Evidence map›Paper›PMID 41372940›Full record

ReviewCancer cell international2025

Non-coding RNAs in laryngeal squamous cell carcinoma: diagnostic biomarkers and emerging exosome-based therapeutic strategies.

Mehmet Nuri Elgormus, Burcu Biltekin, Mustafa Ibas

Abstract readReview
In one paragraph

Review in Cancer cell international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mehmet Nuri ElgormusDepartment of Otorhinolaryngology, Atlas University Faculty of Medicine, Istanbul, 34480, Türkiye.
Burcu BiltekinDepartment of Histology and Embryology, Atlas University Faculty of Medicine, Istanbul, 34480, Türkiye. burcu.biltekin@atlas.edu.tr.
Mustafa IbasDepartment of Otorhinolaryngology, Atlas University Faculty of Medicine, Istanbul, 34480, Türkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Laryngeal Squamous Cell Carcinoma (LSCC) presents significant clinical challenges, including late-stage diagnosis and high rates of recurrence, underscoring an urgent need for novel molecular biomarkers. This review synthesizes current research on non-coding RNAs as pivotal players in the pathogenesis of LSCC. We focus on microRNAs (miRNAs) as powerful diagnostic and prognostic tools, exploring both tissue-specific and circulating exosomal miRNAs (e.g., miR-155, miR-141, miR-21) that reflect tumor biology and correlate with clinical outcomes. Additionally, we examine the therapeutic potential of small interfering RNA (siRNA)-mediated gene silencing, a strategy that enables the targeted knockdown of key oncogenes and pathways associated with LSCC progression, metastasis, and chemoresistance (e.g., targeting EpCAM, MGST1, and Notch1). We discuss the critical challenge of in vivo delivery and the potential of exosome-based systems to bridge the gap between preclinical success and clinical application. By integrating the diagnostic insights from miRNA profiling with the therapeutic precision of siRNA technology, this review outlines a novel integrated Diagnostic and therapeutic framework for laryngeal cancer. We propose that exosomes can serve as a unified platform, first as a source for non-invasive diagnostic biomarkers and subsequently as a natural, biocompatible vehicle for targeted siRNA delivery, bridging the gap between personalized diagnosis and treatment.

Indexed as

BiomarkersExosomesLaryngeal squamous cell carcinoma (LSCC)Liquid biopsyMicroRNA (miRNA)SiRNATargeted therapy

Identifiers

PMID41372940
PMCPMC12801844

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.