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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,100 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,008 | 0,014 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,007 | 0,007 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,005 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,800 | 0,256 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».