Immune-activation as a therapeutic direction for patients with high-risk ovarian cancer based on gene expression signature (1)
The Japanese Serous Ovarian Cancer Study Group Advanced-stage ovarian cancer is one of the most lethal gynecologic malignancies. To improve prognosis of patients with ovarian cancers, a predictive biomarkers leading to personalized treatments are required. In this large-scale cross-platform study of six microarray datasets consisting of 1054 ovarian cancer patients, we developed a novel risk classification system based on a 126-gene expression signature for predicting overall survival by applyin...
Provenance — who produced it, who reused it
Linked to 34 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
- PROGgeneV2: enhancements on the existing database 2014 · 384 cites
- curatedOvarianData: clinically annotated data for the ovarian ca... 2013 · 213 cites
- Deep learning-based ovarian cancer subtypes identification using... 2020 · 88 cites
31 further papers cite this accession but reuse could not be confirmed.
Deep data QC
metadata only · no data-level QC for this typeStandardized, field-standard QC computed by touching the data — every metric states how it was obtained
No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently
Scientific quality
Based on hands-on reproduction of the papers that use this dataset. A reproducible paper that stands on this data is positive evidence; a flagged one is a prompt to look closer — never a verdict on the dataset itself without the evidence.