Evidence map›Paper›PMID 35906213›Full record

ArticleNature communications2022

DNA methylation-based epigenetic signatures predict somatic genomic alterations in gliomas.

Jie Yang, Qianghu Wang, Ze-Yan Zhang, Lihong Long, Ravesanker Ezhilarasan, Jerome M Karp, Aristotelis Tsirigos, Matija Snuderl, Benedikt Wiestler, Wolfgang Wick and 3 more

Abstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Review
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  8. Value of artificial intelligence in neuro-oncology.The Lancet. Digital health · 2025
    Review
  9. Article
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  11. Article
  12. Review
  13. Review
  14. Article
  15. Article
  16. Clinical value of theNeuro-oncology advances
    Article
  17. Article
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

13 authors.

Jie YangDepartment of Radiation Oncology, NYU Grossman School of Medicine, New York, NY, USA.ORCID 0000-0003-2241-5066
Qianghu WangDepartment of Bioinformatics, School of Biomedical Engineering and Informatics, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.ORCID 0000-0002-3488-1059
Ze-Yan ZhangDepartment of Radiation Oncology, NYU Grossman School of Medicine, New York, NY, USA.ORCID 0000-0002-7896-3013
Lihong LongDepartment of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ravesanker EzhilarasanDepartment of Radiation Oncology, NYU Grossman School of Medicine, New York, NY, USA.
Jerome M KarpDepartment of Radiation Oncology, NYU Grossman School of Medicine, New York, NY, USA.
Aristotelis TsirigosDepartment of Pathology, NYU Grossman School of Medicine, New York, NY, USA.ORCID 0000-0002-7512-8477
Matija SnuderlDepartment of Pathology, NYU Grossman School of Medicine, New York, NY, USA.
Benedikt WiestlerDepartment of Neuroradiology, Technical University of Munich, Munich, Germany.
Wolfgang WickGerman Cancer Research Center (DKFZ) and Department of Neurology and NCT Neurooncology Program, University of Heidelberg, Heidelberg, Germany.ORCID 0000-0002-6171-634X
Yinsen MiaoDepartment of Statistics, Rice University, Houston, TX, USA.
Jason T HuseDepartment of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Erik P SulmanDepartment of Radiation Oncology, NYU Grossman School of Medicine, New York, NY, USA. erik.sulman@nyulangone.org.ORCID 0000-0003-4933-9120

Funding

(PQ #4) Single cell barcoding for studies of study clonal evolution in glioblastomaR01CA190121 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI SULMAN, ERIK, VERHAAK, ROEL GW · 2014 to 2017
$1.4M
NCI NIH HHS R01 CA190121
6 · The paper itself

Abstract

Molecular classification has improved diagnosis and treatment for patients with malignant gliomas. However, classification has relied on individual assays that are both costly and slow, leading to frequent delays in treatment. Here, we propose the use of DNA methylation, as an emerging clinical diagnostic platform, to classify gliomas based on major genomic alterations and provide insight into subtype characteristics. We show that using machine learning models, DNA methylation signatures can accurately predict somatic alterations and show improvement over existing classifiers. The established Unified Diagnostic Pipeline (UniD) we develop is rapid and cost-effective for genomic alterations and gene expression subtypes diagnostic at early clinical phase and improves over individual assays currently in clinical use. The significant relationship between genetic alteration and epigenetic signature indicates broad applicability of our approach to other malignancies.

Indexed as

DNA MethylationGliomaEpigenesis, GeneticEpigenomicsHumans

Identifiers

PMID35906213
PMCPMC9338285

What OpenQuestion holds

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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.