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Record W2940715928 · doi:10.1139/bcb-2018-0235

Propofol promotes apoptosis of colorectal cancer cells via alleviating the suppression of lncRNA HOXA11-AS on miRNA let-7i

2019· article· en· W2940715928 on OpenAlexvenueno aff
Yanling Ren, Wei Zhang

Bibliographic record

VenueBiochemistry and Cell Biology · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer, Stress, Anesthesia, and Immune Response
Canadian institutionsnot available
Fundersnot available
KeywordsColorectal cancerApoptosisPropofolCancer researchFlow cytometrymicroRNACarcinogenesisMalignancyCancerCell growthCellMedicineBiologyChemistryInternal medicinePharmacologyImmunologyGeneGenetics

Abstract

fetched live from OpenAlex

To date, surgical resection is the mainstay for the treatment of colorectal cancer (CRC). Propofol (2,6-diisopropylphenol), one of the most commonly used intravenous anaesthetic agents, has been reported to be involved in modulating the malignancy of a variety of human cancers. However, the underlying mechanisms remain poorly understood. In this study, using a cell counting kit (CCK-8), flow cytometry, and caspase-3 cleavage assays, we found that propofol promoted cell apoptosis and inhibited cell proliferation in both Colo205 and SW620 cells, through the down-regulation of HOXA11-AS and up-regulation of let-7i. Moreover, gain-of-function studies of HOXA11-AS or loss-of-function studies of let-7i also revealed a negative correlation between HOXA11-AS and let-7i in propofol-mediated biological functions of CRC cells. Furthermore, our mechanistic experiments revealed that HOXA11-AS acts as a molecular sponge for let-7i, thereby regulating the expression of ABCC10. We investigate the theory that propofol suppresses colorectal cancer tumorigenesis by modulating the HOXA11-AS-let-7i-ABCC10 regulatory network, indicating the potential for propofol to control CRC development.

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 score0.007
Threshold uncertainty score0.434

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 head0.006
GPT teacher head0.241
Teacher spread0.236 · 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

Citations37
Published2019
Admission routes1
Has abstractyes

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