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Integrated transcriptome and single-cell sequencing analysis identify blood-pancreas shared lncRNA biomarkers in new-onset T2DM

GSE298690 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2025/06/04 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 16 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 16 samples: 8 × ctrl vs 8 × newGroups 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)

Type 2 diabetes mellitus (T2DM) is characterized by beta-cell dysfunction and insulin resistance, but the early molecular drivers remain elusive. The aim of this study was to identify blood long non-coding RNAs (lncRNAs) as a potential systemic biomarkers in patients with new-onset, unmedicated T2DM. We performed transcriptome sequencing of peripheral blood from eight T2DM patients and eight healthy individuals.

Read the full summary ↓

The intersection of three differential expression analysis methods (Limma, DESeq2 and EdgeR) identified 1,709 lncRNAs with dysregulated expression in peripheral blood, of which , 853 lncRNAs were up-regulated and 856 lncRNAs were down-regulated. Weighted gene co-expression network analysis (WGCNA) identified two modules comprising a total of 1,617 lncRNAs that were significantly negatively associated with T2DM. By integrating differential expression analysis and WGCNA, we screened a total of 257 lncRNAs that were highly correlated with T2DM and exhibited significant changes in expression levels.Our findings provide valuable transcriptomic data for further studies of T2DM, and the screened blood lncRNAs may reflect systemic dysregulation as early biomarkers.

This dataset

Open GSE298690 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 GSE298690_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1270716 and SRA study SRP589124. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

8 × ctrl vs 8 × new

Supports a between-group comparison across 16 samples.

How we read this: 2 replicated groups read from 16 sample titles; they account for 16 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

GSM9021013 ctrl_1 ctrl
GSM9021014 ctrl_2 ctrl
GSM9021015 ctrl_3 ctrl
GSM9021016 ctrl_4 ctrl
GSM9021017 ctrl_5 ctrl
GSM9021018 ctrl_6 ctrl
GSM9021019 ctrl_7 ctrl
GSM9021020 ctrl_8 ctrl
GSM9021021 new_1 new
GSM9021022 new_2 new
GSM9021023 new_3 new
GSM9021024 new_4 new
GSM9021025 new_5 new
GSM9021026 new_6 new
GSM9021027 new_7 new
GSM9021028 new_8 new

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