ArticleBioData mining2023
Signature literature review reveals AHCY, DPYSL3, and NME1 as the most recurrent prognostic genes for neuroblastoma.
Article in BioData mining, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- Nine quick tips for trustworthy machine learning in the biomedical sciences.PLoS computational biology · 2025Article
- The changes of immune function after neuroblastoma chemotherapy and the prediction of potential immune targets.BMC medical genomics · 2025Article
- DBSCAN and DBCV application to open medical records heterogeneous data for identifying clinically significant clusters of patients with neuroblastoma.BioData mining · 2025Article
- Reciprocal Regulation Between the SCFbioRxiv : the preprint server for biology · 2025Article
- Eight quick tips for biologically and medically informed machine learning.PLoS computational biology · 2025Article
- Nonmetastatic cell 1 gene polymorphisms are associated with the clinical progression and prognoses of patients with colorectal cancer.Frontiers in genetics · 2025Article
- Gene signatures for cancer research: A 25-year retrospective and future avenues.PLoS computational biology · 2024Article
- Olive Oil Components as Novel Antioxidants in Neuroblastoma Treatment: Exploring the Therapeutic Potential of Oleuropein and Hydroxytyrosol.Nutrients · 2024Review
- Revolutionizing pediatric neuroblastoma treatment: unraveling new molecular targets for precision interventions.Frontiers in cell and developmental biology · 2024Review
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Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuroblastoma is a childhood neurological tumor which affects hundreds of thousands of children worldwide, and information about its prognosis can be pivotal for patients, their families, and clinicians. One of the main goals in the related bioinformatics analyses is to provide stable genetic signatures able to include genes whose expression levels can be effective to predict the prognosis of the patients. In this study, we collected the prognostic signatures for neuroblastoma published in the biomedical literature, and noticed that the most frequent genes present among them were three: AHCY, DPYLS3, and NME1. We therefore investigated the prognostic power of these three genes by performing a survival analysis and a binary classification on multiple gene expression datasets of different groups of patients diagnosed with neuroblastoma. Finally, we discussed the main studies in the literature associating these three genes with neuroblastoma. Our results, in each of these three steps of validation, confirm the prognostic capability of AHCY, DPYLS3, and NME1, and highlight their key role in neuroblastoma prognosis. Our results can have an impact on neuroblastoma genetics research: biologists and medical researchers can pay more attention to the regulation and expression of these three genes in patients having neuroblastoma, and therefore can develop better cures and treatments which can save patients' lives.
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