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Record W4297150762 · doi:10.3390/curroncol29100545

Decreased FOXO1 Expression Is Correlated with Poor Prognosis in Myelodysplastic Syndromes

2022· article· en· W4297150762 on OpenAlexvenueno aff
Zheng Zhang, Nanfang Huang, Feng Xv, Sida Zhao, Juan Guo, Youshan Zhao, Chunkang Chang

Bibliographic record

VenueCurrent Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFOXO transcription factor regulation
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsFOXO1Myelodysplastic syndromesMedicineImmune systemOncologyCancer researchGene expression profilingBioinformaticsBiologyImmunologyInternal medicineGeneGene expressionTranscription factorGeneticsBone marrow

Abstract

fetched live from OpenAlex

Myelodysplastic syndrome is one of the main hematological malignancies that threaten the health of the elderly. However, biomarkers which predict the progression and prognosis of MDS are still controversial and puzzling. FOXO1 gene plays an important role in a variety of intracellular functions, including tumor suppression and cellular immune regulation. However, there is no research report on the correlation between FOXO1 and the clinical features of MDS including immune environment. In this study, we observed that FOXO1 expression is associated with neutrophil count, blasts, chromosome and different MDS scoring systems. FOXO1 expression is closely related to MDS cell immune polarization, and the increase expression of FOXO1 is significantly related to the amplification of immune cell polarization ratio. In addition, FOXO1 expression is associated with progression-free survival and overall survival in MDS patients. Moreover, in a multivariate model FOXO1 low-expression was an independent predictor of poor survival in MDS. In summary, FOXO1 may play a candidate tumor suppressor in MDS, and FOXO1 is a useful independent prognostic predictor in MDS, and it may provide a candidate target therapy in future.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.574

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)0.0000.000

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 head0.043
GPT teacher head0.316
Teacher spread0.273 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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