TY - JOUR
T1 - Integrated approach toward the discovery of glyco-biomarkers of inflammation-Related diseases
AU - Angata, Takashi
AU - Fujinawa, Reiko
AU - Kurimoto, Ayako
AU - Nakajima, Kazuki
AU - Kato, Masaki
AU - Takamatsu, Shinji
AU - Korekane, Hiroaki
AU - Gao, Cong Xiao
AU - Ohtsubo, Kazuaki
AU - Kitazume, Shinobu
AU - Taniguchi, Naoyuki
PY - 2012/4
Y1 - 2012/4
N2 - Glycobiology has contributed tremendously to the discovery and characterization of cancer-related biomarkers containing glycans (i.e., glyco-biomarkers) and a more detailed understanding of cancer biology. It is now recognized that most chronic diseases involve some elements of chronic inflammation; these include cancer, Alzheimer's disease, and metabolic syndrome (including consequential diabetes mellitus and cardiovascular diseases). By extending the knowledge and experience of the glycobiology community regarding cancer biomarker discovery, we should be able to contribute to the discovery of diagnostic/prognostic glyco-biomarkers of other chronic diseases that involve chronic inflammation. Future integration of large-scale "omics"-type data (e.g., genomics, epigenomics, transcriptomics, proteomics, and glycomics) with computational model building, or a systems glycobiology approach, will facilitate such efforts.
AB - Glycobiology has contributed tremendously to the discovery and characterization of cancer-related biomarkers containing glycans (i.e., glyco-biomarkers) and a more detailed understanding of cancer biology. It is now recognized that most chronic diseases involve some elements of chronic inflammation; these include cancer, Alzheimer's disease, and metabolic syndrome (including consequential diabetes mellitus and cardiovascular diseases). By extending the knowledge and experience of the glycobiology community regarding cancer biomarker discovery, we should be able to contribute to the discovery of diagnostic/prognostic glyco-biomarkers of other chronic diseases that involve chronic inflammation. Future integration of large-scale "omics"-type data (e.g., genomics, epigenomics, transcriptomics, proteomics, and glycomics) with computational model building, or a systems glycobiology approach, will facilitate such efforts.
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U2 - 10.1111/j.1749-6632.2012.06469.x
DO - 10.1111/j.1749-6632.2012.06469.x
M3 - Review article
C2 - 22380786
AN - SCOPUS:84860231407
SN - 0077-8923
VL - 1253
SP - 159
EP - 169
JO - Annals of the New York Academy of Sciences
JF - Annals of the New York Academy of Sciences
IS - 1
ER -
