Correlation Between Tumor-Associated Collagen Signature and Fibroblast Activation Protein Expression With Prognosis of Clear Cell Renal Cell Carcinoma Patient
Bibliographic record
Abstract
Background: Despite recent promising findings from immunotherapy and other targeted medicines, individuals with metastatic clear cell renal cell carcinoma (mCCRCC) still have a poor prognosis. Biomarkers associated with metastatic status in CCRCC are important for early detection and for the identification of new therapeutic targets. The expression of fibroblast activation protein (FAP) is associated with the development of early metastases and worse cancer-specific survival. Tumor-associated collagen signature (TACS) is a type of collagen that develops during tumor growth and is associated with tumor invasion. Methods: Twenty-six mCCRCC patients that underwent nephrectomy were admitted to this study. Data regarding age, sex, Fuhrman's grade, tumor diameter, staging, FAP expression, and TACS grading were collected. Spearman rho test was used to correlate FAP expression and TACS grading in both primary tumors and metastases and with the patient's age and sex. Results: FAP manifestation correlated positively with TACS degree (Spearman rho test r = 0.51; P = 0.0001). FAP was positive in 25 (96%) of all intratumor samples and positive in 22 (84%) of all stromal samples. Conclusions: FAP can be used as a prognostic factor in mCCRCC; its presence can predict the aggressiveness of mCRCC and poorer outcome in the patient. Furthermore, TACS can also be used for the prediction of aggressiveness and metastasis due to the changes necessary for a tumor to invade other organs.
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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) | 0.002 | 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".