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CDC7 inhibition impairs neuroendocrine transformation in lung and prostate tumors through MYC degradation II

GSE252567 Homo sapiens (human) RNA-seq (gene expression) 14 samples Submitted 2024/05/01 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 14 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
14 samples: 14 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
CDC7 inhibition impairs neuroendocrine transformation in lung and prostate tumors through MYC degradationSignal transduction and targeted therapy · 2024
Summary (written by the submitters)
Neuroendocrine (NE) transformation is a mechanism of resistance to targeted therapy in lung and prostate adenocarcinomas leading to poor prognosis. Upregulation of the cell cycle kinase CDC7 occurred in tumors undergoing NE transformation, after TP53/RB1 co-inactivation, leading to induced sensitivity to the CDC7 inhibitor simurosertib.
Read the full summary ↓
CDC7 inhibition suppressed NE transdifferentiation and extended response to targeted therapy in in vivo models of NE transformation by inducing the proteasome-mediated degradation of MYC, implicated in stemness and histological transformation. Ectopic overexpression of a degradation-resistant MYC isoform reestablished the NE transformation phenotype observed on targeted therapy, even in the presence of simurosertib. CDC7 inhibition also markedly extended response to standard cytotoxics in lung and prostate small cell carcinoma models. These results nominate CDC7 inhibition as a strategy to constrain lineage plasticity and to effectively treat NE tumors. As simurosertib clinical efficacy trials are ongoing, this concept could be readily translated for patients at risk.
Published in
CDC7 inhibition impairs neuroendocrine transformation in lung and prostate tumors through MYC degradation
Quintanal-Villalonga A, Kawasaki K, Redin E et al. · Signal transduction and targeted therapy 2024 · PMID 39054323 · doi:10.1038/s41392-024-01908-y
This dataset
Open GSE252567 in finder → Find more human RNA-seq datasets →
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 GSE252567_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 PRJNA1061198 and SRA study SRP481731. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39054323) ↗
Study design
14 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 14 sample titles: 14 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.