Human papilloma virus (HPV) mediated cancers: an insightful update
Bibliographic record
Abstract
Human papillomavirus (HPV), a DNA virus, is a well-documented causative agent of several cancers, including cervical, vulvar, vaginal, penile, anal, and head & neck cancers. Major factors contributing to HPV-related cancers include persistent infection and the oncogenic potential of particular HPV genotypes. High-risk HPV strains, particularly HPV-16 and HPV-18, are responsible for over 70% of cervical cancer cases worldwide, as well as a significant proportion of other genital and head and neck cancers. At the molecular level, the oncogenic activity of these viruses is driven by the overexpression of E6 and E7 oncoproteins. These oncoproteins dysregulate the cell cycle, inhibit apoptosis, and promote the accumulation of DNA damage, ultimately transforming normal cells into cancerous ones. This review aims to provide a comprehensive overview of the recent advances in HPV-related cancer biology and epidemiology. The review highlights the molecular pathways of HPV-driven carcinogenesis, focusing on the role of viral oncoproteins in altering host cell targets and disrupting cellular signalling pathways. The review explores the therapeutic potential of these viral proteins, and discusses current diagnostic and treatment strategies for HPV-associated cancers. Furthermore, the review highlights the critical role of HPV in the development of various malignancies, emphasizing the persistent challenges in combating these cancers despite advancements in vaccination and therapeutic strategies. We also emphasize recent breakthroughs in utilizing biomarkers to monitor cancer therapy responses, such as mRNAs, miRNAs, lncRNAs, proteins, and genetic markers. We hope this review will serve as a valuable resource for researchers working on HPV, providing insights that can guide future investigations into this complex virus, which continues to be a major contributor to global morbidity and mortality.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".