Prognostic Relevance of PDL1 and CA19-9 Expression in Gallbladder Cancer vs. Inflammatory Lesions
Bibliographic record
Abstract
Chronic inflammation in the gallbladder leading to persistent epithelium damage promotes invasive cancer. The study aimed to assess the prognostic value of PDL1 and CA19-9 markers in cancer/inflammatory lesions of the gallbladder. A total of 29 cases (19 cancer and 10 inflammatory) were included. The PDL1 protein concentration level and mRNA expression were assessed in the tissues’ lysates by ELISA and real-time PCR, respectively. PDL1 and CA19-9 concentration levels were compared and statistically related with clinico-pathological variables. The PDL1 protein level and its relative mRNA expression were correlated. Kaplan–Meir survival and Cox regression analyses were conducted for predicting prognosis. This study investigated the PDL1 and CA19-9 marker expression in both cancer and inflammatory cases of the gallbladder (p = 0.48 and p = 0.17 respectively). PDL1 protein expression was significantly associated with the hormonal profile of the cases (p = 0.04) at an optimum cut-off value of 13 pg/mL, while the CA19-9 marker expression was correlated with the status of liver metastasis (p = 0.0043) and size of the tumor (p = 0.004). A low PDL1 concentration was found when compared to the CA19-9 level among cancer cases (p = 0.12) and proportional in the inflammatory lesions (p = 0.63). A significant positive correlation was found between the PDL1 protein and its relative mRNA expressions in the inflammatory lesions (p = 0.029) when compared to cancer cases (p = 0.069). Our results showed that a protein-based assay for PDL1 expression would be more sensitive compared to RNA based assays for GBC risk stratifications. Overall survival was predicted with CA19-9 and PDL1 levels (p = 0.0074, p = 0.23, respectively). PDL1 and CA19-9 may act as a probable predictor of a poor prognosis in gallbladder cancer (GBC) cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".