Meta-analysis of human cancer microarrays reveals GATA3 is integral to the estrogen receptor alpha pathway
Bibliographic record
Abstract
BACKGROUND: The transcription factor GATA3 has recently been shown to be necessary for mammary gland morphogenesis and luminal cell differentiation. There is also an increasing body of data linking GATA3 to the estrogen receptor alpha (ERalpha) pathway. Among these it was shown that GATA3 associates with the promoter of the ERalpha gene and ERalpha can reciprocally associate with the GATA3 gene. GATA3 has also been directly implicated in a differentiated phenotype in mouse models of mammary tumourigenesis. The purpose of our study was to compare coexpressed genes, by meta-analysis, of GATA3 and relate these to a similar analysis for ERalpha to determine the depth of overlap. RESULTS: We have used a newly described method of meta-analysis of multiple cancer studies within the Oncomine database, focusing here predominantly upon breast cancer studies. We demonstrate that ERalpha and GATA3 reciprocally have the highest overlap with one another. Furthermore, we show that when both coexpression meta-analysis lists for ERalpha and GATA3 are compared there is a significant overlap between both and, like ERalpha, GATA3 coexpresses with ERalpha pathway partners such as pS2 (TFF1), TFF3, FOXA1, BCL2, ERBB4, XBP1, NRIP1, IL6ST, keratin 18(KRT18) and cyclin D1 (CCND1). Moreover, as these data are derived from human tumour samples this adds credence to previous cell-culture or murine based studies. CONCLUSION: GATA3 is hypothesized to be integral to the ERalpha pathway given the following: (1) The large overlap of coexpressed genes as seen by meta-analysis, between GATA3 and ERalpha, (2) The highest coexpressing gene for GATA3 was ERalpha and vice-versa, (3) GATA3, like ERalpha, coexpresses with many well-known ERalpha pathway partners such as pS2.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".