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Record W4225131621 · doi:10.1007/s12032-022-01664-5

Evidence of a role for interleukin-6 in anoikis resistance in oral squamous cell carcinoma

2022· article· en· W4225131621 on OpenAlexaff
Marilena Karavyraki, Richard K. Porter

Bibliographic record

VenueMedical Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsTrinity College
FundersH2020 Marie Skłodowska-Curie ActionsHorizon 2020 Framework ProgrammeIrish Research eLibrary
KeywordsAutocrine signallingAntibodyAnoikisReceptorCancer researchTongueBiologyCancer cellCancerImmunologyInternal medicineMedicinePathology

Abstract

fetched live from OpenAlex

In an endeavour to understand metastasis from oral squamous cell carcinomas, we characterised the metastatic potential of a human tongue derived cell line (SCC-4 cells) and compared this phenotype to pre-cancerous dysplastic oral keratinocyte (DOK) cells derived from human tongue and primary gingival keratinocytes (PGK). We demonstrate that SCC-4 cells constitutively synthesize and release significant amounts of IL-6, a process that is enhanced by the addition of the TLR2/TLR6 agonist, Pam2CSK4. The expression of TLR2/6 and IL-6Ra/gp130 receptors was also confirmed in SCC-4 cells. Cancerous SCC-4 human tongue cells also have a classic EMT profile, unlike precancerous human tongue DOK cells. We also established that IL-6 is driving anoikis resistance in an autocrine fashion and that anti-IL-6 neutralising antibodies, anti-IL-6 receptor antibodies and anti-TLR2 receptor antibodies inhibit anoikis resistance in cancerous SCC-4 human tongue cells. The data suggest a promising role for anti-IL-6 receptor antibody and anti-TLR2 receptor antibody treatment for oral 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)0.001
metaresearch head score (Gemma)0.001
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 score0.406
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.020
GPT teacher head0.326
Teacher spread0.306 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

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