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Record W4406904562 · doi:10.1096/fj.202401764r

The role of circular <scp>RNAs</scp> in autoimmune diseases: Potential diagnostic biomarkers and therapeutic targets

2025· review· en· W4406904562 on OpenAlexaff
Xin’ai Li, Junhui Wang, Shuo Qi, Jeremiah Amalraj, Jingwei Zhou, Zhiguo Ding

Bibliographic record

VenueThe FASEB Journal · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCircular RNAs in diseases
Canadian institutionsUniversity of TorontoLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersShanXi Science and Technology Department
KeywordsCircular RNARNARNA splicingComputational biologyBiologyTranscriptomeAlternative splicingmicroRNACompeting endogenous RNAGeneLong non-coding RNAGene expressionBioinformaticsMessenger RNAGenetics

Abstract

fetched live from OpenAlex

With the emergence of high-quality sequencing technologies, further research on transcriptomes has become possible. Circular RNA (circRNA), a novel type of endogenous RNA molecule with a covalently closed circular structure through "back-splicing," is reported to be widely present in eukaryotic cells and participates mainly in regulating gene and protein expression in various ways. It is becoming a research hotspot in the non-coding RNA field. CircRNA shows close relation to several varieties of autoimmune diseases (AIDs) in both the physiological and pathological level and could potentially be used clinically in terms of diagnosis and treatment. Here, we focus on reviewing the importance of circRNA in various AIDs, with the aim of establishing new biomarkers and providing novel insights into understanding the role and functions of circRNA in AIDs. Specific signaling pathways of how circular RNAs are regulated in AIDs will also be illustrated in this review.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.256
Teacher spread0.249 · 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 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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicCircular RNAs in diseasesFrench-language works237,207