Expression data from human colonic biopsy samples (adenoma-carcinoma)
Whole genomic microarray analysis was performed in order to identify gene expression profile alterations focusing on the dysplastic adenoma-carcinoma transition. Our aims were to determinate characteristic transcript sets for developing diagnostic mRNA expression patterns for objective classification of benign and malignant colorectal diseases and to test the classificatory power of these markers on an independent sample set.
Provenance — who produced it, who reused it
Linked to 42 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.
- N6-methyladenosine-induced circ1662 promotes metastasis of color... 2021 · 165 cites
- The NLRP1 Inflammasome Attenuates Colitis and Colitis-Associated... 2015 · 163 cites
- Micropeptide ASAP encoded by LINC00467 promotes colorectal cance... 2021 · 148 cites
- Comprehensive investigation of a novel differentially expressed... 2017 · 100 cites
- High Expression of lncRNA AFAP1-AS1 Promotes the Progression of... 2018 · 92 cites
- Aberrant DNA methylation of WNT pathway genes in the development... 2016 · 81 cites
- Aging related methylation influences the gene expression of key... 2016 · 78 cites
35 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.