HPV16 E6/E7 upregulates HIF-2α and VEGF by inhibiting LKB1 in lung cancer cells
Bibliographic record
Abstract
Long-term persistent infection of HPV16 E6/E7 is frequently associated with lung cancers, especially in non-smokers and in Asians. However, molecular mechanisms of HPV16 E6/E7 induction of lung cancer are not fully understood. Using bi-directional genetic manipulation and four well-established lung cancer cell lines, we showed HPV16 E6/E7 downregulated expression of liver kinase B1 at both protein and messenger RNA levels; liver kinase B1 downregulated hypoxia-inducible factor 2α at protein level but not at messenger RNA level, and hypoxia-inducible factor 2α upregulated vascular endothelial growth factor at both protein and messenger RNA levels. This is the first study to show hypoxia-inducible factor 2α as a downstream effector of liver kinase B1 in lung cancer cells. Our results indicate that HPV16 E6/E7 indirectly upregulated the expression of vascular endothelial growth factor by inhibition of liver kinase B1 expression and upregulation of hypoxia-inducible factor 2α expression, thus propose a human papillomavirus-liver kinase B1-hypoxia-inducible factor 2α-vascular endothelial growth factor axis for the tumorigenesis of lung cancer. Our study also provides new evidence to support the critical role of liver kinase B1 in the pathogenesis of human papillomavirus-related lung cancer and suggests novel therapeutic targets.
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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".