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Oncogene induced senescence and senescence-associated secretory phenotype (SASP) determines the efficacy of palbociclib in PIK3CA mutated colorectal cancer

GSE276620 Homo sapiens (human) RNA-seq (gene expression) 26 samples Submitted 2025/04/10 Platform GPL29480
At a glance

RNA-seq (gene expression) · human · 26 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
Colorectal cancerThis series is part of our curated briefing on it.
Samples
26 samples: 13 conditions, mostly in duplicateGroups 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)
Palbociclib, a classic CDK4/6 inhibitor, has been extensively studied in the context of targeted cancer therapy. We have systematically investigated the applicability of palbociclib in colorectal cancer, utilizing high-throughput sequencing and other methods to explore its resistance mechanisms. Our findings reveal that PIK3CA gene mutations promote a palbociclib-induced senescent phenotype and alter the expression profile of SASP factors.
Read the full summary ↓
Among these, the SASP factor LCN2 contributes to cellular tolerance to palbociclib by upregulating EGFR protein expression. In summary, our sequencing data provide a molecular framework for the relationship between PIK3CA gene mutations and both palbociclib-induced cellular senescence and resistance.
This dataset
Open GSE276620 in finder → Find more human RNA-seq datasets → Colorectal 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 GSE276620_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 PRJNA1158237 and SRA study SRP531282. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
13 conditions, mostly in duplicate
LOVO_WT ×2 LOVO_E545K ×2 LOVO_H1047 ×2 p_LOVO_WT ×2 p_LOVO_E545K ×2 p_LOVO_H1047 ×2 cm_LOVO_WT ×2 cm_LOVO_E545K ×2 +5 more

Supports a between-group comparison across 26 samples.

How we read this: 13 replicated groups read from 26 sample titles; they account for 26 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 Colorectal 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.