ArticleBMC cancer2015
A seven-gene CpG-island methylation panel predicts breast cancer progression.
Article in BMC cancer, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 33 citations in OpenAlex.
- Predictive epigenetic biomarkers of successful weight-loss intervention in pre-pubertal children with obesity.Clinical epigenetics · 2026Article
- A review of the use of tumour DNA methylation for breast cancer subtyping and prediction of outcomes.Clinical epigenetics · 2025Review
- Detecting plasma SHOX2, HOXA9, SEPTIN9, and RASSF1A methylation and circulating cancer cells for cholangiocarcinoma clinical diagnosis and monitoring.World journal of gastrointestinal oncology · 2025Article
- A non-mutatedFrontiers in oncology · 2023Article
- Bioinformatics analysis reveals the potential target of rosiglitazone as an antiangiogenic agent for breast cancer therapy.BMC genomic data · 2022Article
- PRKCDBP Methylation is a Potential and Promising Candidate Biomarker for Non-small Cell Lung Cancer.Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2022Article
- Design and experimental validation of OPERA_MET-A panel for deep methylation analysis by next generation sequencing.Frontiers in oncology · 2022Article
- Assessment ofCell journal · 2021Article
- The Clinical and Pathological Profile of BRCA1 Gene Methylated Breast Cancer Women: A Meta-Analysis.Cancers · 2021Review
- Low expression of PRKCDBP promoted cisplatin resistance in lung adenocarcinoma by DNMT1 and TNF‑α.Oncology reports · 2020Article
- Identification of lncRNA biomarkers for lung cancer through integrative cross-platform data analyses.Aging · 2020Article
- DNA Methylation Status in Cancer Disease: Modulations by Plant-Derived Natural Compounds and Dietary Interventions.Biomolecules · 2019Review
- Review
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- Pharmacogenetic and pharmacogenomic discovery strategies.Cancer drug resistance (Alhambra, Calif.) · 2019Review
- Paclitaxel Response Can Be Predicted With Interpretable Multi-Variate Classifiers Exploiting DNA-Methylation and miRNA Data.Frontiers in genetics · 2019Article
- Methylation Profiles of BRCA1, RASSF1A and GSTP1 in Vietnamese Women with Breast CancerAsian Pacific journal of cancer prevention : APJCP · 2018Article
- Hypermethylation of CDKN2A exon 2 in tumor, tumor-adjacent and tumor-distant tissues from breast cancer patients.BMC cancer · 2017Article
- Inference for survival prediction under the regularized Cox model.Biostatistics (Oxford, England) · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDNA methylation regulates gene expression, through the inhibition/activation of gene transcription of methylated/unmethylated genes. Hence, DNA methylation profiling can capture pivotal features of gene expression in cancer tissues from patients at the time of diagnosis. In this work, we analyzed a breast cancer case series, to identify DNA methylation determinants of metastatic versus non-metastatic tumors.
methodsCpG-island methylation was evaluated on a 56-gene cancer-specific biomarker microarray in metastatic versus non-metastatic breast cancers in a multi-institutional case series of 123 breast cancer patients. Global statistical modeling and unsupervised hierarchical clustering were applied to identify a multi-gene binary classifier with high sensitivity and specificity. Network analysis was utilized to quantify the connectivity of the identified genes.
resultsSeven genes (BRCA1, DAPK1, MSH2, CDKN2A, PGR, PRKCDBP, RANKL) were found informative for prognosis of metastatic diffusion and were used to calculate classifier accuracy versus the entire data-set. Individual-gene performances showed sensitivities of 63-79 %, 53-84 % specificities, positive predictive values of 59-83 % and negative predictive values of 63-80 %. When modelled together, these seven genes reached a sensitivity of 93 %, 100 % specificity, a positive predictive value of 100 % and a negative predictive value of 93 %, with high statistical power. Unsupervised hierarchical clustering independently confirmed these findings, in close agreement with the accuracy measurements. Network analyses indicated tight interrelationship between the identified genes, suggesting this to be a functionally-coordinated module, linked to breast cancer progression.
conclusionsOur findings identify CpG-island methylation profiles with deep impact on clinical outcome, paving the way for use as novel prognostic assays in clinical settings.
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