Oncoproteins E6/E7 of the human papillomavirus types 16 & 18 synergize in modulating oncogenes and tumor suppressor proteins in colorectal cancer
Bibliographic record
Abstract
Abstract Background This study presents a novel analysis of the oncogenes and tumor suppressor proteins directly modulated by E6/E7 of high-risk HPV types 16 and 18, in two mutational cell models of colorectal cancer (CRC).Methods Two distinct mutational cell models of colorectal cancer, HCT 116 (KRAS mutant) & HT-29 (TP53 mutant) were transfected with E6/E7 of HPV16 and HPV18, individually and in combination (co-transfection), via lipofection. Further, we utilized a liquid chromatography mass spectrometry (LC-MS/MS) approach to analyze and compare the proteomes of KRAS mutant HCT 116 and TP53 mutant HT-29 cell models of CRC, expressing E6/E7 of HPVs type 16 and 18, both, individually and in combination.Results We generated six stably transfected cell lines, namely, HCT 116 HPV16+, HCT 116 HPV18+, HCT 116 HPV16+/18+, HT-29 HPV16+, HT-29 HPV18+, HT-29 HPV16+/18+. Our proteomics data revealed a significantly higher, HPV-induced, modulation of oncogenes and tumor suppressor proteins in the TP53 mutant model of CRC, as compared to the KRAS mutant cell model (p ≤ 0.01). Less than 1% of the genes were found to be commonly modulated by HPV, between the two models of CRC. In addition, we report that HT-29 cells, expressing E6/E7 of both HPVs 16 and 18, significantly reduced the downregulation of oncogenes as compared to cells expressing E6/E7 of either HPV16 or HPV18 individually (p-value is ≤ 0.00001).Conclusion Our data imply that coinfections with the two most frequently observed high-risk HPV types worldwide, leads to the sustenance of a pro-oncogenic environment in CRC; that is suggestive of the cooperative role of the viruses in inflicting cancer. Further, HPV modulates different sets of oncogenes/tumor suppressor proteins in CRC models of varying mutational backgrounds, thus highlighting the importance of personalized therapies for diseases with mutational heterogeneities.
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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".