Oncogenic role of PDK4 in human colon cancer cells
Bibliographic record
Abstract
BACKGROUND: Cancer cells maintain high rates of glycolysis. Pyruvate dehydrogenase kinases (PDK) contribute to this phenomenon, which favours apoptosis resistance and cellular transformation. We previously reported upregulation of PDK4 in normal mucosa of colorectal cancer (CRC) patients compared with controls and in preneoplastic intestine of our mouse model. Decreased methylation of four consecutive PDK4 CpGs was observed in normal mucosa of patients. Although other members of the PDK family have been investigated for transformation potential, PDK4 has not been extensively studied. METHODS: PDK4 methylation in blood of CRC patients and controls was evaluated by pyrosequencing. PDK4 expression in human colon carcinoma cells was down-regulated by RNAi. Cellular migration and invasion, apoptosis and qRT-PCR of key genes were assessed. RESULTS: Pyrosequencing revealed decreased methylation of the same four consecutive CpGs in the blood of patients compared with controls. Cellular migration and invasion were reduced and apoptosis was increased following transient or stable inhibition of PDK4. Expression of vimentin, HIF-1 and VEGFA was reduced. CONCLUSIONS: These studies demonstrate the involvement of PDK4 in transformation. Methylation assessment of PDK4 in the blood may be useful for non-invasive CRC detection. PDK4 should be considered as a target for development of anticancer strategies and therapies.
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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".