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Photoproximity labeling of c-Myc reveals SLK as a cancer-specific co-regulator [total RNA-seq]

GSE307813 Homo sapiens (human) RNA-seq (gene expression) 15 samples Submitted 2025/09/16 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 15 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
15 samples: 15 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
Photoproximity labeling of c-Myc reveals SLK as a cancer specific co-regulatorbioRxiv : the preprint server for biology · 2025
Summary (written by the submitters)
Transcription factors (TFs) have long been aspirational therapeutic targets for the treatment of diseases, as their dysregulation is a common mechanism for altered cell states. Despite this, many TFs implicated in disease have disordered structures and lack canonical binding pockets, rendering them non-trivial targets for small molecule-based therapies.
Read the full summary ↓
Directly inhibiting TF function has proven difficult, but indirect inhibition by targeting the effector molecules that modulate TF function is a promising, yet underexplored, alternative approach. Here we report a strategy for capturing cancer-specific protein-protein interactions using context-dependent µMap photoproximity labeling. Using an intein-based method for catalyst conjugation in biochemically intact nuclei, we demonstrate that we can capture unique protein interactomes of c-Myc in healthy and cancerous prostate cell lines, and that these unique interactors can be mined to identify druggable vulnerabilities. We find that a cancer-specific c-Myc interactor, STE20 like kinase (SLK), selectively promotes c-Myc stabilization at the protein level, drives epithelial morphology, and is essential for tumorigenesis, validating it as a viable therapeutic target. Mechanistically, this stabilization is driven by SLK-mediated phosphorylation of c-Myc at serine 329, which antagonizes GSK3β-dependent phosphorylation of the c-Myc phosphodegron and effectively increases the stability of c-Myc. This cancer-selective interaction is enabled by a change in SLK splicing that promotes nuclear localization of the long isoform, rather than changes at the protein or total RNA level. Furthermore, analysis of cancer patient data shows a strong correlation between the SLK long splice isoform and expression of c-Myc targets across multiple tumor types. Importantly, the SLK-c-Myc interaction is validated in cancer cell lines from diverse tissues, suggesting this novel regulatory axis is broadly operative across human cancer.
Published in
Photoproximity labeling of c-Myc reveals SLK as a cancer specific co-regulator
Milione RR, Tong F, Wolfe KL et al. · bioRxiv : the preprint server for biology 2025 · PMID 41256636 · doi:10.1101/2025.10.02.680136
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 GSE307813_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 PRJNA1327355 and SRA study SRP619195. Searching any of these in the dataset finder brings you back here.

Study design
15 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 15 sample titles: 15 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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