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NUDT21 alters glioma migration through differential alternative polyadenylation of LAMC1

GSE230769 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2024/05/01 Platform GPL15520
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)
Samples
12 samples: 3 × LN229 Scramble vs 3 × LN229 shNUDT21 vs 3 × U251 Scramble vs 3 × U251 shNUDT21Groups 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)
We identified LAMC1 as a critical NUDT21 target involved in several core glioma-driving signaling pathways. qRT-PCR analysis confirmed that NUDT21-knockdown in GBM cells resulted in the preferred usage of the proximal C/PAS of LAMC1. Furthermore, functional studies revealed that NUDT21-knockdown-induced 3’UTR shortening of LAMC1 was sufficient to induce translational gain, as LAMC1 was upregulated in these cells compared to their respective controls.
Read the full summary ↓
Additionally, we demonstrated that primary glial cells recapitulated this phenomenon. We also further dissected the miRNA-mediated mechanism by which LAMC1 expression was regulated by showing that NUDT21 knockdown in glioma cells directed the 3’UTR shortening of LAMC1, removing binding sites for miR-124/506 and consequently inducing LAMC1 expression. Remarkably, we reported that the knockdown of NUDT21 significantly promoted GBM cell migration and that co-depletion of LAMC1 and NUDT21 abrogated this effect. Lastly, we observed that LAMC1 3’UTR shortening predicted poor prognosis of low- and high-grade glioma patients from The Cancer Genome Atlas.
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 GSE230769_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 PRJNA962545 and SRA study SRP434887. Searching any of these in the dataset finder brings you back here.

Study design
3 × LN229 Scramble vs 3 × LN229 shNUDT21 vs 3 × U251 Scramble vs 3 × U251 shNUDT21

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
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