ArticleBMC medical genomics2017
Identification of prognostic signature in cancer based on DNA methylation interaction network.
Article in BMC medical genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- IMI-driver: Integrating multi-level gene networks and multi-omics for cancer driver gene identification.PLoS computational biology · 2024Article
- Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach.The EPMA journal · 2024Article
- Pancancer analysis of oncogenic BARX2 identifying its prognostic value and immunological function in liver hepatocellular carcinoma.Scientific reports · 2023Article
- Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs.Life (Basel, Switzerland) · 2022Article
- Computational Analysis of High-Dimensional DNA Methylation Data for Cancer Prognosis.Journal of computational biology : a journal of computational molecular cell biology · 2022Review
- Review
- Heterogeneity Analysis of Bladder Cancer Based on DNA Methylation Molecular Profiling.Frontiers in oncology · 2022Article
- Pan-cancer evaluation of gene expression and somatic alteration data for cancer prognosis prediction.BMC cancer · 2021Article
- DNA methylation subtypes for ovarian cancer prognosis.FEBS open bio · 2021Article
- The epigenetic silencing of microRNA-433 facilitates the malignant phenotypes of non-small cell lung cancer by targeting CREB1.American journal of translational research · 2021Article
- Identification of prognostic signature of non-small cell lung cancer based on TCGA methylation data.Scientific reports · 2020Article
- Aberrant Methylation and Differential Expression of SLC2A1, TNS4, GAPDH, ATP8A2, and CASZ1 Are Associated with the Prognosis of Lung Adenocarcinoma.BioMed research international · 2020Article
- Article
- Identification of Cancer Hallmarks Based on the Gene Co-expression Networks of Seven Cancers.Frontiers in genetics · 2019Article
- miR‑3666 suppresses cellular proliferation and invasion in colorectal cancer by targeting SATB2.Molecular medicine reports · 2018Article
- Specific breast cancer prognosis-subtype distinctions based on DNA methylation patterns.Molecular oncology · 2018Article
- A bioinformatics potpourri.BMC genomics · 2018Article
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2 authors.
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Abstract
backgroundThe identification of prognostic biomarkers for cancer patients is essential for cancer research. These days, DNA methylation has been proved to be associated with cancer prognosis. However, there are few methods which identify the prognostic markers based on DNA methylation data systematically, especially considering the interaction among DNA methylation sites.
methodsIn this paper, we first evaluated the stabilities of microRNA, mRNA, and DNA methylation data in prognosis of cancer. After that, a rank-based method was applied to construct a DNA methylation interaction network. In this network, nodes with the largest degrees (10% of all the nodes) were selected as hubs. Cox regression was applied to select the hubs as prognostic signature. In this prognostic signature, DNA methylation levels of each DNA methylation site are correlated with the outcomes of cancer patients. After obtaining these prognostic genes, we performed the survival analysis in the training group and the test group to verify the reliability of these genes.
resultsWe applied our method in three cancers (ovarian cancer, breast cancer and Glioblastoma Multiforme). In all the three cancers, there are more common ones of prognostic genes selected from different samples in DNA methylation data, compared with gene expression data and miRNA expression data, which indicates the DNA methylation data may be more stable in cancer prognosis. Power-law distribution fitting suggests that the DNA methylation interaction networks are scale-free. And the hubs selected from the three networks are all enriched by cancer related pathways. The gene signatures were obtained for the three cancers respectively, and survival analysis shows they can distinguish the outcomes of tumor patients in both the training data sets and test data sets, which outperformed the control signatures.
conclusionsA computational method was proposed to construct DNA methylation interaction network and this network could be used to select prognostic signatures in cancer.
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