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Record W2904550514 · doi:10.1016/j.celrep.2018.11.065

KLF4, A Gene Regulating Prostate Stem Cell Homeostasis, Is a Barrier to Malignant Progression and Predictor of Good Prognosis in Prostate Cancer

2018· article· en· W2904550514 on OpenAlexfundno aff
Xiaozhong Xiong, Markus Schöber, Evelyne Tassone, Alireza Khodadadi‐Jamayran, Ana Sastre-Perona, Hua Zhou, Aristotelis Tsirigos, Steven S. Shen, Miao Chang, Jonathan Melamed, Liliana Ossowski, Elaine Wilson

Bibliographic record

VenueCell Reports · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicKruppel-like factors research
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesM.S.I. FoundationShell United StatesNational Cancer InstituteSchool of Medicine, New York UniversityCenter for Outcomes Research and Evaluation, Yale School of MedicineNational Institutes of HealthAmerican Cancer Society
KeywordsKLF4Prostate cancerProstateCancerCancer researchCancer stem cellStem cellBiologyGene signatureHomeostasisPathologyOncologyMedicineInternal medicineGeneGene expressionSOX2Embryonic stem cellEndocrinologyCell biologyGenetics

Abstract

fetched live from OpenAlex

There is a considerable need to identify those individuals with prostate cancer who have indolent disease. We propose that genes that control adult stem cell homeostasis in organs with slow turnover, such as the prostate, control cancer fate. One such gene, KLF4, overexpressed in murine prostate stem cells, regulates their homeostasis, blocks malignant transformation, and controls the self-renewal of tumor-initiating cells. KLF4 loss induces the molecular features of aggressive cancer and converts PIN lesions to invasive sarcomatoid carcinomas; its re-expression in vivo reverses this process. Bioinformatic analysis links these changes to human cancer. KLF4 and its downstream targets make up a gene signature that identifies indolent tumors and predicts recurrence-free survival. This approach may improve prognosis and identify therapeutic targets for advanced cancer.

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 score0.279
Threshold uncertainty score0.833

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 head0.011
GPT teacher head0.281
Teacher spread0.270 · 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

Citations36
Published2018
Admission routes1
Has abstractyes

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