Gene expression signatures in breast cancer distinguish phenotype charact., histological subtypes, and tumor invasivness
Background. The development of reliable gene expression profiling technology is having an increasing impact on our understanding of breast cancer biology. Methods. In this study, microarray analysis was performed in order to establish gene signatures for different breast cancer phenotypes, determine differentially expressed gene sequences at different stages of the disease, and identify sequences with biological significance for tumor progression. Samples were taken from patients before their t...
Provenance — who produced it, who reused it
Linked to 25 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.
- Oncogenic long noncoding RNA landscape in breast cancer 2017 · 221 cites
- Actin stress fiber organization promotes cell stiffening and pro... 2017 · 182 cites
- Portraying breast cancers with long noncoding RNAs 2016 · 116 cites
- Highly interconnected genes in disease-specific networks are enr... 2012 · 81 cites
- Downregulation of the FTO m6A RNA demethylase promotes EMT-media... 2021 · 78 cites
20 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.