Genomic data on breast cancer transcript profile modulation by 17beta-hydroxysteroid dehydrogenase type 1 and 17-beta-estradiol
Bibliographic record
Abstract
The data presented here are related to the research article entitled “Estradiol-independent modulation of breast cancer transcript profile by 17beta-hydroxysteroid dehydrogenase type 1” (J.A. Aka, E.L. Calvo, S.X. Lin, 2016) [1]. We evaluated the effect of the steroidal enzyme 17β-HSD1 and its product, the estrogenic hormone 17-beta-estradiol (E2), on gene transcription profile of breast cancer cells. RNA interference technique was used to knock down the 17β-HSD1 gene (HSD17B1) in the hormone-dependent breast cancer cell line T47D in steroid-deprived medium. Transfected cells were subsequently treated with E2, and microarray analyses (with three contrasts) were used to investigate (i) the effect of 17β-HSD1 expression on breast cancer cell transcript profile in steroid-deprived condition, (ii) the effect of E2 on breast cancer gene expression and (iii) if E2 affects gene regulation by 17β-HSD1. Functional enrichments of the differentially expressed genes were assessed using Ingenuity Pathway Analysis (IPA). Here, we showed data on 140 genes that are induced or repressed 1.5 time or higher (p < 0.05) in the HSD17B1-silenced and E2-treated T47D cells revealed by microarray analysis, and presented the 14 functional terms found in the cancer and in the cell death and survival categories revealed by the IPA biological function analysis. Data on IPA Canonical Pathway and network analyses is also presented. Further discussion on gene regulation by 17β-HSD1 and E2 is provided in the accompanying publication [1].
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".