MétaCan
Menu
Back to cohort
Record W4411214366 · doi:10.3390/biomedicines13061435

Clinical Utility of IFIT Proteins in Human Malignancies

2025· review· en· W4411214366 on OpenAlexafffund
Armen Parsyan, Arpitha Kochiyanil, Anne C. Bonvissuto, Vasudeva Bhat, Alison L. Allan

Bibliographic record

VenueBiomedicines · 2025
Typereview
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsSt Joseph's Health CareLondon Health Sciences CentreWestern University
FundersAcademic Medical Organization of Southwestern OntarioCanadian Institutes of Health ResearchCancer Research Society
KeywordsComputational biologyCancer researchMedicineBiology

Abstract

fetched live from OpenAlex

Interferon (IFN)-induced proteins with tetratricopeptide repeats (IFITs) are key interferon-stimulated genes (ISGs), and in humans include IFIT1, IFIT2, IFIT3 and IFIT5. These proteins are primarily known for their role in the innate immune response to pathogens. However, growing evidence suggests that IFITs participate in a range of other cellular processes, including cancer development and progression. Notably, IFITs may behave in either a pro-oncogenic or tumor suppressive fashion depending on cancer types and emphasizing their potential dual function in tumorigenesis. Importantly, IFITs have shown potential to be utilized as clinical biomarkers in oncology. Their aberrant expression has been correlated with survival and other clinical outcomes, including resistance to radiotherapy, chemotherapy, targeted treatments and immunotherapy in various cancers. Additionally, they have also been reported to be a part of various clinical predictive models in cancers. This review provides an overview of the current understanding of IFIT proteins' involvement in cancers, with an emphasis on their emerging roles as clinically relevant biomarkers.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.077
GPT teacher head0.410
Teacher spread0.333 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueBiomedicinesSame topicinterferon and immune responsesFrench-language works237,207