TOX3 Promotes Ovarian Estrogen Synthesis: An RNA-Sequencing and Network Study
Bibliographic record
Abstract
Background Women who undergo chronic exposure to excessive estrogen are at a high risk of developing breast cancer. TOX3 has been reported to be highly expressed in breast tumors and is closely related to estrogen receptors. However, the effect of TOX3 on estrogen synthesis remains poorly understood. Methods Using lentiviruses as a vector, we stably overexpressed TOX3 in the ovarian granulosa cell line KGN, the cells where estradiol is primarily produced, to investigate its role in estrogen production as well as cell viability and apoptosis. RNA-Sequencing was applied to uncover the global gene expression upon TOX3 overexpression. Results We observed an increased level of cell viability and a reduced cell apoptosis rate after TOX3 overexpression, and the level of estradiol in the cell culture supernatant also increased significantly. Gene set enrichment analysis of the transcriptome showed that the ovarian steroidogenesis pathway was significantly enriched. Similarly, pathway mapping using the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses also showed that TOX3 overexpression affects the ovarian steroidogenesis pathway. Further experiments showed that upregulated FSHR, CYP19A1, and BMP6 accounted for the enhanced estrogen synthesis. Conclusion Our study demonstrated that TOX3 quantitatively and qualitatively stimulates estrogen synthesis by enhancing estrogen signaling pathway–related gene expression in ovarian granulosa cells. These findings suggest that TOX3 may play a vital role in the pathogenesis of breast cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".