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Record W4306676996 · doi:10.3389/fonc.2022.1004793

The molecular mechanisms underlying neutrophil infiltration in vessel co-opting colorectal cancer liver metastases

2022· article· en· W4306676996 on OpenAlexaff
Miran Rada, Nour Hassan, Anthoula Lazaris, Peter Metrakos

Bibliographic record

VenueFrontiers in Oncology · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsColorectal cancerAngiogenesisCancer researchPhenotypeIn vivoDownregulation and upregulationPathologyMedicineBiologyCancerInternal medicineGene

Abstract

fetched live from OpenAlex

Colorectal cancer liver metastases (CRCLMs) have two major histopathological growth patterns (HGPs): desmoplastic (DHGP) and replacement (RHGP). The DHGP tumours derive their vasculature by angiogenesis, while the RHGP tumours use vessel co-option. Various studies have associated RHGP tumours with an unfavourable prognosis, as well as high levels of resistance to anti-angiogenic agents and chemotherapy. Recently, we reported higher numbers of neutrophils in the tumour microenvironment (TME) of vessel co-opting tumours compared to their angiogenic counterparts. However, the molecular mechanisms underlying this phenotype are unclear. Herein, we suggested a positive correlation between the expression of angiopoietin-1 (Ang1) in the hepatocytes and the presence of neutrophils in vessel co-opting tumours. Importantly, upregulation of Ang1 in the hepatocytes is associated with the presence of runt-related transcription factor-1 (RUNX1) in the neighboring cancer cells in vitro and in vivo . Altogether, our data suggest the molecular mechanisms by which neutrophils are infiltrated in vessel co-opting CRCLM lesions. This finding may yield novel therapeutic strategies for CRCLM patients in future.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.746

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 studies0.0010.000
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 head0.018
GPT teacher head0.289
Teacher spread0.270 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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