Feline sarcoma‐related protein expression correlates with malignant aggressiveness and poor prognosis in renal cell carcinoma
Bibliographic record
Abstract
Feline sarcoma-related protein (Fer) is a ubiquitously expressed non-receptor protein tyrosine kinase associated with proliferation in various cancer cells. However, no reports have described the pathological roles and prognostic value of Fer expression in renal cell carcinoma (RCC). We investigated Fer expression in three RCC cell lines (ACHN, Caki-1, and Caki-2) and in normal tubule cells (HK-2) by immunoblotting. Fer expression was highest in ACHN cells, with Caki-1 showing intermediate levels and Caki-2 showing low levels, and was undetectable in HK-2. RNA interference was therefore used to assess the effects of Fer knockdown in ACHN. Knockdown of Fer expression was found to inhibit RCC cell proliferation and colony formation. Immunohistochemical analysis of 131 human RCC tissues (110 conventional, 11 chromophobe, and 10 papillary) investigated relationships between Fer expression and clinicopathological features, including cancer cell proliferation, apoptosis, and prognostic value for survival. In human tissues, Fer expression was significantly higher in cancer cells than in normal tubules. In addition, expression levels correlated with cancer cell proliferation, but not with apoptosis. Multivariate analysis indicated associations of Fer expression with pT stage, tumor grade, and metastasis (P < 0.001). Fer expression was also prognostic for cause-specific survival according to multivariate analysis (hazard ratio, 3.89; 95% confidence interval, 1.02-14.84, P = 0.047). Fer expression correlates with RCC cell proliferation both in vitro and in vivo, and with tumor progression and survival. This represents useful information for discussing the pathological and clinical significance of Fer in RCC.
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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".