Cancer-stromal cell interactions in breast cancer brain metastases induce glycocalyx-mediated resistance to HER2-targeting therapies [RNA-seq]
RNA-seq (gene expression) · human · 40 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
- 40 samples: 40 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
- Cancer-stromal cell interactions in breast cancer brain metastases induce glycocalyx-mediated resistance to HER2-targeting therapiesProceedings of the National Academy of Sciences of the United States of America · 2024
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE249573_RAW.tar (it can run to tens of gigabytes).
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 PRJNA1049737 and SRA study SRP476373. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 40 sample titles: 40 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.
- GSM7950276 453BM3_NER_DP_723 (RNA-seq)
- GSM7950277 453BM3_NER_DP_729 (RNA-seq)
- GSM7950278 453BM3_NER_mC_723 (RNA-seq)
- GSM7950279 453BM3_NER_mC_729 (RNA-seq)
- GSM7950280 BM3_DMSO_GFP_723 (RNA-seq)
- GSM7950281 BM3_DMSO_GFP_729 (RNA-seq)
- GSM7950282 BM3_NER_GFP_723 (RNA-seq)
- GSM7950283 BM3_NER_GFP_729 (RNA-seq)
- GSM7950284 453_DMSO_mC_723 (RNA-seq)
- GSM7950285 453_DMSO_mC_729 (RNA-seq)
- GSM7950286 453_NER_mC_723 (RNA-seq)
- GSM7950287 453_NER_mC_729 (RNA-seq)
- GSM7950288 453BM3_DMSO_DP_723 (RNA-seq)
- GSM7950289 453BM3_DMSO_DP_729 (RNA-seq)
- GSM7950290 453BM3_DMSO_mC_723 (RNA-seq)
- GSM7950291 453BM3_DMSO_mC_729 (RNA-seq)
- GSM7950292 453_DMSO_1 (RNA-seq)
- GSM7950293 453_DMSO_2 (RNA-seq)
- GSM7950294 453_NER_1 (RNA-seq)
- GSM7950295 453_NER_2 (RNA-seq)
- GSM7950296 453BM3_DMSO_1 (RNA-seq)
- GSM7950297 453BM3_DMSO_2 (RNA-seq)
- GSM7950298 453BM3_NER_1 (RNA-seq)
- GSM7950299 453BM3_NER_2 (RNA-seq)
- GSM7950300 monoBM3_DMSO_1 (RNA-seq)
- GSM7950301 monoBM3_DMSO_2 (RNA-seq)
- GSM7950302 monoBM3_NER_1 (RNA-seq)
- GSM7950303 monoBM3_NER_2 (RNA-seq)
- GSM7950304 monoHCC_DMSO_1 (RNA-seq)
- GSM7950305 monoHCC_DMSO_2 (RNA-seq)
- GSM7950306 monoHCC_NER_1 (RNA-seq)
- GSM7950307 monoHCC_NER_2 (RNA-seq)
- GSM7950308 coBM3_DMSO_1 (RNA-seq)
- GSM7950309 coBM3_DMSO_2 (RNA-seq)
- GSM7950310 coBM3_NER_1 (RNA-seq)
- GSM7950311 coBM3_NER_2 (RNA-seq)
- GSM7950312 coHCC_DMSO_1 (RNA-seq)
- GSM7950313 coHCC_DMSO_2 (RNA-seq)
- GSM7950314 coHCC_NER_1 (RNA-seq)
- GSM7950315 coHCC_NER_2 (RNA-seq)
- GSE249459 Cancer-stromal cell interactions in breast cancer brain metastases induce glycocalyx-mediated resistance to HER2-targeting therapies [ATAC-seq] 24 samples shares glycocalyx-mediated, cancer-stromal, her2-targeting, interactions, metastases, resistance, therapies
- GSE272874 IGF2BP1 phosphorylation regulates ribonucleoprotein condensate formation by impairing low-affinity protein and RNA interactions (RNA-Seq) 26 samples shares interactions, rna-seq
- GSE216549 Central role of SUMOylation in the regulation of chromatin interactions and transcriptional outputs of androgen receptor in prostate cancer cells (LNCaP RNA-seq) 24 samples shares interactions, rna-seq
- GSE274485 The Long Non-coding RNA Lnc-RAINY Regulates Genes Involved in Radiation Susceptibility Through DNA:DNA:RNA triplex-forming Interactions and has Tumor Therapeutic Potential in Non-small Cell Lung Cancers [RNA-seq] 24 samples shares interactions, rna-seq
- GSE235711 Immune-Epithelial Interactions and Tissue Remodeling in the Inflammatory Respiratory Nasal Tract [scRNA-seq] 20 samples shares interactions, rna-seq
- GSE293428 Mapping Endothelial-Macrophage Interactions in Diabetic Vasculature: Role of TREM2 in Vascular Inflammation and Ischemic Response [scRNA-Seq] 18 samples shares interactions, rna-seq
- GSE279180 Cell type mapping reveals tissue niches and interactions in subcortical multiple sclerosis lesions [snRNA-seq] 16 samples shares interactions, rna-seq
- GSE290659 MYC-FACT Interactions Drive Z-DNA Formation to Modulate Transcriptional Dynamics [RNA-seq CC & CL] 16 samples shares interactions, rna-seq
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.