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Record W4289544678 · doi:10.1002/1878-0261.13299

Tieing together loose ends: telomere instability in cancer and aging

2022· review· en· W4289544678 on OpenAlexafffund
Gustavo Borges, Mélanie Criqui, Lea Harrington

Bibliographic record

VenueMolecular Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersInstitute of Cancer ResearchFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsTelomereTelomeraseGenome instabilityBiologySenescenceTelomerase reverse transcriptaseCancer cellCell biologyCancerSomatic cellDNA damageCancer researchGeneticsDNAGene

Abstract

fetched live from OpenAlex

Telomere maintenance is essential for maintaining genome integrity in both normal and cancer cells. Without functional telomeres, chromosomes lose their protective structure and undergo fusion and breakage events that drive further genome instability, including cell arrest or death. One means by which this loss can be overcome in stem cells and cancer cells is via re-addition of G-rich telomeric repeats by the telomerase reverse transcriptase (TERT). During aging of somatic tissues, however, insufficient telomerase expression leads to a proliferative arrest called replicative senescence, which is triggered when telomeres reach a critically short threshold that induces a DNA damage response. Cancer cells express telomerase but do not entirely escape telomere instability as they often possess short telomeres; hence there is often selection for genetic alterations in the TERT promoter that result in increased telomerase expression. In this review, we discuss our current understanding of the consequences of telomere instability in cancer and aging, and outline the opportunities and challenges that lie ahead in exploiting the reliance of cells on telomere maintenance for preserving genome stability.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.065
GPT teacher head0.393
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations32
Published2022
Admission routes2
Has abstractyes

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