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POFUT1 Serves as an Independent Prognostic Factor and Therapeutic Target by Activating the PI3K/AKT Pathway in Glioma

GSE316535 Homo sapiens (human) RNA-seq (gene expression) 14 samples Submitted 2026/03/31 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 14 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
14 samples: 14 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)
Protein O - Fucosyltransferase 1 (POFUT1) is a significantly different gene in gliomas. The analysis of the The Cancer Genome Atlas Program (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases and clinical samples found that POFUT1 was highly expressed in tumor tissue (p < 0.01). Glioma patients with high POFUT1 had a significantly poorer clinical prognosis than those with low POFUT1.
Read the full summary ↓
And POFUT1 amplification and protein overexpression were significantly correlated with malignant progression and poor prognosis in glioma patients. Furthermore, our experimental results found that knocking down POFUT1 can inhibit the abilities of proliferation and migration in gliomas. RNA sequencing was found that the downstream significantly differentially expressed genes of POFUT1 are enriched in the PI3K-AKT signaling pathway, and cell experiments also verified this result. Our results suggest that POFUT1 may affect the malignant progression of glioma by regulating the PI3K-AKT signaling pathway. The result may provide new insights into the treatment of glioma.
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 GSE316535_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 PRJNA1403273 and SRA study SRP662837. Searching any of these in the dataset finder brings you back here.

Study design
14 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 14 sample titles: 14 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
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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.