Gene-expression molecular subtyping of triple-negative breast cancer tumours: importance of immune response
Triple-negative (TN) breast cancers need to be refined in order to identify therapeutic subgroups of patients. We conducted an unsupervised analysis of microarray gene-expression profiles of 107 TN breast cancer patients and undertook robust functional annotation of the molecular entities found by means of numerous approaches including immunohistochemistry and gene-expression signatures. An 87 TN external cohort was used for validation. Fuzzy clustering separated TN tumours into three clusters:...
Provenance — who produced it, who reused it
Linked to 81 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.
- Survival analysis across the entire transcriptome identifies bio... 2021 · 1,740 cites
- Targeting lysyl oxidase (LOX) overcomes chemotherapy resistance... 2020 · 363 cites
- Opposing Functions of BRD4 Isoforms in Breast Cancer 2020 · 207 cites
- Prognostic Significance and Tumor Immune Microenvironment Hetero... 2021 · 121 cites
- Identification and validation of a combined hypoxia and immune i... 2020 · 90 cites
- Identification of a five‐lncRNA signature for predicting the ris... 2018 · 77 cites
- FADS1/2 control lipid metabolism and ferroptosis susceptibility... 2024 · 77 cites
72 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.