Network analysis identifies DAPK3 as a potential biomarker for lymphatic invasion and colon adenocarcinoma prognosis
Bibliographic record
Abstract
Colon adenocarcinoma is a prevalent malignancy with significant mortality. Hence, the identification of molecular biomarkers with prognostic significance is important for improved treatment and patient outcomes. Clinical traits and RNA-Seq of 551 patient samples in the UCSC Toil Recompute Compendium of The Cancer Genome Atlas TARGET and Genotype Tissue Expression project datasets (primary_site = colon) were used for weighted gene co-expression network analysis to reveal the association between gene networks and cancer cell invasion. One module, containing 151 genes, was significantly correlated with lymphatic invasion, a histopathological feature of higher risk colon cancer. DAPK3 (death-associated protein kinase 3) was identified as the pseudohub of the module. Gene ontology identified gene enrichment related to cytoskeletal organization and apoptotic signaling processes, suggesting modular involvement in tumor cell survival, migration, and epithelial-mesenchymal transformation. Although DAPK3 expression was reduced in patients with colon cancer, high expression of DAPK3 was significantly correlated with greater lymphatic invasion and poor overall survival.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".