Presença dos biomarcadores c-MET e ABCB5 no adenocarcinoma de próstata e sua associação com fatores prognósticos
Bibliographic record
Abstract
Introduction: Prostate adenocarcinoma is one of the most prevalent malignancies worldwide. Despite often having a slow evolution, it still represents an important cause of death in oncology. Since its clinical results are very heterogeneous, there is a need to identify prognostic markers. Biomarkers are tools and technologies that can aid in this understanding. Objective: To evaluate the presence of c-MET and ABCB5 in prostate adenocarcinoma and to analyze whether their expression correlates with prognostic factors. Method: 170 cases were selected through an active search in electronic medical records and review of physical records and anatomopathological reports. Immunohistochemistry was performed for the biomarkers c-MET and ABCB5 in all samples. Retrospective clinical data were collected and plotted in tables. Through TMA (tissue microarray) the tissues were submitted to immunohistochemistry by the peroxidase technique. Clinical and epidemiological information was cross-referenced with the result obtained by immunostaining and its statistical analysis. Result: Forty-seven men with a mean age of 61.6±6.9 (48-75), with a mean PSA of 11±9.7 (2.4-60.5) were included in this study. 2% had a Gleason score of 5 or 6 and 29, 1%, 7 or 8. Regarding tumor classification, 6 cases had T3a and 2 T3b staging. None had a tumor greater than T3b and there was 1 case of metastasis. With regard to biomarkers, there was positive labeling of c-MET in 32 cases and of ABCB5 in 5. Positive labeling had no statistically significant association with any of the prognostic factors evaluated. Conclusion: There was expression of c-MET and ABCB5 in the prostate cancer, but without association of them with prognostic factors.
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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 | 0.002 |
| 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 | 0.000 |
| 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".