Table 3 Examples of epigenetics and network-oriented analysis in cancer and CVD susceptibility

CVDs

WGCNA,

Comb-p

Discovery set

2129 women from the WHI;

Replication set:

2726 subjects from the FHS

Blood

To construct a DNA methylation-oriented network and analyze possible relationships with incident CHD

HumanMethylation450 microarray

DMRs annotated to SLC9A1, SLC1A5 and TNRC6C strongly correlated with incident CHD

Westerman et al. [137]

Co-variation of enhancer activity and gene expression across study participants and GO enrichment

10 end-stage PAH patients at time of lung explant and 9 unused donor control subjects

PAECs

To construct a regulatory network based on TF-H3K27ac enhancer relationship

ChIP-Seq, Illumina RNA-Seq

A remodeling of active (H3K27ac) enhancers combined with differential transcription factors may guide a dysregulated angiogenesis and endothelial-to-mesenchymal-transition

Reyes-Palomares et al. [112]

Cancer

NcADMM algorithm;

LLR

562 TCGA ovarian cancer

Online data

To construct a DNA methylation-oriented network

of TCGA ovarian cancer

Illumina Infinium HumanMethylation27 platform;

Affymetrix HT-HGU133A platform

Identified the path associated with CCNE1, AURKA and RAB25 mediated by DNA methylation

https://doi.org/10.38/s41598-019-42010-6

GSEA, MSigDB, FEM

64endometrial cancer tissue and 23 healthy control samples

Endometrial tissue

To study new methylated biomarker test to distinguish endometrial cancers from non-cancers

Illumina Infinium HumanMethylation27K BeadChip

HAND2 methylation is a common and crucial molecular alteration in endometrial cancer that could potentially be employed as a biomarker

https://doi.org/10.1371/journal.pmed.1001551

WGCNA, GEPIA

201 patients of the TCGA prostate cancer

TCGA database

To build a network analysis correlation of RNA and DNA methylation to identify target therapy

Illumina human methylation 450 platform

This protocol has predicted the FOXD1 as predictor of poor prognosis

https://doi.org/10.2217/epi-2019-0349

WGCNA, ssGSEA, GO and KEGG pathway enrichment

1248 breast cancer patients

TCGA database

To build a DNA methylation and RNA-seq network for Brest cancer stratification patients

RNA-seq and DNA methylation datasets

Stratify breast cancer patients into low- and high-risk groups

https://doi.org/10.1186/s12967-019-2126-6

GEO, MLP

391 patients of 11 different cancer

TCGA database

To construct DNA methylation network and gene expression

Illumina human methylation 450 k BeadChip; Illumina 450 k platform

New application to classified the different cancer type based on DNA methylation levels

32384093

MCODE, K-shell method

780 samples in BRCA, 468 samples in SKCM, and 428 samples in UCEC

TCGA database

To make a DNA methylation data, mRNA expression data and clinical data network

Illumina HumanMethylation 450 K Assay

Identification of gene signatures associated with cancer prognosis

https://doi.org/10.3390/genes10080571

GREAT, LOLA, ENCODE

30 glioblastoma patients

Tissue

To study the genomic location and abundance of 5 hmC in glioblastomas to study the disease progression

IlluminaHumanMethylation450kmanifest, version 0.4.0; IlluminaHumanMethylation450kanno.ilmn12.hg19, version 0.2.1

Identification of a global loss of 5 hmC in glioblastoma compared with healthy prefrontal cortex tissues

https://doi.org/10.1038/ncomms13177

Affymetrix Genome Wide SNP Arrays v6; WGS; ENCODE; HotNet

200 AML

Blood

To make a DNA-methylation network with RNA and microRNA to investigate the AML pathogenesis, classification, and risk stratification

Affymetrix U133 Plus 2 platform; Illumina Infinium HumanMethylation450 BeadChip; Affymetrix SNP Array 6.0; Illumina HiSeq 2000; Illumina GAIIX

Identification of pathway that stratified the AML patients

https://doi.org/10.1056/NEJMoa1301689

WGS; ATAC-seq; WGBS; ENCODE

410 TCGA samples from 23 cancer types

TCGA database

To build DNA regulatory elements and gene promoter network for future integrative gene regulatory analyses

Illumina MiSeq Sequencer;

Identification of transcription factors and enhancers driving molecular subtypes of cancer associated with clinical prognosis

https://doi.org/10.1126/science.aav1898

GREAT; GSEA; ATAC-seq

Mammary tumors from mouse models and human patients

Tissue

To create a network-chromatin accessibility and transcriptional profiling during mammary development to identify factors that mediate cancer cell state interconversions

Illumina HiSeq 2500

Identification of SOX10 that binds the genes that regulate neural crest

https://doi.org/10.1016/j.ccell.2018.08.001

SNF; GO and KEGG pathway enrichment

185pancreatic cancers

Tissue

To build a mRNAs, miRNAs and DNA methylation network for pancreatic cancer patient stratification

Informatic platform

Identification various signaling cascade associated with different tumor subtype

https://doi.org/10.1038/s41598-020-58290-2

scRRBS; NONCODE, ENCODE

26 single cells isolated from a 51-year-old male HCC patient

Tissue

To use a DNA methylation, RNA-seq and CNV network in HCC single cell

scTrio-seq; Illumina HiSeq2000 or HiSeq 2500 Sequencer

Identification of new approach to study the heterogeneity and complexity of cell populations in development and cancer interrogating in the same time the genome, methylome, and transcriptome

https://doi.org/10.1038/cr.2016.23

MACs2; ENCODE; GREAT

CML cells

Cell culture

To build an ATAC-seq and RNA-seq network in single cells

Illumina HiSeq 4000; NextSeq; qRT-PCR

Correlation between GATA and CD24 that induce a high genetic and epigenetic variability, and resistance to imatinib mesylate treatment

28118844

WGBS; WGS; MSigDb

100 castration-resistant prostate metastases

Tissue

Introduction of whole-genome, whole-methylome and whole-transcriptome

sequencing network in metastatic cancer to study the regulatory role of methylation

ChIP–seq; RNA-seq; Illumina Novaseq 6000

Identification of a novel epigenomic

subtype associated with hypermethylation and somatic mutations in TET2, DNMT3B, IDH1, and BRAF

https://doi.org/10.1038/s41588-020-0648-8