Extensive protein S-nitrosylation associated with human pancreatic ductal adenocarcinoma pathogenesis
Bibliographic record
Abstract
NO (nitric oxide)-mediated protein S-nitrosylation has been established as one major signaling mechanism underlying cancer initiation and development, but its roles in PDAC (pancreatic ductal adenocarcinoma) pathogenesis still remain largely unexplored. In this study, we identified 585 unique S-nitrosylation sites among 434 proteins in PDAC patients and PANC-1 cell line by a site-specific proteomics. Larger number of S-nitrosylated proteins were identified in PDAC tissues and PANC-1 cells than adjacent non-cancerous tissues. These S-nitrosylated proteins are significantly enriched in a multitude of biological processes associated with tumorigenesis, including carbohydrate metabolism, cytoskeleton regulation, cell cycle, focal adhesion, adherent junctions, and cell migration. Components of the pancreatic cancer pathway were extensively S-nitrosylated, such as v-raf-1 murine leukemia viral oncogene homolog 1 (Raf-1) and Signal transducer and activator of transcription 3 (STAT3). Moreover, NOS (NO synthase) inhibitor significantly repressed STAT3 S-nitrosylation in PANC-1 cells, which caused significant increase of STAT3 phosphorylation and PANC-1 cell viability, suggesting important roles of protein S-nitrosylation in PDAC development. These results revealed extensive protein S-nitrosylation associated with PDAC pathogenesis, which provided a basis for protein modification-based cancer diagnosis and targeted therapy.
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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 | 0.000 |
| 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".