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Record W1968640027 · doi:10.1002/jcb.24398

miR‐200c enhances radiosensitivity of human breast cancer cells

2012· article· en· W1968640027 on OpenAlexaff
Jing Lin, Cong Liu, Fu Gao, R. E. J. Mitchel, Luqian Zhao, Yanyong Yang, Jixiao Lei, Jianming Cai

Bibliographic record

VenueJournal of Cellular Biochemistry · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsAtomic Energy (Canada)
FundersShanghai Pulmonary HospitalTongji UniversityNational Science Foundation
KeywordsRadiosensitivityCancer researchBreast cancerCancer cellApoptosisCancerIonizing radiationRadiation therapyCellMedicineOncologyBiologyChemistryInternal medicineIrradiationPhysicsGenetics

Abstract

fetched live from OpenAlex

Due to the intrinsic resistance of many tumors to radiotherapy, current methods to improve the survival of cancer patients largely depend on increasing tumor radiosensitivity. It is well-known that miR-200c inhibits epithelial-mesenchymal transition (EMT), and enhances cancer cell chemosensitivity. We sought to clarify the effects of miR-200c on the radiosensitization of human breast cancer cells. In this study, we found that low levels of miR-200c expression correlated with radiotolerance in breast cancer cells. miR-200c overexpression could increase radiosensitivity in breast cancer cells by inhibiting cell proliferation, and by increasing apoptosis and DNA double-strand breaks. Additionally, we found that miR-200c directly targeted TANK-binding kinase 1 (TBK1). However, overexpression of TBK1 partially rescued miR-200c mediated apoptosis induced by ionizing radiation. In summary, miR-200c can be a potential target for enhancing the effect of radiation treatment on breast cancer cells.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.248
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations86
Published2012
Admission routes1
Has abstractyes

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