Metabolomics and Transcriptional Responses in Estrogen Receptor Positive Breast Cancer Cells
Bibliographic record
Abstract
Estrogen exposure is well recognized as a high risk factor of breast cancer, despite the fact that the hormone transcriptionally regulates the expression of tumor suppressor genes, like BRCA1 (Hockings et al., 2008). Estrogen acts through the estrogen receptor ┙ (ER┙) in target cells (Ali and Coombes, 2000). ER┙ overexpression occurs in about 70% of breast cancers, referred as “ER positive” (ER+) (Dickson and Lippman, 1988). ER┙ is a primary target for chemoprevention as well as other therapeutic interventions for breast cancer with the ER+ phenotype. Clinically, in pathological lesions such as women with benign breast tumors, who underwent surgery, presented with a high proportion of ER positivity than the control group. In such cases, logistic regression yielded an adjusted odds ratio of 6.5 for risk of development of breast cancer compared to 0.3 odds ratio for PR (progesterone receptor); there was an increased proportion of cells expressing ER┙ by immunohistochemical staining, thereby demonstrating the importance of ER┙ as a breast cancer risk marker (Khan et al., 1994). Estrogen or E2 mostly exerts its mitogenic effects by the modulation of metabolic responses and through the transcriptional induction of genes regulating crucial cellular processes like the cell cycle (Prall et al., 1998). Microarray and RNA interference studies suggest that the transcriptional regulation of about 60% of estrogen responsive genes (induced or repressed) is dependent upon ER┙ and Sp1/3 transcription factors (Bazley and Gullick, 2005). The basal expression of ER┙, in turn, is regulated by Sp1 in ER+ breast cancer cells (deGraffenried et al., 2002). Sp1 and Sp3 are ubiquitously expressed in mammalian cells and are abnormally expressed in various cancers, including breast (Li and Davie, 2010). Sp1 and Sp3 bind to the same DNA sequence defined as Sp1/3 sites, with similar affinity. Sp1 and Sp3 play important roles in regulating genes critical to the initiation and progression of breast cancers (Hirokawa, 1984; Lu and Archer, 2010). Targeting these proteins is a promising cancer therapeutic approach (Jia et al., 2010). In most cases, ER+ breast cancers present a better clinical prognosis than the ERbreast cancers. Moreover, it is an encouraging fact that the leading antiestrogenic drugs such as tamoxifen have effectively improved the overall survival of pre and postmenopausal women by the reduction of cancer incidence and formation of new tumors (Ferguson and Davidson, 1997; Muss, 2001). However, a major obstacle to breast cancer treatment is the development of drug resistance. Resistance is commonly associated with an increased expression of Erb
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".