Overexpression of fibroblast growth factor receptors FGFR1 and FGFR2 in renal cell carcinoma
Bibliographic record
Abstract
OBJECTIVE: Fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) has recently emerged as a critical event in the transformation and tumorigenicity of several murine and human tumors. This pathway could be a mechanism driving angiogenesis in patients with metastatic renal cell carcinoma (RCC). Membrane antigens such as FGFR expressed in RCC are attractive targets for new therapeutic and diagnostic applications. This study evaluated the expression of FGFR1 and FGFR2 in RCC. MATERIAL AND METHODS: Formalin-fixed, paraffin-embedded specimens of 100 primary tumors and 40 metastatic lymph nodes removed from 140 untreated RCC patients were evaluated by immunohistochemistry with FGFR1 and FGFR2 antibodies. The extent of FGFR expression was compared with 40 specimens of normal human kidney tissue (selected from the surgical diagnostic files). Significant differences in the immunoexpression of FGFR among these groups were assessed bychi-squared and Fisher's exact tests using a semi-quantitative scoring system on the extent of stained cells and intensity of corresponding immunostained cells (0 to 3+). RESULTS: Expression of FGFR1 was observed in 98% (98/100) of primary renal tumors and in 82.5% (33/40) of lymph-node metastases. Intensity was 3+ in allcases. Nuclear expression of FGFR1 was found in 68% (95/140). FGFR2 staining was seen in 4% (4/100) of primary tumors and in 5% (2/40) of lymph-node metastases. FGFR2 was expressed in RCC of non-clear cell histology. FGFR1 expression was significantly lower in the normal kidney tissue(p = 0.001) and was detected in 2.5% of cases (1/40); no FGFR2 expression was found. CONCLUSION: This study has shown for the first time that FGFR1 is highly expressed in RCC patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".