Feedback control of the <scp>CXCR</scp>7/<scp>CXCL</scp>11 chemokine axis by estrogen receptor α in ovarian cancer
Bibliographic record
Abstract
Ovarian cancer (OC) is one of the most intractable diseases, exhibiting tremendous molecular heterogeneity and lacking reliable methods for screening, resulting in late diagnosis and widespread peritoneal dissemination. Menopausal estrogen replacement therapy is a well-recognized risk factor for OC, but little is known about how estrogen might contribute to this disease at the cellular level. This study identifies chemokine receptor CXCR7/ACKR3 as an estrogen-responsive gene, whose expression is markedly enhanced by estrogen through direct recruitment of ERα and transcriptional active histone modifications in OC cells. The gene encoding CXCR7 chemokine ligand I-TAC/CXCL11 was also upregulated by estrogen, resulting in Ser-118 phosphorylation, activation, and recruitment of estrogen receptor ERα at the CXCR7 promoter locus for positive feedback regulation. Both CXCR7 and CXCL11, but not CXCR3 (also recognized to interact with CXCL11), were found to be significantly increased in stromal sections of microdissected tumors and positively correlated in mesenchymal subtype of OC. Estrogenic induction of mesenchymal markers SNAI1, SNAI2, and CDH2 expression, with a consequent increase in cancer cell migration, was shown to depend on CXCR7, indicating a key role for CXCR7 in mediating estrogen upregulation of mesenchymal markers to induce invasion of OC cells. These findings identify a feed-forward mechanism that sustains activation of the CXCR7/CXCL11 axis under ERα control to induce the epithelial-mesenchymal transition pathway and metastatic behavior of OC cells. Such interplay underlies the complex gene profile heterogeneity of OC that promotes changes in tumor microenvironment and metastatic acquisition.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".