A Gene Signature Predicting for Survival in Suboptimally Debulked Patients with Ovarian Cancer
To identify a prognostic gene signature accounting for the distinct clinical outcomes in ovarian cancer patients Despite the existence of morphologically indistinguishable disease, patients with advanced ovarian tumors display a broad range of survival end points. We hypothesize that gene expression profiling can identify a prognostic signature accounting for these distinct clinical outcomes. To resolve survival-associated loci, gene expression profiling was completed for an extensive set of 185...
Provenance — who produced it, who reused it
Linked to 122 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.
- Functional genomics identifies five distinct molecular subtypes... 2013 · 291 cites
- curatedOvarianData: clinically annotated data for the ovarian ca... 2013 · 213 cites
- High-grade serous tubo-ovarian cancer refined with single-cell R... 2021 · 195 cites
- Network-based Survival Analysis Reveals Subnetwork Signatures fo... 2013 · 188 cites
- A network module-based method for identifying cancer prognostic... 2012 · 165 cites
- Angiogenic mRNA and microRNA Gene Expression Signature Predicts... 2012 · 150 cites
- lncRNA-Xist/miR-101-3p/KLF6/C/EBPα axis promotes TAM polarizatio... 2020 · 128 cites
- Conservation of immune gene signatures in solid tumors and progn... 2016 · 126 cites
- EZH2 activates CHK1 signaling to promote ovarian cancer chemores... 2020 · 100 cites
- sigFeature: Novel Significant Feature Selection Method for Class... 2020 · 92 cites
- WNT7A/β-catenin signaling induces FGF1 and influences sensitivit... 2014 · 91 cites
- Deep learning-based ovarian cancer subtypes identification using... 2020 · 88 cites
- EZH2-mediated epigenetic silencing of TIMP2 promotes ovarian can... 2017 · 88 cites
- ACTL6A promotes repair of cisplatin-induced DNA damage, a new me... 2021 · 79 cites
66 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.