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Transcriptomic Analysis of Circulating Extracellular Vesicles and Serum during the Perioperative period of Fontan and Glenn Surgery

GSE269879 Homo sapiens (human) Non-coding RNA profiling 109 samples Submitted 2024/12/20 Platform GPL24676
At a glance

Non-coding RNA profiling · human · 109 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
109 samples: 109 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)
Single-ventricle defects are palliatively corrected using the Glenn and Fontan procedures that provide lifesaving relief but lead to early mortality rates and poor prognosis post-operatively. The pathophysiology of these complications is diverse, and there are no clear predictors for these outcomes. As such, we aimed to identify novel biomarkers for single-ventricle associated outcomes through the characterization of the serum transcriptome and small extracellular vesicles (sEVs).
Read the full summary ↓
We report on molecular responses in serum and sEVs pre-operatively and post-operatively in patients undergoing either Glenn or Fontan operations. We found that transcriptomes of serum RNA and sEV RNA are distinctly different, whereas differences between the Fontan and Glenn transcriptomes did not show any significant changes. An in vitro wound healing assay further confirmed that post-operative sEVs have higher angiogenic potential. Moreover, we used a partial-least square regression model to identify 100 RNAs and 50 microRNAs that are involved in angiogenesis-related pathways and accurately predict our wound healing results and compared them with our previously published models of ovine Fontan surgery. Lastly, an EV-origin analysis showed decreased secretion of sEV from cardiac tissue in post-operative samples and increased secretion of sEVs from stomach and nervous tissue. Overall, this study provided evidence that the circulating sEVs transcriptome are potent biomarkers and demonstrated the role of sEVs as a possible mediator for formation of veno-venous collaterals.
This dataset
Open GSE269879 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 GSE269879_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 PRJNA1124062 and SRA study SRP513977. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
109 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 109 sample titles: 109 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

+ 69 more — browse all 109 samples with per-sample file links →

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.