GEO series

Methylomic and transcriptomic differences in treatment-naïve profiles among black and non-Hispanic white (NHW) women [RNA-Seq]

GSE240929 Homo sapiens (human) RNA-seq (gene expression) 66 samples Submitted 2025/01/01 Platform GPL20301

At a glance

RNA-seq (gene expression) · human · 66 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: Ovarian cancerThis series is part of our curated briefing on it.
Samples: 66 samples: 36 × B vs 30 × WGroups 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: Comparative transcriptomic, epigenomic and immunological analyses identify drivers of disparity in high-grade serous ovarian cancerNPJ genomic medicine · 2024

Summary (written by the submitters)

Epithelial ovarian cancer (EOC) is a highly lethal gynecological malignancy, with the high-grade serous ovarian cancer subtype (HGSOC) accounting for a significant proportion of cases. Despite platinum-based chemotherapies being the primary treatment for EOC, HGSOC displays high resistance to these therapies. Black women have the highest mortality/incidence ratio of all ethnic groups, and recent studies suggest significant genetic and epigenetic differences between black and non-Hispanic white women who are treatment naïve.

Read the full summary ↓

This study aims to explore immunogenic differences and hypermethylation phenotype in black women that may contribute to poorer outcomes within this patient group. We collected primary tumor samples from 36 black and 31 non-Hispanic white (NHW) treatment-naïve patients via biopsy and performed methylomic and transcriptomic analyses. Our analysis revealed significant differences in gene expression and methylation between black and NHW patients. Among the differentially expressed genes, we found significant pathway enrichment within p53/apoptosis signaling pathway and cholesterol/lipid modulation. Additionally, we observed mild hypermethylation in black patients vs. the NHW cohort. Furthermore, we discovered differences in the proportion of important immune cell types between the two groups, with CD8+ T-cells and CD4+ memory-resting T-cells being significantly higher proportionally in black patients. In conclusion, our study identified significant differential expression between black and NHW patient groups, as well as mild hypermethylation and differences in the proportion of important immune cell types. These findings provide further insights into the biological mechanisms underlying the disparities in outcomes between black and NHW patients with EOC. Further exploration of the contribution of these differences to clinical outcomes and treatment response in black women is warranted.

Published in

Comparative transcriptomic, epigenomic and immunological analyses identify drivers of disparity in high-grade serous ovarian cancer

Huang H, Keathley R, Kim U et al. · NPJ genomic medicine 2024 · PMID 39622796 · doi:10.1038/s41525-024-00448-2

This dataset

Open GSE240929 in finder → Find more human RNA-seq datasets → Ovarian 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 GSE240929_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 PRJNA1005901 and SRA study SRP455324. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39622796) ↗

Study design

36 × B vs 30 × W

Supports a between-group comparison across 66 samples.

How we read this: 2 replicated groups read from 66 sample titles; they account for 66 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

GSM7712284 B1 B
GSM7712285 B11 B
GSM7712286 B12 B
GSM7712287 B13 B
GSM7712288 B14 B
GSM7712289 B15 B
GSM7712290 B16 B
GSM7712291 B17 B
GSM7712292 B18 B
GSM7712293 B19 B
GSM7712294 B2 B
GSM7712295 B20 B
GSM7712296 B21 B
GSM7712297 B22 B
GSM7712298 B23 B
GSM7712299 B24 B
GSM7712300 B25 B
GSM7712301 B26 B
GSM7712302 B27 B
GSM7712303 B28 B
GSM7712304 B29 B
GSM7712305 B3 B
GSM7712306 B30 B
GSM7712307 B4 B
GSM7712308 B5 B
GSM7712309 B6 B
GSM7712310 B7 B
GSM7712311 B8 B
GSM7712312 B9 B
GSM7712313 W1 W
GSM7712314 W10 W
GSM7712315 W11 W
GSM7712316 W13 W
GSM7712317 W14 W
GSM7712318 W15 W
GSM7712319 W16 W
GSM7712320 W17 W
GSM7712321 W18 W
GSM7712322 W19 W
GSM7712323 W2 W

+ 26 more — browse all 66 samples with per-sample file links →

More Ovarian cancer datasets

GSE268514 Integrative genomic analysis of Korean high-grade serous ovarian cancer 161 samples same disease briefing
GSE311212 Transcriptomic profiling in human ovarian cancer cell line OVCAR-3 following CDK2 inhibitor treatment 6 samples same disease briefing
GSE154600 Multi-omic analysis of subtype evolution and heterogeneity in high-grade serous ovarian carcinoma 5 samples same disease briefing
GSE128098 TET1 reprograms the epithelial ovarian cancer epigenome and reveals casein kinase 2α as a therapeutic target 4 samples same disease briefing
GSE28330 Telomeric Allelic Imbalance Indicates Defective DNA Repair and Sensitivity to DNA-Damaging Agents 107 samples same disease briefing
GSE57477 Molecular subtyping of serous ovarian tumors reveals multiple connections to intrinsic breast cancer subtypes 72 samples same disease briefing
GSE314300 Rb CUT&RUN profiling in ovarian cancer cell lines following CDK2 inhibitor treatment 10 samples same disease briefing
GSE135242 Hi-C identifies structural variation associated with resistance in paired patient-derived ovarian cancer models 8 samples same disease briefing

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.