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Characterization of age signatures of DNA methylation in normal and cancer tissues from multiple studies.

Kim J, Kim K, Kim H, Yoon G, Lee K - BMC Genomics (2014)

Bottom Line: Genes related to the normal signature were enriched for aging-related gene ontology terms including metabolic processes, immune system processes, and cell proliferation.The related gene products of the normal signature had more than the average number of interacting partners in a protein interaction network and had a tendency not to interact directly with each other.The genomic sequences of the normal signature were well conserved and the age-associated DNAm levels could satisfactorily predict the chronological ages of tissues regardless of tissue type.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Ajou University School of Medicine, Suwon 443-380, South Korea. kiylee@ajou.ac.kr.

ABSTRACT

Background: DNA methylation (DNAm) levels can be used to predict the chronological age of tissues; however, the characteristics of DNAm age signatures in normal and cancer tissues are not well studied using multiple studies.

Results: We studied approximately 4000 normal and cancer samples with multiple tissue types from diverse studies, and using linear and nonlinear regression models identified reliable tissue type-invariant DNAm age signatures. A normal signature comprising 127 CpG loci was highly enriched on the X chromosome. Age-hypermethylated loci were enriched for guanine-and-cytosine-rich regions in CpG islands (CGIs), whereas age-hypomethylated loci were enriched for adenine-and-thymine-rich regions in non-CGIs. However, the cancer signature comprised only 26 age-hypomethylated loci, none on the X chromosome, and with no overlap with the normal signature. Genes related to the normal signature were enriched for aging-related gene ontology terms including metabolic processes, immune system processes, and cell proliferation. The related gene products of the normal signature had more than the average number of interacting partners in a protein interaction network and had a tendency not to interact directly with each other. The genomic sequences of the normal signature were well conserved and the age-associated DNAm levels could satisfactorily predict the chronological ages of tissues regardless of tissue type. Interestingly, the age-associated DNAm increases or decreases of the normal signature were aberrantly accelerated in cancer samples.

Conclusion: These tissue type-invariant DNAm age signatures in normal and cancer can be used to address important questions in developmental biology and cancer research.

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Related in: MedlinePlus

Study overview. (A) Sources of DNAm data with sample information. Eight studies from GEO and five from open TCGA data were included. (B) Identifying an age-associated DNAm signature. Linear and nonlinear regression models using single or combined studies were applied. (C) Age prediction and characterization of identified age-associated signatures. Various analyses using DNAm patterns and distributions, gene ontology, and protein networks in normal and cancer tissues were performed.
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Fig1: Study overview. (A) Sources of DNAm data with sample information. Eight studies from GEO and five from open TCGA data were included. (B) Identifying an age-associated DNAm signature. Linear and nonlinear regression models using single or combined studies were applied. (C) Age prediction and characterization of identified age-associated signatures. Various analyses using DNAm patterns and distributions, gene ontology, and protein networks in normal and cancer tissues were performed.

Mentions: To identify robust age-associated DNAm signatures, we first searched and downloaded various DNAm profiles from diverse studies available in the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/; Figure 1). We then excluded studies without age information or with small numbers of samples (<10). We also excluded samples of diseased tissues other than cancer. It is known that technical bias exists across different array platforms [17], so we considered only the Illumina Infinium HumanMethylation27 Bead Chip array, which was the most widely used among the downloaded profiles. Consequently, we collected DNAm profiles of 2149 samples (1537 disease-free normal and 612 cancer samples) from eight studies available in the GEO. Additionally, we downloaded 1844 publicly available DNAm profiles (275 normal and 1569 cancer samples) of five cancer types (breast, ovarian, glioblastoma, kidney, and colon) evaluated on the same platform from The Cancer Genome Atlas (TCGA) consortium [18–22]. In total, we gathered DNAm profiles of 1812 normal healthy and 2181 cancer samples. These samples included diverse tissue types and exhibited a wide range of ages from 0 to 91 years (Additional file 1: Table S1). We next normalized DNAm levels (range from 0 to 1) using a single beta-score measure that indicates conceptually the normalized levels of DNAm (Methods). The normalized DNAm levels were well correlated between normal or cancer samples, but had higher correlation scores between normal samples (Figure 2A,B), and for both normal and cancer tissues, the DNAm levels in CGI regions of individual samples were much lower than those in non-CGIs (Additional file 2: Figure S1). Moreover, the DNAm levels of normal and cancer tissue showed different patterns depending on the genomic regions. In CGIs, for example, the average DNAm levels were generally higher in cancer than those in normal tissue, except for ovarian cancer samples (Additional file 2: Figure S1).Figure 1


Characterization of age signatures of DNA methylation in normal and cancer tissues from multiple studies.

