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Record W2755417012 · doi:10.5539/cco.v6n2p7

The Effect of Human Papillomavirus E6 Oncogene on the Radiosensitivity of Non-Oropharyngeal Cancer Cells

2017· article· en· W2755417012 on OpenAlexvenueno aff
Angela Hong, Xiaoying Zhang, Xiao Mei Zhang, Barbara Rose

Bibliographic record

VenueCancer and Clinical Oncology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsnot available
Fundersnot available
KeywordsOncogeneRadiosensitivityCancer researchCancerClonogenic assayMelanomaTransfectionCell cultureColorectal cancerAdenocarcinomaMedicineCellOncologyBiologyInternal medicineCell cycleRadiation therapy

Abstract

fetched live from OpenAlex

Background:We have previously shown that stable transfection of the human papillomavirus (HPV) E6*I oncogene can sensitize two HPV negative oropharyngeal cancer (OSCC) cell lines to radiation. In the current study, we extended our work on OSCC to determine whether the HPV E6 oncogene can enhance the radiosensitivity of non-OSCC cell lines.Methods:Three non-OSCC cell lines (melanoma, colorectal adenocarcinoma and large cell lung cancer) were stably transfection with the HPV E6 oncogene (E6 total, E6*I and E6*II) and treated with different doses of radiation. Clonogenic assays were used to measure the radiation survival.Results:Following transfection, there was a reduction in the survival of the melanoma cell line after 2 Gy (SF2) from 0.401 (untransfected) to 0.219 (Melanoma-E6 total). This reduction was not evident at higher doses of radiation. There was no significant change in the SF2 of melanoma-E6*I (0.303) and melanoma-E6*II (0.414). The SF2 colorectal adenocarcinoma and large cell lung cancer cell lines did not change significantly after transfection.Conclusions: The radiosensitizing effect of HPV E6 oncogene is cell line specific. We found no clear evidence of a radiosensitising effect of E6 in these three non-OSCC cancer cell lines.

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)0.001
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 score0.245
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.041
GPT teacher head0.430
Teacher spread0.389 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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