The Potential of PD-1 and PD-L1 as Prognostic and Predictive Biomarkers in Colorectal Adenocarcinoma Based on TILs Grading
Bibliographic record
Abstract
AIM: Colorectal cancer (CRC) is a prevalent malignancy with a high mortality rate. Tumor-infiltrating lymphocytes (TILs) play a crucial role in the immune response against tumors. Programmed death-1 (PD-1) and programmed death-ligand 1 (PD-L1) are key immune checkpoints regulating T cells in the tumor microenvironment. This study aimed to assess the relationships among PD-1 expression on TILs, PD-L1 expression in tumors, and TIL grading in colorectal adenocarcinoma. METHODS: A cross-sectional design was employed to analyze 130 colorectal adenocarcinoma samples. The expression of PD-1 and PD-L1 was assessed through immunohistochemistry. A semi-quantitative scoring system was applied. Statistical analysis with the chi-square test was performed to explore correlations, with the data analyzed in SPSS version 27. RESULTS: < 0.001). CONCLUSIONS: The expression of PD-1 on TILs and PD-L1 on tumor cells correlated significantly with the grading of TILs in colorectal adenocarcinoma. This finding shows potential as a predictive biomarker for PD-1/PD-L1 blockade therapy. Further studies are needed to strengthen these results.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".