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SETD2 Deficiency Drives Mitochondrial DNA Leakage and Creates a Druggable Dependency on BCL-XL in Clear Cell Renal Cell Carcinoma

GSE297044 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2026/04/23 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 16 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
16 samples: 6 conditions, mostly in triplicateGroups 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)
SETD2 is frequently mutated or deleted in clear cell renal cell carcinoma (ccRCC). Loss of SETD2 could create synthetic lethal dependencies that confer therapeutic vulnerabilities. Here, we demonstrated that SETD2 deficiency promotes cytoplasmic mitochondrial DNA (mtDNA) leakage, leading to basal activation of cGAS-STING inflammatory signaling and increased apoptotic priming.
Read the full summary ↓
This inflammatory state upregulated the BH3-only protein NOXA, constrained MCL-1 function, and enforced a synthetic lethal dependency on the anti-apoptotic protein BCL-xL. Pharmacological inhibition of BCL-xL further amplified cGAS-STING signaling in SETD2-deficient cells through sublethal mitochondrial outer membrane permeabilization, resulting in increased mtDNA release and robust NOXA induction. Elevated NOXA neutralized the compensatory MCL-1-mediated survival signaling, triggering apoptosis. In contrast, SETD2 proficient ccRCC cells exhibited minimal cGAS-STING activation and failed to induce NOXA following BCL-xL inhibition, rendering them resistant. Genetic ablation of cGAS, STING, IRF3, or NOXA rescued sensitivity to BCL-xL inhibition, confirming that mtDNA-driven innate immune signaling is required for this dependency. In vivo, BCL-xL inhibition suppressed tumor growth and prolonged survival in SETD2-deficient xenograft models. Collectively, these findings establish a mechanistic link between SETD2 loss, mtDNA-driven innate immune activation, and enforced BCL-xL dependence in ccRCC, revealing a therapeutically targetable vulnerability in SETD2-deficient tumors.
This dataset
Open GSE297044 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 GSE297044_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 PRJNA1262435 and SRA study SRP585008. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in triplicate
786-O SETD2 WT_DMSO treated ×3 786-O SETD2 WT_A1331852 treated ×3 786-O SETD2 KO_DMSO treated ×3 786-O SETD2 KO_A1331852 treated ×3 A498_SETD2 deficient ×2 A498_SETD2 addback ×2

Supports a between-group comparison across 16 samples.

How we read this: 6 replicated groups read from 16 sample titles; they account for 16 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
More Kidney cancer datasets

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