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Adaptive anoikis resistance promotes ovarian cancer metastasis

GSE241546 Homo sapiens (human) RNA-seq (gene expression) 34 samples Submitted 2024/12/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 34 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
Ovarian cancerThis series is part of our curated briefing on it.
Samples
34 samples: 34 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)
Anoikis resistance or evasion of cell death triggered by cell detachment is a hallmark of cancer that is concurrent with cell survival and metastasis. Phenotypes of anoikis resistant cancer cells have been extensively investigated, however, how exposure to suspension stress may lead to acquisition of a anoikis resistance phenotype has not been previously described in detail.
Read the full summary ↓
Here we show using a spectrum of ovarian cancer cells, that cycles of suspension stress followed by attached growth, leads to adaptation and acquisition of resistance to cell death in suspension . Comparing the stepwise transcriptomic changes as cells acquire resistance, we find strong transcriptional reprogramming in the population with a majority of differentially expressed genes being downregulated during progressive acquisition of anoikis resistance. Adapted anoikis resistant cells display an enhanced dependency on oxidative phosphorylation and are capable of evasion from T cell-mediated immune surveillance. We find that such acquired anoikis resistance is not genetic. However, transcriptional reprogramming is essential to this process as acquisition of such adaptive anoikis resistance invitro and invivo are both exquisitely sensitive to specific inhibition of transcriptional reprogramming. Our data demonstrate that recovery from the verge of anoikis leads to adaptation that promotes metastasis in ovarian cancer, but can be therapeutically prevented by specific inhibitors to transcriptional reprogramming.
This dataset
Open GSE241546 in finder → Find more human RNA-seq datasets → Ovarian cancer landscape →
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 GSE241546_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 PRJNA1008671 and SRA study SRP456839. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
34 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 34 sample titles: 34 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
More Ovarian cancer datasets

Search all human RNA-seq datasets in GEO →

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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.