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Integrative genomic analysis of Korean high-grade serous ovarian cancer

GSE268514 Homo sapiens (human) RNA-seq (gene expression) 161 samples Submitted 2024/06/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 161 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
161 samples: 10 conditions, mostly in triplicateGroups 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)
Ovarian cancer is one of the most common gynecologic malignancies globally, which is a significant cause of morbidity and mortality, accounting for 207,252 new deaths each year. The 5-year survival rate of this cancer is reported to be 26-42% and is often diagnosed in an advanced stage. Epithelial ovarian cancer (EOC) is a heterogeneous disease that comprises various histological subtypes.
Read the full summary ↓
Of them, high-grade serous ovarian cancer (HGSOC) is the most common cancer subtype (70-80%), which is highly aggressive and grows rapidly. Despite the initial success observed with cytoreductive surgery and platinum-based chemotherapy, over 75% of HGSOC patients experience relapse following completion of first-line therapy. The challenge of tailoring therapeutic interventions to control progressive disease is compounded by the inherent cellular heterogeneity and genomic instability characteristic of HGSOC. While platinum chemotherapy remains the cornerstone of contemporary treatment, the grim reality persists that a majority of EOC patients develop chemotherapy resistance, resulting in a five-year survival rate of less than 50%. Numerous ovarian cancer studies have underscored the prognostic significance of factors such as age at diagnosis, disease stage, grade, histology, residual disease post-surgery, and disease recurrence. The genetic predisposition towards EOC indicates that high-grade HGSC exhibits significant chromosomal instability and carries mutations in the tumor protein 53 (TP53) gene, as well as mutations in the breast cancer genes BRCA1/2. Molecular markers like BRCA1/2 mutations and homologous repair deficiency in HGSOC have been validated as predictive of response to platinum therapy and poly-ADP polymerase (PARP) inhibitors. Furthermore, recent research has shed light on the prognostic potential of immune cell populations infiltrating ovarian tumor tissue. The Cancer Genome Atlas (TCGA) and other genomic studies of HGSOC have conducted extensive genomic and transcriptomic analyses of HGSOC to delineate its genomic landscape and facilitate the development of targeted therapies for this highly lethal malignancy. Key findings from previous studies include the prevalent mutation of TP53 genes, frequent and widespread DNA copy number alterations, identification of transcriptional signatures associated with clinical outcome and molecular subtype, and diverse mechanisms of BRCA1/2 inactivation. However, most studies have focused on HGSOC of white populations, and there is limited comprehensive molecular characterization of East Asian HGSOC, including those from Korea. In this study, we aimed to discern clinical and molecular factors distinguishing 123 HGSOC patients from the Korean population through an integrated analysis of clinical features, germline variants, somatic genomic alterations, and the tumor immune microenvironment.
This dataset
Open GSE268514 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 GSE268514_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 PRJNA1113725 and SRA study SRP508844. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
10 conditions, mostly in triplicate
NUEV ×65 NUFS ×26 NULG ×22 NUFM ×17 NUF ×7 NUHF ×7 NUHU ×4 NTAC ×3 +6 more

Supports a between-group comparison across 157 samples.

How we read this: 10 replicated groups read from 161 sample titles; they account for 157 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

+ 121 more — browse all 161 samples with per-sample file links →

More Ovarian 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.