Cancer-associated fibroblasts and their prognostic role in colorectal cancer: review and meta-analysis
Bibliographic record
Abstract
Background: Cancer-associated fibroblasts (CAFs) are a component of the tumor microenvironment, influencing tumor behavior and progression. This meta-analysis aims to describe the prognostic value of CAF biomarkers in colorectal cancer (CRC). Methods: A systematic search of the existing literature was performed in Medline and Embase on CAF immunohistochemical (IHC) biomarkers and their association with disease-free survival (DFS) and overall survival (OS). The selection of studies was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and Population, Intervention, Comparator, Outcome criteria. The quality of the included articles was assessed using the Newcastle-Ottawa Scale. Results: = 15,396 patients) were included in the final meta-analysis. In general, CAF biomarker expression was associated with poor prognosis, with high heterogeneity among studies. Significant results for DFS were found for cluster of differentiation (CD) 163, matrix metalloproteinase 9 (MMP-9), and tenascin C. The following markers were significantly associated with OS: CD163, MMP-9, periostin, and vimentin. Conclusion: Several CAF IHC biomarkers, particularly CD163, MMP-9, periostin, and vimentin, significantly associated with prognosis in CRC, could be proposed as surrogates for the phenotypical characterization of the tumor microenvironment.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.026 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".