Peritoneal tumor spread in serous ovarian cancer-epithelial mesenchymal status and outcome
RNA-seq (gene expression) · human · 42 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
- 42 samples: 42 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
- Peritoneal tumor spread in serous ovarian cancer-epithelial mesenchymal status and outcomeOncotarget · 2015
Read the full summary ↓
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 GSE293643_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 PRJNA1245400 and SRA study SRP576151. 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 42 sample titles: 42 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.
- GSM8887609 A13 RNA-seq
- GSM8887610 S13 RNA-seq
- GSM8887611 A16 RNA-seq
- GSM8887612 S16 RNA-seq
- GSM8887613 A24 RNA-seq
- GSM8887614 S24 RNA-seq
- GSM8887615 A28 RNA-seq
- GSM8887616 S28 RNA-seq
- GSM8887617 A41 RNA-seq
- GSM8887618 S41 RNA-seq
- GSM8887619 A42 RNA-seq
- GSM8887620 S42 RNA-seq
- GSM8887621 A54 RNA-seq
- GSM8887622 S54 RNA-seq
- GSM8887623 A21 RNA-seq
- GSM8887624 A25 RNA-seq
- GSM8887625 M21 RNA-seq
- GSM8887626 M25 RNA-seq
- GSM8887627 S21 RNA-seq
- GSM8887628 S25 RNA-seq
- GSM8887629 M16 RNA-seq
- GSM8887630 M53 RNA-seq
- GSM8887631 P12 RNA-seq
- GSM8887632 P42 RNA-seq
- GSM8887633 P53 RNA-seq
- GSM8887634 M28 RNA-seq
- GSM8887635 M35 RNA-seq
- GSM8887636 M39 RNA-seq
- GSM8887637 M54 RNA-seq
- GSM8887638 P27 RNA-seq
- GSM8887639 P41 RNA-seq
- GSM8887640 S62 RNA-seq
- GSM8887641 A6 RNA-seq
- GSM8887642 A8 RNA-seq
- GSM8887643 A9 RNA-seq
- GSM8887644 M58 RNA-seq
- GSM8887645 P55 RNA-seq
- GSM8887646 P58 RNA-seq
- GSM8887647 P8 RNA-seq
- GSM8887648 P16 RNA-seq
+ 2 more — browse all 42 samples with per-sample file links →
- GSE268514 Integrative genomic analysis of Korean high-grade serous ovarian cancer 161 samples same disease briefing · shares ovarian, serous
- GSE154600 Multi-omic analysis of subtype evolution and heterogeneity in high-grade serous ovarian carcinoma 5 samples same disease briefing · shares ovarian, serous
- GSE57477 Molecular subtyping of serous ovarian tumors reveals multiple connections to intrinsic breast cancer subtypes 72 samples same disease briefing · shares ovarian, serous
- GSE311212 Transcriptomic profiling in human ovarian cancer cell line OVCAR-3 following CDK2 inhibitor treatment 6 samples same disease briefing · shares ovarian
- GSE128098 TET1 reprograms the epithelial ovarian cancer epigenome and reveals casein kinase 2α as a therapeutic target 4 samples same disease briefing · shares ovarian
- GSE314300 Rb CUT&RUN profiling in ovarian cancer cell lines following CDK2 inhibitor treatment 10 samples same disease briefing · shares ovarian
- GSE135242 Hi-C identifies structural variation associated with resistance in paired patient-derived ovarian cancer models 8 samples same disease briefing · shares ovarian
- GSE28330 Telomeric Allelic Imbalance Indicates Defective DNA Repair and Sensitivity to DNA-Damaging Agents 107 samples same disease briefing
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.