Multilevel omic data clustering reveals variable contribution of methylator phenotype to integrative cancer subtypes
| Autor: | Pawel Karpinski, Wojciech Kielan, Árpád V. Patai, Izabela Laczmanska, Maria M. Sasiadek, Wojciech Hap |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Cancer Research CpG Island Methylator Phenotype DNA Copy Number Variations Gene Expression Profiling Gene sets Computational biology Biology DNA Methylation Omics Survival Analysis digestive system diseases 03 medical and health sciences 030104 developmental biology Cancer genome Neoplasms Consensus clustering DNA methylation Genetics Cluster Analysis Humans CpG Islands Cluster analysis neoplasms Methylator phenotype |
| Zdroj: | Epigenomics. 10(10) |
| ISSN: | 1750-192X |
| Popis: | Aim: We aimed to assess to what extent CpG island methylator phenotype (CIMP) contributes to cancer subtypes obtained by multilevel omic data analysis. Materials & methods: 16 The Cancer Genome Atlas datasets encompassing three data layers in 4688 tumor samples were analyzed. We identified cancer integrative subtypes (ISs) by the use of similarity network fusion and consensus clustering. CIMP high (CIMP-H) associated ISs were profiled by gene sets and transcriptional regulators enrichment analysis. Results & conclusion: In nine out of 16 cancer datasets CIMP-H clusters significantly overlaped with unique ISs. The contribution of CIMP-H on integrative molecular profiling is variable; therefore, only in a subset of cancer types does CIMP-H contribute to homogenous integrative subtype. CIMP-H associated ISs are heterogenous groups with regard to deregulated pathways and transcriptional regulators. |
| Databáze: | OpenAIRE |
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