Machine learning unveils an immune-related DNA methylation profile in germline DNA from breast cancer patients.
Biomarker
Breast cancer
DNA methylation
Early detection
Liquid biopsy
Machine learning
Peripheral blood
Journal
Clinical epigenetics
ISSN: 1868-7083
Titre abrégé: Clin Epigenetics
Pays: Germany
ID NLM: 101516977
Informations de publication
Date de publication:
15 May 2024
15 May 2024
Historique:
received:
11
12
2023
accepted:
26
04
2024
medline:
16
5
2024
pubmed:
16
5
2024
entrez:
15
5
2024
Statut:
epublish
Résumé
There is an unmet need for precise biomarkers for early non-invasive breast cancer detection. Here, we aimed to identify blood-based DNA methylation biomarkers that are associated with breast cancer. DNA methylation profiling was performed for 524 Asian Chinese individuals, comprising 256 breast cancer patients and 268 age-matched healthy controls, using the Infinium MethylationEPIC array. Feature selection was applied to 649,688 CpG sites in the training set. Predictive models were built by training three machine learning models, with performance evaluated on an independent test set. Enrichment analysis to identify transcription factors binding to regions associated with the selected CpG sites and pathway analysis for genes located nearby were conducted. A methylation profile comprising 51 CpGs was identified that effectively distinguishes breast cancer patients from healthy controls achieving an AUC of 0.823 on an independent test set. Notably, it outperformed all four previously reported breast cancer-associated methylation profiles. Enrichment analysis revealed enrichment of genomic loci associated with the binding of immune modulating AP-1 transcription factors, while pathway analysis of nearby genes showed an overrepresentation of immune-related pathways. This study has identified a breast cancer-associated methylation profile that is immune-related to potential for early cancer detection.
Sections du résumé
BACKGROUND
BACKGROUND
There is an unmet need for precise biomarkers for early non-invasive breast cancer detection. Here, we aimed to identify blood-based DNA methylation biomarkers that are associated with breast cancer.
METHODS
METHODS
DNA methylation profiling was performed for 524 Asian Chinese individuals, comprising 256 breast cancer patients and 268 age-matched healthy controls, using the Infinium MethylationEPIC array. Feature selection was applied to 649,688 CpG sites in the training set. Predictive models were built by training three machine learning models, with performance evaluated on an independent test set. Enrichment analysis to identify transcription factors binding to regions associated with the selected CpG sites and pathway analysis for genes located nearby were conducted.
RESULTS
RESULTS
A methylation profile comprising 51 CpGs was identified that effectively distinguishes breast cancer patients from healthy controls achieving an AUC of 0.823 on an independent test set. Notably, it outperformed all four previously reported breast cancer-associated methylation profiles. Enrichment analysis revealed enrichment of genomic loci associated with the binding of immune modulating AP-1 transcription factors, while pathway analysis of nearby genes showed an overrepresentation of immune-related pathways.
CONCLUSION
CONCLUSIONS
This study has identified a breast cancer-associated methylation profile that is immune-related to potential for early cancer detection.
Identifiants
pubmed: 38750495
doi: 10.1186/s13148-024-01674-2
pii: 10.1186/s13148-024-01674-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
66Subventions
Organisme : National Medical Research Council
ID : MOH-OFIRG19nov-0019
Organisme : National Medical Research Council
ID : MOH-OFIRG19nov-0019
Organisme : National Medical Research Council
ID : MOH-OFIRG19nov-0019
Organisme : National Cancer Centre of Singapore
ID : NRSFCCB211A2
Organisme : National Cancer Centre of Singapore
ID : NRSFCCB211A2
Organisme : National Cancer Centre of Singapore
ID : NRSFCCB211A2
Informations de copyright
© 2024. The Author(s).
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