Molecular network analyses implicate death-associated protein kinase 3 (DAPK3) as a key factor in colitis-associated dysplasia progression
Bibliographic record
Abstract
ABSTRACT Ulcerative colitis (UC) is a progressive disorder that elevates the risk of cancer development through a colitis-dysplasia-carcinoma sequence. Differential gene expression (DEGs) profiles of three UC clinical subtypes and healthy controls were developed for the GSE47908 microarray dataset [n = 15 (healthy controls), n = 20 (left-sided colitis), n = 19 (pancolitis), and n = 6 (colitis-associated dysplasia, CAD)] using limma R. Gene ontology (GO) enrichment analysis of DEGs revealed a shift in transcriptome landscape as UC progressed from left-sided colitis to pancolitis to CAD, from being immune-centric to being cytoskeleton-dependent. Hippo signaling (via Yes-associated protein, YAP) and Ephrin receptor signaling were the top canonical pathways progressively altered in concert with the pathogenic progression of UC. Molecular interaction network analysis of DEGs in left-sided colitis, pancolitis, and CAD revealed one pairwise line or edge that was topologically important to the network structure. This edge was found to be highly enriched in actin-based processes, and death-associated protein kinase 3 (DAPK3) was a critical member and sole protein kinase associated with this edge. DAPK3 is a regulator of actin-cytoskeleton reorganization that controls proliferation and apoptosis. Differential correlation analyses revealed a negative correlation for DAPK3-YAP in healthy controls which flipped to positive in left-sided colitis. With UC progression to CAD, the DAPK3-YAP correlation grew progressively more positive. In summary, DAPK3 was identified as a candidate gene involved in UC progression to dysplasia.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".