Identification and comparison of aberrant key regulatory networks in breast, colon, liver, lung, and stomach cancers through methylome database analysis.
Aberrant methylation of specific CpG sites at the promoter is widely responsible for genesis and development of various cancer types. Even though the microarray-based methylome analyzing techniques have contributed to the elucidation of the methylation change at the genome-wide level, the identification of key methylation markers or top regulatory networks appearing common in highly incident cancers through comparison analysis is still limited. In this study, we in silico performed the genome-wide methylation analysis on each 10 sets of normal and cancer pairs of five tissues: breast, colon, liver, lung, and stomach. The methylation array covers 27,578 CpG sites, corresponding to 14,495 genes, and significantly hypermethylated or hypomethylated genes in the cancer were collected (FDR adjusted p-value <0.05; methylation difference >0.3). Analysis of the dataset confirmed the methylation of previously known methylation markers and further identified novel methylation markers, such as GPX2, CLDN15, and KL. Cluster analysis using the methylome dataset resulted in a diagram with a bipartite mode distinguishing cancer cells from normal cells regardless of tissue types. The analysis further revealed that breast cancer was closest with lung cancer, whereas it was farthest from colon cancer. Pathway analysis identified that either the "cancer" related network or the "cancer" related bio-function appeared as the highest confidence in all the five cancers, whereas each cancer type represents its tissue-specific gene sets. Our results contribute toward understanding the essential abnormal epigenetic pathways involved in carcinogenesis. Further, the novel methylation markers could be applied to establish markers for cancer prognosis.
Department of Life Science, Dongguk University-Seoul, Seoul, Korea.
Byungtak
B
Kim
Department of Life Science, Dongguk University-Seoul, Seoul, Korea.
Seongeun
S
Kang
Department of Life Science, Dongguk University-Seoul, Seoul, Korea.
Gookjoo
G
Jeong
Department of Life Science, Dongguk University-Seoul, Seoul, Korea.
Sung-Bin
SB
Park
Department of Life Science, Dongguk University-Seoul, Seoul, Korea.
Sun Jung
SJ
Kim
D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism; D002460:Cell Line; D045744:Cell Line, Tumor; D016000:Cluster Analysis; D003110:Colonic Neoplasms / Q000235:genetics* / Q000378:metabolism; D018899:CpG Islands / Q000235:genetics; D019175:DNA Methylation / Q000502:physiology; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics*; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D008113:Liver Neoplasms / Q000235:genetics* / Q000378:metabolism; D008175:Lung Neoplasms / Q000235:genetics* / Q000378:metabolism; D046228:Microarray Analysis; D060888:Real-Time Polymerase Chain Reaction; D020133:Reverse Transcriptase Polymerase Chain Reaction; D013274:Stomach Neoplasms / Q000235:genetics* / Q000378:metabolism