Metadata supporting data files in the published article: Changes in expression of hormone-regulated and proliferation-associated genes across the menstrual cycle in oestrogen receptor-positive breast cancer
Bibliographic record
Abstract
The study investigated whether there are consistent differences in the expression of oestrogen-regulated genes and proliferation-associated genes in premenopausal oestrogen receptor-positive (ER+) breast cancer as a result of the major changes in hormone levels that occur through the menstrual cycle. Data access: Datasets Hormone_concentrations.xls, demographic_data.xls and Protein_W1vW23.xls are publicly available in the figshare repository as part of this data record (https://doi.org/10.6084/m9.figshare.9892211). All the other datasets supporting the findings of this study are available in the supplementary files of the published article. Study approval: The study was approved by the Institutional Ethics Committee of the National Cancer Hospital, Hanoi, Vietnam from where all study participants were recruited and by the Research Ethics Board of the University of Toronto, Canada, from where the study was coordinated. The Committee for Clinical Research at the Royal Marsden Hospital, London approved the analysis of the samples collected in this trial. All participants provided written informed consent. Study aims and methodology: The study aimed to determine if there are changes in the expression of oestrogen- and progesterone-regulated genes (ERGs and PRGs) and proliferation-associated genes (PAGs) in premenopausal ER+ breast cancer as a result of the major changes in hormone levels that occur through the menstrual cycle. Biopsy samples from 96 patients in two independent prospective studies of the effect of menstrual cycle on ER+ breast cancer were used. Plasma hormone measurements were used to assign tumours to one of three pre-defined menstrual cycle windows: W1 (days 27-35 and 1-6; low oestradiol and low progesterone), W2 (days 7-16; high oestradiol and low progesterone) and W3 (days 17-26; intermediate oestradiol and high progesterone). RNA expression of 50 genes, including 27 ERGs, 11 putative PRGs and seven PAGs was measured using the NanoString nCounter gene expression system (GEN2). Dataset description: Data supporting figure 1: Dataset Hormone_concentrations.xls is in .xls file format and consists of serum hormone concentrations during the menstrual cycle. Data supporting figures 2 and 6: Nanostring raw gene expression data and window of cycle, and housekeeper normalized log-transformed gene expression data for all samples from Supplementary tables 2 and 6 respectively, were used to derive figures 2 and 6. Data supporting figures 3, 4 and 5: Supplementary tables 3 and 6 were used to derive figures 3, 4 and 5 Data supporting supplementary figures 3, 6, 7, and supplementary tables 3, 4, 5, 6 and 7: Supplementary tables 2 and 6 were used to derive these supplementary figures and tables. Data supporting supplementary figure 2: Supplementary table 2 was used to derive supplementary figure 2. Data supporting supplementary figure 4: Supplementary tables 3 and 6 were used to derive supplementary figure 4. Data supporting supplementary table 1: Dataset demographic_data.xls is in .xls file format and supports supplementary table 1 of the published article. Data supporting supplementary figure 5: Dataset Protein_W1vW23.xls is in .xls file format and supports supplementary figure 5 of the published article. The dataset consists of the protein levels of ER, progesterone receptor (PgR)and Ki67 between menstrual cycle Window 1 vs. Window 2 or 3.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.800 | 0.256 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".