← BioTransfer GEO Dataset Finder
GEO series

Long non-coding RNA TTN-AS1 promotes acute liver injury in sepsis: A novel potential monitoring and therapeutic target [RNA-Seq]

GSE301821 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2026/07/04 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 16 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
16 samples: 16 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.
Summary (written by the submitters)
Sepsis-associated liver injury (SALI) is a common and severe complication of sepsis that significantly affects patient outcomes. However, reliable early biomarkers and effective therapeutic targets are still lacking. In this study, we integrated whole-blood transcriptomic sequencing data and Olink inflammation-related proteomics data from SALI patients to identify key regulatory factors.
Read the full summary ↓
By integrating weighted gene co-expression network analysis (WGCNA), differential expression profiling, and competing endogenous RNA (ceRNA) network construction, we identified the long non-coding RNA TTN-AS1 as a potential regulatory candidate. Its upregulation in SALI was further validated using a public dataset. Functional validation in a cecal ligation and puncture (CLP) mouse model demonstrated that TTN-AS1 overexpression significantly aggravated liver injury, as evidenced by elevated serum ALT and AST levels (P < 0.001), increased inflammatory cytokines IL-6 and TNF-α (P < 0.001), disrupted liver architecture and aggravated inflammatory infiltration on H&E staining, enhanced expression of apoptosis-related proteins Caspase-3 (P < 0.001) and BAX (P < 0.01), and increased hepatocyte apoptosis indicated by TUNEL assay (P < 0.001). This is the earliest report demonstrating that lncRNA TTN-AS1 contributes to the pathogenesis of SALI, highlighting its promise as a biomarker for early diagnosis and a potential target for therapy.
This dataset
Download · which file do I need?

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.

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.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE301821_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

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 PRJNA1287615 and SRA study SRP598966. Searching any of these in the dataset finder brings you back here.

Study design
16 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 16 sample titles: 16 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.

Samples in this study
Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.