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Impact of sunitinib resistance on clear cell renal cell carcinoma therapeutic sensitivity in vitro

GSE216494 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2024/04/17 Platform GPL11154Platform 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
Kidney cancerThis series is part of our curated briefing on it.
Samples
12 samples: 12 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.
Paper
Impact of sunitinib resistance on clear cell renal cell carcinoma therapeutic sensitivity in vitroCell cycle (Georgetown, Tex.) · 2024
Summary (written by the submitters)
Sunitinib resistance creates a major clinical challenge for the treatment of advanced clear cell renal cell carcinoma (ccRCC) and functional and metabolic changes linked to sunitinib resistance are not fully understood. We sought to characterize the molecular and metabolic changes induced by the development of sunitinib resistance in ccRCC by developing and characterizing the transcriptome of two human ccRCC cell lines resistant to sunitinib.
Published in
Impact of sunitinib resistance on clear cell renal cell carcinoma therapeutic sensitivity in vitro
Ghosh S, Garige M, Haggerty PR et al. · Cell cycle (Georgetown, Tex.) 2024 · PMID 38263737 · doi:10.1080/15384101.2024.2306760
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 GSE216494_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 PRJNA894090 and SRA study SRP404388. Searching any of these in the dataset finder brings you back here.

Study design
12 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 12 sample titles: 12 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.