Network analysis identifies DAPK3 as a potential biomarker for lymphovascular invasion and prognosis of colon adenocarcinoma
Bibliographic record
Abstract
Abstract Adenocarcinoma of the colon is the fourth most common malignancy worldwide with significant rates of mortality. Hence, the identification of novel molecular biomarkers with prognostic significance is of particular importance for improvements in treatment and patient outcome. Clinical traits and RNA-Seq data of 551 patient samples and 18,205 genes in the UCSC Toil Recompute Compendium of TCGA TARGET and GTEx datasets (restricted to |Primary_site| = colon) were obtained from the Xena platform. Weighted gene co-expression network analysis was completed, and 24 unique modules were assembled to specifically examine 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. Search tool for the retrieval of interacting genes/proteins (STRING) and gene ontology (GO) analyses identified the module to be enriched in genes related to cytoskeletal organization and apoptotic signaling, suggesting involvement in tumor cell survival and migration along with epithelial-mesenchymal transformation. Of genes that were differentially expressed and significant for overall survival, DAPK3 (death-associated protein kinase 3) was revealed as the pseudo-hub of the module. Although DAPK3 expression was reduced in colon cancer patients, survival analysis revealed that high expression of DAPK3 was significantly correlated with greater lymphovascular 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".