Metastasis Gene Expression Profile Predicts Recurrence and Death in Colon Cancer Patients (VMC Samples)
Background and Aims: Staging inadequately predicts metastatic risk in colon cancer patients. We used a gene expression profile derived from invasive murine colon cancer cells that were highly metastatic in an immunocompetent mouse model to identify colon cancer patients at risk for recurrence in a phase I, exploratory biomarker study. Methods: 55 colorectal cancer patients from Vanderbilt Medical Center (VMC) were used as the training dataset and 177 patients from the Moffitt Cancer Center we...
Provenance — who produced it, who reused it
Linked to 102 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.
- Machine learning-based integration develops an immune-derived ln... 2022 · 914 cites
- Subtypes of primary colorectal tumors correlate with response to... 2012 · 257 cites
- The long non-coding RNA CYTOR drives colorectal cancer progressi... 2018 · 162 cites
- TAZ Expression as a Prognostic Indicator in Colorectal Cancer 2013 · 157 cites
- Molecular implications of MUC5AC-CD44 axis in colorectal cancer... 2020 · 157 cites
- OMA1 reprograms metabolism under hypoxia to promote colorectal c... 2020 · 139 cites
- Nuclear Factor of Activated T-cell Activity Is Associated with M... 2014 · 129 cites
- Molecular subtype identification and prognosis stratification by... 2021 · 97 cites
- Extracellular vesicle release from intestinal organoids is modul... 2019 · 97 cites
- A gene expression profile of stem cell pluripotentiality and dif... 2012 · 89 cites
- An integrative analysis reveals functional targets of GATA6 tran... 2013 · 81 cites
- LATS2 Suppresses Oncogenic Wnt Signaling by Disrupting β-Catenin... 2013 · 73 cites
68 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.