Association between AHR Expression and Immune Dysregulation in Pancreatic Ductal Adenocarcinoma: Insights from Comprehensive Immune Profiling of Peripheral Blood Mononuclear Cells
Bibliographic record
Abstract
Pancreatic cancer, particularly pancreatic ductal adenocarcinoma (PDAC), has an immune suppressive environment that allows tumour cells to evade the immune system. The aryl-hydrocarbon receptor (AHR) is a transcription factor that can be activated by certain exo/endo ligands, including kynurenine (KYN) and other tryptophan metabolites. Once activated, AHR regulates the expression of various genes involved in immune responses and inflammation. Previous studies have shown that AHR activation in PDAC can have both pro-tumorigenic and anti-tumorigenic effects, depending on the context. It can promote tumour growth and immune evasion by suppressing anti-tumour immune responses or induce anti-tumour effects by enhancing immune cell function. In this study involving 30 PDAC patients and 30 healthy individuals, peripheral blood samples were analysed. PDAC patients were categorized into Low (12 patients) and High/Medium (18 patients) AHR groups based on gene expression in peripheral blood mononuclear cells (PBMCs). The Low AHR group showed distinct immune characteristics, including increased levels of immune-suppressive proteins such as PDL1, as well as alterations in lymphocyte and monocyte subtypes. Functional assays demonstrated changes in phagocytosis, nitric oxide production, and the expression of cytokines IL-1, IL-6, and IL-10. These findings indicate that AHR’s expression level has a crucial role in immune dysregulation in PDAC and could be a potential target for early diagnostics and personalised therapeutics.
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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 | 0.000 |
| 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 | 0.000 |
| 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".