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Molecular determinants of neoadjuvant chemotherapy resistance in breast cancer: An analysis of gene expression and tumor microenvironment

GSE309004 Homo sapiens (human) RNA-seq (gene expression) 54 samples Submitted 2025/12/02 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 54 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
Breast cancerThis series is part of our curated briefing on it.
Samples
54 samples: 53 distinct titles, 1 of them repeatedGroups 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
Molecular determinants of neoadjuvant chemotherapy resistance in breast cancer: An analysis of gene expression and tumor microenvironmentPloS one · 2025
Summary (written by the submitters)
Neoadjuvant chemotherapy (NAC) is a critical component of breast cancer treatment, but the molecular mechanisms underlying resistance remain poorly understood. This study aimed to identify transcriptomic changes associated with NAC resistance across four breast cancer subtypes: Luminal A, Luminal B/HER2-positive, Luminal B/HER2-negative, and Triple-Negative Breast Cancer (TNBC).
Read the full summary ↓
RNA-seq analysis was performed on paired pre- and post-NAC breast cancer samples from 35 non-responders. Differentially expressed genes (DEGs) were identified, and functional enrichment analyses were conducted. Protein-protein interaction (PPI) networks were constructed to identify hub genes. Tumor microenvironment (TME) infiltration was estimated using deconvolution algorithms. The results revealed distinct gene expression profiles between pre- and post-NAC samples, with FOS and NR4A1 being common DEGs across all subtypes. Enriched pathways varied among subtypes, including signal transduction, estrogen biosynthesis, extracellular matrix organization, dendritic cell activation, and B cell activation. TME analysis showed increased infiltration of specific immune cell populations after NAC, including CD4 memory T cells, regulatory T cells, neutrophils, macrophages, and mast cells, varying by subtype. These findings suggest that NAC modulates gene expression, cellular activity, and TME interactions, potentially contributing to treatment resistance. Understanding the molecular determinants of NAC resistance is crucial for developing targeted therapeutic strategies and improving outcomes for breast cancer patients.
Published in
Molecular determinants of neoadjuvant chemotherapy resistance in breast cancer: An analysis of gene expression and tumor microenvironment
Guevara-Nieto HM, Orozco-Castaño CA, Parra-Medina R et al. · PloS one 2025 · PMID 41086192 · doi:10.1371/journal.pone.0334335
This dataset
Open GSE309004 in finder → Find more human RNA-seq datasets → Breast 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 GSE309004_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 PRJNA1333701 and SRA study SRP626256. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41086192) ↗
Study design
53 distinct titles, 1 of them repeated
B ×2

How we read this: Read from 54 sample titles: 53 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

+ 14 more — browse all 54 samples with per-sample file links →

More Breast 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.