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Record W4297536586 · doi:10.1186/s12885-022-10094-5

Three-dimensional nuclear telomere architecture and differential expression of aurora kinase genes in chronic myeloid leukemia to measure cell transformation

2022· article· en· W4297536586 on OpenAlexafffund
Fábio Morato de Oliveira, Valderez Ravaglio Jamur, Lismeri Wuicik Merfort, Aline Rangel Pozzo, Sabine Mai

Bibliographic record

VenueBMC Cancer · 2022
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsUniversity of ManitobaResearch Institute in Oncology and HematologyCancerCare Manitoba
FundersUniversity of ManitobaCancerCare Manitoba Foundation
KeywordsTelomereMyeloid leukemiaBiologyCancer researchCell cycleTelomeraseGeneMedicinePathologyOncologyGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Telomere dysfunction results in aneuploidy, and ongoing chromosomal abnormalities. The three-dimensional (3D) nuclear organization of telomeres allows for a distinction between normal and tumor cells. On the other hand, aurora kinase genes (AURKA and AURKB) play an important role regulating the cell cycle. A correlation between overexpression of aurora kinase genes and clinical aggressiveness has been demonstrated in different types of neoplasias. To better understand cellular and molecular mechanisms of CML evolution, it was examined telomere dysfunction (alterations in the 3D nuclear telomere architecture), and the expression levels of AURKA and AURKB genes in two clinical distinct subgroups of CML samples, from the same patient. METHODS: Eighteen CML patients, in total, 36 bone marrow samples (18 patients, chronic vs. accelerated/blast phase) were eligible for 3D telomeric investigations. Quantitative 3D imaging, cytologic diagnosis and cytogenetic determination of additional chromosomal abnormalities were assessed according to standard protocols. RESULTS: Using TeloView software, two CML subgroups were defined based on their 3D telomeric profiles, reflecting the different stages of the disease (chronic vs. accelerated/blast phase). Statistical analyses showed significant differences between the CML subgroups (p < 0.001). We also found that AURKA and AURKB mRNA were expressed at significantly higher levels in both CML subgroups, when compared with healthy donors. Our findings suggest that the evolution of CML progresses from a low to a high level of telomere dysfunction, that is, from an early stage to a more aggressive stage, followed by disease transformation, as demonstrated by telomere, additional chromosomal abnormalities, and gene expression profile dynamics. CONCLUSIONS: Thus, we demonstrated that 3D telomere organization, in accordance with the genomic instability observed in CML samples were able to distinguish subgroup CML patients. Classifying CML patients based on these characteristics might represent an important strategy to define better therapeutic strategies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.243
Teacher spread0.226 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations10
Published2022
Admission routes2
Has abstractyes

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