High Expression of Human Leukocyte Antigen-G is Associated with a Poor Prognosis in Patients with PDAC
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it