Kim J, Kim K, Kim H, Yoon G, Lee K - BMC Genomics (2014)

Study overview. (A) Sources of DNAm data with sample information. Eight studies from GEO and five from open TCGA data were included. (B) Identifying an age-associated DNAm signature. Linear and nonlinear regression models using single or combined studies were applied. (C) Age prediction and characterization of identified age-associated signatures. Various analyses using DNAm patterns and distributions, gene ontology, and protein networks in normal and cancer tissues were performed.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4289351&req=5

Fig1: Study overview. (A) Sources of DNAm data with sample information. Eight studies from GEO and five from open TCGA data were included. (B) Identifying an age-associated DNAm signature. Linear and nonlinear regression models using single or combined studies were applied. (C) Age prediction and characterization of identified age-associated signatures. Various analyses using DNAm patterns and distributions, gene ontology, and protein networks in normal and cancer tissues were performed.
Mentions: To identify robust age-associated DNAm signatures, we first searched and downloaded various DNAm profiles from diverse studies available in the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/; Figure 1). We then excluded studies without age information or with small numbers of samples (<10). We also excluded samples of diseased tissues other than cancer. It is known that technical bias exists across different array platforms [17], so we considered only the Illumina Infinium HumanMethylation27 Bead Chip array, which was the most widely used among the downloaded profiles. Consequently, we collected DNAm profiles of 2149 samples (1537 disease-free normal and 612 cancer samples) from eight studies available in the GEO. Additionally, we downloaded 1844 publicly available DNAm profiles (275 normal and 1569 cancer samples) of five cancer types (breast, ovarian, glioblastoma, kidney, and colon) evaluated on the same platform from The Cancer Genome Atlas (TCGA) consortium [18–22]. In total, we gathered DNAm profiles of 1812 normal healthy and 2181 cancer samples. These samples included diverse tissue types and exhibited a wide range of ages from 0 to 91 years (Additional file 1: Table S1). We next normalized DNAm levels (range from 0 to 1) using a single beta-score measure that indicates conceptually the normalized levels of DNAm (Methods). The normalized DNAm levels were well correlated between normal or cancer samples, but had higher correlation scores between normal samples (Figure 2A,B), and for both normal and cancer tissues, the DNAm levels in CGI regions of individual samples were much lower than those in non-CGIs (Additional file 2: Figure S1). Moreover, the DNAm levels of normal and cancer tissue showed different patterns depending on the genomic regions. In CGIs, for example, the average DNAm levels were generally higher in cancer than those in normal tissue, except for ovarian cancer samples (Additional file 2: Figure S1).Figure 1

Bottom Line: Genes related to the normal signature were enriched for aging-related gene ontology terms including metabolic processes, immune system processes, and cell proliferation.The related gene products of the normal signature had more than the average number of interacting partners in a protein interaction network and had a tendency not to interact directly with each other.The genomic sequences of the normal signature were well conserved and the age-associated DNAm levels could satisfactorily predict the chronological ages of tissues regardless of tissue type.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Ajou University School of Medicine, Suwon 443-380, South Korea. kiylee@ajou.ac.kr.

ABSTRACT

Background: DNA methylation (DNAm) levels can be used to predict the chronological age of tissues; however, the characteristics of DNAm age signatures in normal and cancer tissues are not well studied using multiple studies.

Results: We studied approximately 4000 normal and cancer samples with multiple tissue types from diverse studies, and using linear and nonlinear regression models identified reliable tissue type-invariant DNAm age signatures. A normal signature comprising 127 CpG loci was highly enriched on the X chromosome. Age-hypermethylated loci were enriched for guanine-and-cytosine-rich regions in CpG islands (CGIs), whereas age-hypomethylated loci were enriched for adenine-and-thymine-rich regions in non-CGIs. However, the cancer signature comprised only 26 age-hypomethylated loci, none on the X chromosome, and with no overlap with the normal signature. Genes related to the normal signature were enriched for aging-related gene ontology terms including metabolic processes, immune system processes, and cell proliferation. The related gene products of the normal signature had more than the average number of interacting partners in a protein interaction network and had a tendency not to interact directly with each other. The genomic sequences of the normal signature were well conserved and the age-associated DNAm levels could satisfactorily predict the chronological ages of tissues regardless of tissue type. Interestingly, the age-associated DNAm increases or decreases of the normal signature were aberrantly accelerated in cancer samples.

Conclusion: These tissue type-invariant DNAm age signatures in normal and cancer can be used to address important questions in developmental biology and cancer research.

Show MeSH
Related in: MedlinePlus