MétaCan
Menu
Back to cohort
Record W4417360865 · doi:10.3390/curroncol32120707

STAT2 Promotes Tumor Growth in Colorectal Cancer Independent of Type I IFN Receptor Signaling

2025· article· en· W4417360865 on OpenAlexvenueno aff
Jorge Canar, Mariona Bono, Michael Slifker, Giovanni Sitia, Ana M. Gamero

Bibliographic record

VenueCurrent Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicCytokine Signaling Pathways and Interactions
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Cancer Institute
KeywordsSTAT2Colorectal cancerInterferonReceptorSTAT proteinCancerSignal transductionCancer cell

Abstract

fetched live from OpenAlex

The role of Signal Transducer and Activator of Transcription 2 (STAT2) in cancer remains poorly understood. STAT2 is a key mediator of type I interferon (IFN) signaling, activating the expression of IFN-stimulated genes with antiviral and antiproliferative effects. However, emerging evidence suggests that STAT2 can also promote tumor growth. Here, we show that high STAT2 mRNA expression in colon cancer tumors correlates with reduced overall survival in patients. In preclinical models, deletion of STAT2 in tumor cells suppressed tumor growth, whereas STAT2 overexpression enhanced tumor growth, supporting its pro-tumorigenic role. To determine whether this function depends on type I IFN receptor (IFNAR1) signaling, we generated IFNAR1 knockout (IFNAR1 KO) colon carcinoma cells and compared their growth with parental and STAT2-deficient (STAT2 KO) tumor cells. Loss of type I IFN signaling was confirmed by western blot and qPCR analyses. In vitro, IFNAR1 KO and STAT2 KO tumor cells proliferated at similar rates. However, in xenograft tumor transplantation models, IFNAR1 KO cells formed larger tumors while STAT2 KO tumor cells formed smaller ones compared to parental tumor cells. These findings indicate that STAT2 promotes colorectal cancer growth through mechanisms independent of IFNAR1 signaling.

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)0.000
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 score0.180
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

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)0.0010.000

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 head0.066
GPT teacher head0.403
Teacher spread0.337 · 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueCurrent OncologySame topicCytokine Signaling Pathways and InteractionsFrench-language works237,207