Level D· Scientific groundwork from lab and animal studiesNarrative ReviewEurope PMCOpen access

Recent advances in investigation of circRNA/lncRNA-miRNA-mRNA networks through RNA sequencing data analysis

Gao Y., Takenaka K., Xu SM., Cheng Y., Janitz M.

Narrative Review on Neuroinflammation, published in Brief Funct Genomics (2025) — summary generated from the PubMed abstract.

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Level D· Scientific groundwork from lab and animal studiesEvidence level of this study

Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.

  • Level A · Stronger Clinical Evidence
  • Level B · Emerging clinical evidence with positive signals
  • Level C · Early human research exploring benefits
  • Level D · Scientific groundwork from lab and animal studies
  • Emerging · Emerging topic under active research
Read the A–D evidence level guide

This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.

Study type
Narrative Review
Journal
Brief Funct Genomics (2025)
Reported sample size
—
Source database
Europe PMC
PMID
40251826
PMCID
PMC12008121
DOI
10.1093/bfgp/elaf005
Citations
25

Abstract (original English)

Non-coding RNAs (ncRNAs) are RNA molecules that are transcribed from DNA but are not translated into proteins. Studies over the past decades have revealed that ncRNAs can be classified into small RNAs, long non-coding RNAs and circular RNAs by genomic size and structure. Accumulated evidences have eludicated the critical roles of these non-coding transcripts in regulating gene expression through transcription and translation, thereby shaping cellular function and disease pathogenesis. Notably, recent studies have investigated the function of ncRNAs as competitive endogenous RNAs (ceRNAs) that sequester miRNAs and modulate mRNAs expression. The ceRNAs network emerges as a pivotal regulatory function, with significant implications in various diseases such as cancer and neurodegenerative disease. Therefore, we highlighted multiple bioinformatics tools and databases that aim to predict ceRNAs interaction. Furthermore, we discussed limitations of using current technologies and potential improvement for ceRNAs network detection. Understanding of the dynamic interplay within ceRNAs may advance the biological comprehension, as well as providing potential targets for therapeutic intervention.

What this study does not prove

  • • This study does not prove SVF is an approved treatment or a replacement for standard care.
  • • This is a narrative review: it collects no new patient data and does not systematically appraise evidence quality.

Evidence level

Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.

How we grade evidence
AnimalsHumansMicroRNAsRNA, MessengerSequence Analysis, RNAComputational BiologyGene Regulatory NetworksRNA, Long NoncodingRNA, Circular

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