FGFR4 Polymorphic Variants Modulate Phenotypic Features of Cushing Disease
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Cushing disease is a potentially lethal condition resulting from hormone excess, usually due to a small pituitary tumor that fails to respond to negative feedback inhibition. A minority of patients develop larger, more aggressive tumors of the same lineage but with modest hormone excess. Here we show that a common polymorphism in the fibroblast growth factor receptor 4 (FGFR4) transmembrane domain yields receptor isoforms with distinct properties that mediate these biological differences. Forced expression of the major FGFR4-G388 variant allele supports pY-signal transducer and activator of transcription (STAT3) responses. In contrast, expression of the minor FGFR4-R388 allele enhances STAT3 serine phosphorylation, driving cellular growth. In addition, FGFR4-R388 enhances glucocorticoid receptor phosphorylation and nuclear translocation. Consistent with these findings, glucocorticoid administration resulted in enhanced hormone negative feedback in mice with knock-in of the FGFR4 variant allele. Moreover, clinical data from patients with pituitary tumors revealed that those homozygous for the R388 allele have a higher frequency of silent corticotroph macroadenomas than FGFR4-G388 carriers, who were more likely to have small but hormonally active microadenomas. These findings demonstrate that the FGFR4 transmembrane polymorphic variants can modulate cellular growth and sensitivity to glucocorticoid hormone negative feedback through distinct STAT3 modifications of relevance to the human forms of Cushing disease.
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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".