_S00_09)
- GSM7671245 Whole-blood RNA-seq from pre-ACLF(02_I_027_A00_09)
- GSM7671246 Whole-blood RNA-seq from pre-ACLF(02_I_027_S00_09)
- GSM7671247 Whole-blood RNA-seq from pre-ACLF(02_I_028_S00_09)
- GSM7671248 Whole-blood RNA-seq from AD(02_I_040_S00_09)
- GSM7671249 Whole-blood RNA-seq from AD(02_I_042_S00_09)
- GSM7671250 Whole-blood RNA-seq from pre-ACLF(02_I_048_S00_09)
- GSM7671251 Whole-blood RNA-seq from AD(02_I_055_S00_09)
- GSM7671252 Whole-blood RNA-seq from AD(02_I_056_S00_09)
- GSM7671253 Whole-blood RNA-seq from AD(021002_S00)
- GSM7671254 Whole-blood RNA-seq from AD(021014_S00)
- GSM7671255 Whole-blood RNA-seq from AD(021020_S00)
- GSM7671256 Whole-blood RNA-seq from AD(021020_S01)
- GSM7671257 Whole-blood RNA-seq from AD(021033_S00)
- GSM7671258 Whole-blood RNA-seq from AD(021033_S01)
- GSM7671259 Whole-blood RNA-seq from ACLF(022001_S00)
- GSM7671260 Whole-blood RNA-seq from ACLF(022002_S00)
- GSM7671261 Whole-blood RNA-seq from ACLF(022002_S01)
- GSM7671262 Whole-blood RNA-seq from ACLF(022004_S00)
- GSM7671263 Whole-blood RNA-seq from ACLF(022004_S01)
- GSM7671264 Whole-blood RNA-seq from ACLF(022005_S00)
- GSM7671265 Whole-blood RNA-seq from ACLF(022005_S01)
- GSM7671266 Whole-blood RNA-seq from pre-ACLF(03_I_001_S00_09)
- GSM7671267 Whole-blood RNA-seq from HS(03_K_004_S00_09)
- GSM7671268 Whole-blood RNA-seq from HS(03_K_005_S00_09)
- GSM7671269 Whole-blood RNA-seq from HS(03_K_012_S00_09)
- GSM7671270 Whole-blood RNA-seq from HS(03_K_014_S00_09)
- GSM7671271 Whole-blood RNA-seq from HS(03_K_017_S00_09)
- GSM7671272 Whole-blood RNA-seq from HS(03_K_020_S00_09)
- GSM7671273 Whole-blood RNA-seq from AD(031016_S00)
- GSM7671274 Whole-blood RNA-seq from ACLF(032014_S00)
- GSM7671275 Whole-blood RNA-seq from ACLF(032014_S01)
+ 1137 more — browse all 1177 samples with per-sample file links →
- GSE171741 Albumin treatment transcriptionally reprograms circulating immune cells in patients with acutely decompensated cirrhosis 139 samples shares decompensated, cirrhosis, acutely
- GSE224198 Single-cell multi-omics analysis revealing immune features of inactivated SARS-CoV-2 vaccination in systemic lupus erythematosus patients 26 samples shares systemic, features
- GSE277909 Epigenetic attenuation of interferon signaling drives aging-related improvements in systemic lupus 271 samples shares systemic
- GSE318645 Dual Immune Checkpoint Blockade Induces Distinct Systemic and Intratumoral Immune Activation in Recurrent Glioblastoma 226 samples shares systemic
- GSE313307 Systemic infections alter cortical transcriptional signatures in Alzheimer′s disease 202 samples shares systemic
- GSE276755 Systemic and breast chronic inflammation and hormone disposition promote a tumor-permissive environment for breast cancer in older women (Human Bulk RNA-Seq) 168 samples shares systemic
- GSE286040 Endothelial Sensitivity to Pro-Fibrotic Signals Links Systemic exposure to Pulmonary Fibrosis 96 samples shares systemic
- GSE130955 Global skin gene expression profile of early diffuse cutaneous systemic sclerosis shows a prominent innate and adaptive immunity profile 91 samples shares systemic
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.