High Expression of Human Leukocyte Antigen-G is Associated with a Poor Prognosis in Patients with PDAC
Bibliographic record
Abstract
Pancreatic Adenocarcinoma (PDAC) is one of the most deadly malignant tumors worldwide. A variety of mechanisms are involved in PDAC biological behaviors, of which, the mechanisms of immune escape may be a pivotal hallmark. HLA-G is a tolerant molecule implicated in tumor escape and serves as a prognostic biomarker in tumors. Our study evaluated the expression of HLA-G in PDAC and explored its clinical significance. In a cohort of 122 PDAC patients, 78 patents (63.9%) exhibited high level of HLA-G tumor tissues. Multivariate analysis suggested that HLA-G level was an independent predictor for OS (HR = 3.894, 95% CI = 2.380-6.370, p <0.001). High level of HLA-G significantly correlated with PDAC aggressive features, such as more advanced stage (TNM Stage II) (p<0.001), extrapancreatic infiltration (T3 stage) (p<0.001), lymph node involvement (p=0.019) and poor differentiation (p=0.010). In western blot analysis, almost all of the tumor cell lines (5/6) expressed high levels of HLA-G. In ELISA analysis, the level of plasma sHLA-G in PDAC patients were significantly increased than in healthy control (P=0.0037). Further analysis revealed the level of sHLA-G inversely related to numbers of peripheral activated T cells (CD8+CD28+ T cells), which may indicate that sHLA-G inactivates T cell responses resulting in tumor immune escape. In conclusion, tumor-derived HLA-G may indicate the mechanism of immune escape and impaired PDAC clinical outcome, especially in early-stage patients, which may also be a potential therapeutic target.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".