← BioTransfer GEO Dataset Finder
GEO series

Fucosyltransferase 8-derived circular RNA drives M2 polarization of macrophages through ENO1-TNF signaling axis to promote lung cancer progression

GSE307293 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/12/10 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 12 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)
Topic
Lung cancerThis series is part of our curated briefing on it.
Samples
12 samples: 3 × A549-NC vs 3 × A549-sh-circFUT8 vs 3 × H1299-NC vs 3 × H1299-sh-circFUT8Groups 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
Fucosyltransferase 8-Derived Circular RNA Drives M2 Polarization of Macrophages Through ENO1-TNF Signaling Axis to Promote Lung Cancer ProgressionThoracic cancer · 2025
Summary (written by the submitters)
CircFUT8 (hsa_circ_0003028) was significantly upregulated in lung cancer tissues, correlating with advanced stages and poor prognosis. It enhanced lung cancer cell proliferation, migration, and invasion while inhibiting apoptosis in cellular and animal models. Mechanistically, circFUT8 directly binds ENO1 to form an RNA-protein complex, promoting M2 macrophage polarization. Silencing circFUT8 reversed these effects by suppressing ENO1 and M2 polarization, inhibiting tumor progression.
Published in
Fucosyltransferase 8-Derived Circular RNA Drives M2 Polarization of Macrophages Through ENO1-TNF Signaling Axis to Promote Lung Cancer Progression
Ren Y, Shen Y, Wu Q et al. · Thoracic cancer 2025 · PMID 41328768 · doi:10.1111/1759-7714.70194
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 GSE307293_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 PRJNA1315159 and SRA study SRP617660. Searching any of these in the dataset finder brings you back here.

Study design
3 × A549-NC vs 3 × A549-sh-circFUT8 vs 3 × H1299-NC vs 3 × H1299-sh-circFUT8

Supports a between-group comparison across 12 samples.

How we read this: 4 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
More Lung cancer datasets

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.