Noncanonical functions of telomerase and telomeres in viruses‐associated cancer
Bibliographic record
Abstract
The cause of cancer is attributed to the uncontrolled growth and proliferation of cells resulting from genetic changes and alterations in cell behavior, a phenomenon known as epigenetics. Telomeres, protective caps on the ends of chromosomes, regulate both cellular aging and cancer formation. In most cancers, telomerase is upregulated, with the telomerase reverse transcriptase (TERT) enzyme and telomerase RNA component (TERC) RNA element contributing to the maintenance of telomere length. Additionally, it is noteworthy that two viruses, human papillomavirus (HPV) and Epstein-Barr virus (EBV), utilize telomerase for their replication or persistence in infected cells. Also, TERT and TERC may play major roles in cancer not related to telomere biology. They are involved in the regulation of gene expression, signal transduction pathways, cellular metabolism, or even immune response modulation. Furthermore, the crosstalk between TERT, TERC, RNA-binding proteins, and microRNAs contributes to a greater extent to cancer biology. To understand the multifaceted roles played by TERT and TERC in cancer and viral life cycles, and then to develop effective therapeutic strategies against these diseases, are fundamental for this goal. By investigating deeply, the complicated mechanisms and relationships between TERT and TERC, scientists will open the doors to new therapies. In its analysis, the review emphasizes the significance of gaining insight into the multifaceted roles that TERT and TERC play in cancer pathogenesis, as well as their involvement in the viral life cycle for designing effective anticancer therapy approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".