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Whole-Transcriptome Profiling Reveals RNA Expression Dynamics and ceRNA Network Remodeling across Health, Sepsis, and Sepsis-Associated Acute Respiratory Distress Syndrome [RNA-Seq]

GSE341493 Homo sapiens (human) Non-coding RNA profiling 12 samples Submitted 2026/08/06 Platform GPL16791
At a glance

Non-coding RNA profiling · human · 12 samples

What it is
Non-coding RNA profilingLevels of RNAs that do not code for proteins, such as miRNA or lncRNA.
Organism
Homo sapiens (human)
Samples
12 samples: 4 × healthy control vs 4 × patient with sepsis without ARDS vs 4 × patient with sepsis-associated ARDSGroups 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)
Background: Sepsis-associated acute respiratory distress syndrome (ARDS) is a severe manifestation of infection-related organ dysfunction characterized by dysregulated immune-inflammatory responses and disruption of tissue barriers. However, the whole-transcriptome alterations underlying the progression from sepsis to sepsis-associated ARDS remain insufficiently characterized.
Read the full summary ↓
Therefore, this study aimed to systematically characterize the expression patterns of mRNAs, long non-coding RNAs (lncRNAs), circular RNAs (circRNAs) and microRNAs (miRNAs) in peripheral blood from healthy controls, sepsis without ARDS, and sepsis-associated ARDS patients.Methods: 12 participants were enrolled, comprising four healthy controls (HC group), 4 patients with sepsis without ARDS (SWA group), and 4 patients with sepsis-associated ARDS (SA group). Peripheral blood samples were obtained and subjected to whole-transcriptome sequencing. Trend analysis was performed to identify transcripts exhibiting monotonic increases or decreases across the HC–SWA–SA sequence. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were subsequently performed. Target-gene prediction and expression-correlation analyses were also performed to construct a competing endogenous RNA (ceRNA) regulatory network.Results: All sequencing datasets passed the quality control and were suitable for the following analyses. A total of 773 trend genes were identified, of which 350 were progressively upregulated and 423 were progressively downregulated. These genes were predominantly enriched in immune responses, cell adhesion, cytokine production, extracellular matrix-related processes, MAPK and PI3K–Akt signaling pathways, integrin signaling, focal adhesion, ECM–receptor interaction, and antigen processing and presentation. A total of 189 trend lncRNAs, 252 trend circRNAs, and six trend miRNAs were identified. Their predicted functions were associated with RNA metabolism, translational regulation, chromatin-associated regulation, cytoskeletal organization, epithelial barrier integrity, cell polarity, and intracellular trafficking. The resulting ceRNA network contained several miRNA-centered modules involving hsa-miR-193b-3p, hsa-let-7b-3p, hsa-miR-223-3p, hsa-miR-223-5p, novel_mir_76, and novel_mir_177. These modules were functionally associated mainly with ion transport, calcium signaling, membrane-channel activity, tight-junction regulation, and vesicular trafficking. Conclusion: This study delineates an integrated whole-transcriptome landscape of progressive RNA expression alterations across the transition from health to sepsis without ARDS and, ultimately, to sepsis-associated ARDS. The findings suggest that coordinated dysregulation of coding and non-coding RNAs may contribute to immune-inflammatory activation, extracellular matrix remodeling, barrier dysfunction, and altered post-transcriptional regulation. Collectively, these findings highlight candidate molecular signatures and regulatory axes that warrant further investigation in mechanistic and biomarker-oriented studies of sepsis-associated ARDS.
This dataset
Open GSE341493 in finder → Find more human RNA-seq datasets →
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.

Series matrix ↓
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 GSE341493_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

Also filed as BioProject PRJNA1502839 and SRA study SRP721593. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
4 × healthy control vs 4 × patient with sepsis without ARDS vs 4 × patient with sepsis-associated ARDS

Supports a case/control comparison: 8 samples read as cases, 4 as controls.

How we read this: 3 replicated groups read from 12 sample titles; they account for 12 of them. 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

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