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Record W4409727307 · doi:10.1016/j.isci.2025.112505

Polycyclic aromatic hydrocarbons promote tumorigenesis of gallbladder cancer via aryl hydrocarbon Receptor-HEGBC positive feedback axis

2025· article· en· W4409727307 on OpenAlexaff
Lei Yuan, Yumeng Zhang, Jianguo Xu, Feng Liu, Yingchao Liang, Beili Wang, Meihong Wu, Zhiyuan Fan, Yu Li

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsNovelis (Canada)
FundersNational Natural Science Foundation of China
KeywordsAryl hydrocarbon receptorCarcinogenesisArylChemistryPolycyclic aromatic hydrocarbonHydrocarbonGallbladder cancerGallbladderCancerStereochemistryOrganic chemistryInternal medicineMedicineBiochemistry

Abstract

fetched live from OpenAlex

Gallbladder cancer (GBC) is a highly aggressive tumor associated with risk factors, such as chronic infection, gallstones, and exposure to harmful chemicals. Our study explores the role of polycyclic aromatic hydrocarbons (PAHs) and long noncoding RNA HEGBC in GBC progression. We found that PAHs activate the aryl hydrocarbon receptor (AhR), which enhances HEGBC expression and promotes GBC cell proliferation, invasion, and metastasis both in vitro and in vivo . Mechanistically, AhR binds to the HEGBC promoter, establishing a positive feedback loop that further activates AhR transcription. Moreover, CYP1A1 was identified as a key downstream effector of PAHs-AhR/HEGBC-mediated proliferation, migration, and invasion of GBC cells in vitro and in vivo . These findings provide the integrative view of a molecular mechanism loop for regulating the malignant progression of GBC centered by PAHs/AhR/HEGBC, which represents a promising strategy for the treatment of GBC.

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 score0.003
Threshold uncertainty score0.009

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)0.0030.000

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 head0.007
GPT teacher head0.269
Teacher spread0.262 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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