Insight into Oncogenic Viral Pathways as Drivers of Viral Cancers: Implication for Effective Therapy
Bibliographic record
Abstract
As per a recent study conducted by the WHO, 15.4% of all cancers are caused by infectious agents of various categories, and more than 10% of them are attributed to viruses. The emergence of COVID-19 has once again diverted the scientific community's attention toward viral diseases. Some researchers have postulated that SARS-CoV-2 will add its name to the growing list of oncogenic viruses in the long run. However, owing to the complexities in carcinogenesis of viral origin, researchers across the world are struggling to identify the common thread that runs across different oncogenic viruses. Classical pathways of viral oncogenesis have identified oncogenic mediators in oncogenic viruses, but these mediators have been reported to act on diverse cellular and multiple omics pathways. In addition to viral mediators of carcinogenesis, researchers have identified various host factors responsible for viral carcinogenesis. Henceforth owing to viral and host complexities in viral carcinogenesis, a singular mechanistic pathway remains yet to be established; hence there is an urgent need to integrate concepts from system biology, cancer microenvironment, evolutionary perspective, and thermodynamics to understand the role of viruses as drivers of cancer. In the present manuscript, we provide a holistic view of the pathogenic pathways involved in viral oncogenesis with special emphasis on alteration in the tumor microenvironment, genomic alteration, biological entropy, evolutionary selection, and host determinants involved in the pathogenesis of viral tumor genesis. These concepts can provide important insight into viral cancers, which can have an important implication for developing novel, effective, and personalized therapeutic options for treating viral cancers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".