Role Of Telomere And Telomerase In Cancer: A Comprehensive Update In Recent Advances
Bibliographic record
Abstract
One of the most important and fast changing fields of research in current molecular oncology and cancer biology is the complex interaction among telomeres, telomerase, and cancer development. With special focus on recent years' innovative discoveries, this thorough study synthesizes and evaluates the significant improvements achieved in knowledge of the intricate interaction between telomere biology and cancer progression over the previous decade. Emerging as major controllers of genomic stability, cellular lifetime, and cancer formation are telomeres, the specialized nucleoprotein structures acting as protective caps at chromosomal ends. A key stage in cellular immortality and malignant transformation is the activation of telomerase, a specialized reverse transcriptase usually repressed in most somatic cells but revived in about 85–90% of human malignancies. The most recent findings in telomere biology—including hitherto unidentified regulatory mechanisms, fresh protein interactions, and new therapeutic approaches transforming our knowledge of cancer formation and treatment—are investigated in great detail here. By means of meticulous examination of recent clinical trials, experimental investigations, and technological developments, we investigate how targeting telomere preservation processes presents interesting directions for cancer treatment. The review particularly emphasizes groundbreaking findings that have transformed our understanding of telomere dynamics in cancer cells, including novel mechanisms of alternative lengthening of telomeres (ALT), the role of telomere position effects in gene regulation, and the impact of telomere dysfunction on chromosomal instability and tumor evolution. Moreover, we present thorough information on new therapeutic approaches including immunotherapy techniques, telomere-targeting drugs, and new telomerase inhibitors, therefore offering important new perspectives on their possible clinical uses and constraints.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".