Evidence map›Paper›PMID 42233780›Full record

ReviewBioscience reports2026

Targeting the MYCN interaction network in neuroblastoma.

Chelsea Xinyi Yow, Sharon Yeoh, Eoin Leen, Richard Bayliss

Abstract readReview
In one paragraph

Review in Bioscience reports, 2026. 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

4 authors.

Chelsea Xinyi YowSchool of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, LS2 9JT, U.K.
Sharon YeohSchool of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, LS2 9JT, U.K.ORCID 0000-0002-8530-1405
Eoin LeenSchool of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, LS2 9JT, U.K.
Richard BaylissSchool of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, LS2 9JT, U.K.ORCID 0000-0003-0604-2773

Funding

UKRI | Medical Research Council (MRC) MR/V029975/1
6 · The paper itself

Abstract

Neuroblastoma is a common solid tumour in children and accounts for a disproportionate share of childhood cancer mortality. High-risk neuroblastoma is a major clinical challenge, with survival rates below 50% despite intensive therapy. Amplification of the MYCN oncogene is a hallmark of high-risk disease. MYCN protein is a transcription factor that promotes proliferation and blocks differentiation. MYCN is often described as 'undruggable' due to its lack of enzymatic activity and intrinsically disordered nature that means it lacks well-defined binding pockets for small molecules. This review explores the molecular interactome of MYCN as an opportunity for therapeutic discovery. We highlight functional and structural features of the interactions with key partners, such as MAX, WDR5, TFIIIC5 and Aurora kinase A. We discuss emerging strategies to disrupt MYCN interactions and to enable its degradation through disruption of protein stabilisation mechanisms or the use of degraders such as proteolysis targeting chimeras. By integrating knowledge of MYCN biology, molecular structures and chemical biology, these approaches provide promising routes towards targeted therapies for MYCN-driven neuroblastoma.

Indexed as

Antineoplastic AgentsNeuroblastomaN-Myc Proto-Oncogene ProteinAnimalsAurora Kinase AHumansMolecular Targeted TherapyProtein BindingProtein Interaction MapsProteolysisAntineoplastic AgentsAurora Kinase AMYCN protein, humanN-Myc Proto-Oncogene ProteindegradersMYCN amplificationneuroblastomaprotein-protein interactions

Identifiers

PMID42233780
PMCPMC13270163

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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.