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Record W3024719193 · doi:10.1002/hed.26244

Significant association between host transcriptome‐derived <scp>HPV</scp> oncogene <scp>E6</scp>* influence score and carcinogenic pathways, tumor size, and survival in head and neck cancer

2020· article· en· W3024719193 on OpenAlexaff
Tingting Qin, Lada A. Koneva, Yidan Liu, Yanxiao Zhang, Anna E. Arthur, Katie R. Zarins, Thomas E. Carey, Douglas B. Chepeha, Gregory T. Wolf, Laura S. Rozek, Maureen A. Sartor

Bibliographic record

VenueHead & Neck · 2020
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of Toronto
FundersNational Cancer InstituteMedical School, University of MichiganNational Institutes of HealthUniversity of Michigan
KeywordsTranscriptomeOncogeneCarcinogenCancerHost (biology)Host factorsHead and neckCancer researchBiologyMedicineInternal medicineGeneticsGeneCell cycleGene expressionSurgeryVirus

Abstract

fetched live from OpenAlex

BACKGROUND: Human papillomavirus (HPV) oncogenes E6, E7, and shorter isoforms of E6 (E6*) are known carcinogenic factors in head and neck squamous cell carcinoma (HNSCC). Little is known regarding E6* functions. METHODS: We analyzed RNA-seq data from 68 HNSCC HPV type 16-positive tumors to determine host genes and pathways associated with E6+E7 expression (E6E7) or the percent of full-length E6 (E6%FL). Influence scores of E6E7 and E6%FL were used to test for associations with clinical variables. RESULTS: For E6E7, we recapitulated all major known affected pathways and revealed additional pathways. E6%FL was found to affect mitochondrial processes, and E6%FL influence score was significantly associated with overall survival and tumor size. CONCLUSIONS: HPV E6E7 and E6* result in extensive, dose-dependent compensatory effects and dysregulation of key cancer pathways. The switch from E6 to E6* promotes oxidative phosphorylation, larger tumor size, and worse prognosis, potentially serving as a prognostic factor for HPV-positive HNSCC.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.282
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 teacher head, not a consensus.

Study designObservational
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

Citations16
Published2020
Admission routes1
Has abstractyes

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