E6/E7 of HPV Type 16 Promotes Cell Invasion and Metastasis of Human Breast Cancer Cells
Bibliographic record
Abstract
Human papillomaviruses (HPVs) could be important risk factors for breast carcinogenesis and metastasis, as roughly 50% of breast cancers are positive for high-risk HPVs. To determine the role of high-risk HPVs in human breast carcinogenesis and metastasis, we examined the effect of E6/E7 of HPV type 16 in two non-invasive breast cancer cell lines, MCF7 and BT20. We report that E6/E7 of HPV type 16 induces cell invasive and metastatic abilities of MCF7 and BT20 in vitro and in vivo, respectively, in comparison with the wild type cells. This is accompanied by an upregulation of Id-1, a family member of helix-loop-helix (HLH) transcription factors, in MCF7 and BT20 cell lines which express E6/E7. Earlier studies have reported that Id-1 regulates cell invasion and metastasis of human breast cancer cells. To gauge the role of Id-1 in cell invasion and metastasis induced by E6/E7 of HPV type 16, we investigated the effect of E6/E7 in mouse normal embryonic fibroblast (NEF) and knockout Id-1 (Id-1(-/-)) cells. We establish that E6/E7 induces cell invasive ability in NEF but not Id-1(-/-) cells; moreover, we were able to inhibit the invasion ability of MCF7-E6/E7 and BT20-E6/E7 using Id-1 antisense retroviruses. Furthermore, we report that E6/E7 oncoproteins up-regulate Id-1 promoter activity in MCF7 and BT20 cells. We also found that HPV type 16 is present in all invasive and metastatic breast cancer and less frequently in in-situ breast cancer as opposed to normal mammary tissue. In parallel, we demonstrate that Id-1 overexpression is correlated with the presence of HPV type 16 in human invasive and metastatic breast cancer. These data suggest that high-risk HPV infections can induce cell invasion and metastasis in breast cancer through Id-1 regulation.
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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) | 0.002 | 0.001 |
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".