Targeting AHR Increases Pancreatic Cancer Cell Sensitivity to Gemcitabine through the ELAVL1-DCK Pathway
Bibliographic record
Abstract
The aryl hydrocarbon receptor (AHR) is a transcription factor that is commonly upregulated in pancreatic ductal adenocarcinoma (PDAC). AHR hinders the shuttling of human antigen R (ELAVL1) from the nucleus to the cytoplasm, where it stabilises its target messenger RNAs (mRNAs) and enhances protein expression. Among these target mRNAs are those induced by gemcitabine. Increased AHR expression leads to the sequestration of ELAVL1 in the nucleus, resulting in chemoresistance. This study aimed to investigate the interaction between AHR and ELAVL1 in the pathogenesis of PDAC in vitro. AHR and ELAVL1 genes were silenced by siRNA transfection. The RNA and protein were extracted for quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot (WB) analysis. Direct binding between the ELAVL1 protein and AHR mRNA was examined through immunoprecipitation (IP) assay. Cell viability, clonogenicity, and migration assays were performed. Our study revealed that both AHR and ELAVL1 inter-regulate each other, while also having a role in cell proliferation, migration, and chemoresistance in PDAC cell lines. Notably, both proteins function through distinct mechanisms. The silencing of ELAVL1 disrupts the stability of its target mRNAs, resulting in the decreased expression of numerous cytoprotective proteins. In contrast, the silencing of AHR diminishes cell migration and proliferation and enhances cell sensitivity to gemcitabine through the AHR-ELAVL1-deoxycytidine kinase (DCK) molecular pathway. In conclusion, AHR and ELAVL1 interaction can form a negative feedback loop. By inhibiting AHR expression, PDAC cells become more susceptible to gemcitabine through the ELAVL1-DCK pathway.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".