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Noncoding RNA profiling in omentum adipose tissue from obese patients and the identification of novel metabolic biomarkers

GSE286454 Homo sapiens (human) RNA-seq (gene expression) 10 samples Submitted 2025/01/19 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 10 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
10 samples: 5 × normal vs 5 × obesityGroups 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
Noncoding RNA profiling in omentum adipose tissue from obese patients and the identification of novel metabolic biomarkersFrontiers in genetics · 2025
Summary (written by the submitters)
Obesity, a prevalent metabolic disorder, is linked to perturbations in the balance of gene expression regulation. Noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), and microRNAs (miRNAs), play pivotal roles in regulating gene expression. The aim of this study was to identify additional ncRNA candidates that are implicated in obesity, elucidating their potential as key regulators of the pathogenesis of obesity.
Read the full summary ↓
In our research, compared with those in healthy individuals, various lncRNAs, circRNAs, and miRNAs were significantly differentially expressed in obese subjects. We established comprehensive ncRNA profiles from human greater omentum adipose tissue and compared the profiles between obese individuals with healthy controls via whole-transcriptome sequencing. Subsequent validation via qRT‒PCR substantiated the sequencing results, reinforcing the reliability of our findings. These insights not only validate the importance of ncRNAs but also lay a theoretical foundation for further functional exploration of the roles of lncRNAs, circRNAs, and miRNAs in obesity, potentially offering novel perspectives on the pathogenesis of this condition.
Published in
Noncoding RNA profiling in omentum adipose tissue from obese patients and the identification of novel metabolic biomarkers
Zhang Y, Chen A, Lu S et al. · Frontiers in genetics 2025 · PMID 39981261 · doi:10.3389/fgene.2025.1533637
This dataset
Open GSE286454 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 GSE286454_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 ↗

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 PRJNA1209529 and SRA study SRP556726. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39981261) ↗
Study design
5 × normal vs 5 × obesity

Supports a between-group comparison across 10 samples.

How we read this: 2 replicated groups read from 10 sample titles; they account for 10 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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