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Multi-Omics Analysis and Molecular Subtypes Provide Potential Therapeutic Implications in Fumarate Hydratase-deficient Renal Cell Carcinoma

GSE273036 Homo sapiens (human) RNA-seq (gene expression) 93 samples Submitted 2024/10/20 Platform GPL20795
At a glance

RNA-seq (gene expression) · human · 93 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
93 samples: 93 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)
Conducting an in-depth exploration of the multi-omics landscape of Fumarate Hydratase (FH)-deficient renal cell carcinoma (RCC), the study aimed to unravel the intricate molecular mechanisms underlying this rare and aggressive form of renal cancer. Analyzing 126 patients with FH-deficient RCC, we identified distinct variant spectrum of FH gene, with truncating mutation correlated with adverse prognostic factors and a worse response to immune checkpoint blockade.
Read the full summary ↓
The identification of three molecular subtypes with diverse clinical/genomic characteristics and varying response to systemic treatment further enriched the understanding of the heterogeneous tumor microenvironment (particularly immune-related) in FH-deficient RCC, and suggested potential therapeutic targets. These findings offer a basis for molecular stratification, shedding light on the tailored therapeutic strategies in improving treatment response of FH-deficient RCC.
This dataset
Open GSE273036 in finder → Find more human RNA-seq datasets → Kidney 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 GSE273036_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 PRJNA1135765 and SRA study SRP520602. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
93 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 56 samples read as cases, 37 as controls.

How we read this: Read from 93 sample titles: 93 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

+ 53 more — browse all 93 samples with per-sample file links →

More Kidney 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.