MicroRNA modulated networks of adaptive and innate immune response in pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Despite progress in treatment strategies, only ~24% of pancreatic ductal adenocarcinoma (PDAC) patients survive >1 year. Our goal was to elucidate deregulated pathways modulated by microRNAs (miRNAs) in PDAC and Vater ampulla (AMP) cancers. Global miRNA expression was identified in 19 PDAC, 6 AMP and 25 paired, histologically normal pancreatic tissues using the GeneChip 4.0 miRNA arrays. Computational approaches were used for miRNA target prediction/identification of miRNA-regulated pathways. Target gene expression was validated in 178 pancreatic cancer and 4 pancreatic normal tissues from The Cancer Genome Atlas (TCGA). 20 miRNAs were significantly deregulated (FC≥2 and p<0.05) (15 down- and 5 up-regulated) in PDAC. miR-216 family (miR-216a-3p, miR-216a-5p, miR-216b-3p and miR-216b-5p) was consistently down-regulated in PDAC. miRNA-modulated pathways are associated with innate and adaptive immune system responses in PDAC. AMP cancers showed 8 down- and 1 up-regulated miRNAs (FDR p<0.05). Most enriched pathways (p<0.01) were RAS and Nerve Growth Factor signaling. PDAC and AMP display different global miRNA expression profiles and miRNA regulated networks/tumorigenesis pathways. The immune response was enriched in PDAC, suggesting the existence of immune checkpoint pathways more relevant to PDAC than AMP.
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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".