TY - JOUR
T1 - Proteomics, pathway array and signaling network-based medicine in cancer
AU - Zhang, David Y.
AU - Ye, Fei
AU - Gao, Ling
AU - Liu, Xiaoliang
AU - Zhao, Xin
AU - Che, Yufang
AU - Wang, Hongxia
AU - Wang, Libo
AU - Wu, Josephine
AU - Song, Dong
AU - Liu, Wei
AU - Xu, Hong
AU - Jiang, Bo
AU - Zhang, Weijia
AU - Wang, Jinhua
AU - Lee, Peng
N1 - Funding Information:
This work is supported by the Susan Komen Breast Cancer Foundation and the Department of Defense Breast Cancer Research Program grants to PL
PY - 2009/10/28
Y1 - 2009/10/28
N2 - Cancer is a multifaceted disease that results from dysregulated normal cellular signaling networks caused by genetic, genomic and epigenetic alterations at cell or tissue levels. Uncovering the underlying protein signaling network changes, including cell cycle gene networks in cancer, aids in understanding the molecular mechanism of carcinogenesis and identifies the characteristic signaling network signatures unique for different cancers and specific cancer subtypes. The identified signatures can be used for cancer diagnosis, prognosis, and personalized treatment. During the past several decades, the available technology to study signaling networks has significantly evolved to include such platforms as genomic microarray (expression array, SNP array, CGH array, etc.) and proteomic analysis, which globally assesses genetic, epigenetic, and proteomic alterations in cancer. In this review, we compared Pathway Array analysis with other proteomic approaches in analyzing protein network involved in cancer and its utility serving as cancer biomarkers in diagnosis, prognosis and therapeutic target identification. With the advent of bioinformatics, constructing high complexity signaling networks is possible. As the use of signaling network-based cancer diagnosis, prognosis and treatment is anticipated in the near future, medical and scientific communities should be prepared to apply these techniques to further enhance personalized medicine.
AB - Cancer is a multifaceted disease that results from dysregulated normal cellular signaling networks caused by genetic, genomic and epigenetic alterations at cell or tissue levels. Uncovering the underlying protein signaling network changes, including cell cycle gene networks in cancer, aids in understanding the molecular mechanism of carcinogenesis and identifies the characteristic signaling network signatures unique for different cancers and specific cancer subtypes. The identified signatures can be used for cancer diagnosis, prognosis, and personalized treatment. During the past several decades, the available technology to study signaling networks has significantly evolved to include such platforms as genomic microarray (expression array, SNP array, CGH array, etc.) and proteomic analysis, which globally assesses genetic, epigenetic, and proteomic alterations in cancer. In this review, we compared Pathway Array analysis with other proteomic approaches in analyzing protein network involved in cancer and its utility serving as cancer biomarkers in diagnosis, prognosis and therapeutic target identification. With the advent of bioinformatics, constructing high complexity signaling networks is possible. As the use of signaling network-based cancer diagnosis, prognosis and treatment is anticipated in the near future, medical and scientific communities should be prepared to apply these techniques to further enhance personalized medicine.
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U2 - 10.1186/1747-1028-4-20
DO - 10.1186/1747-1028-4-20
M3 - Review article
C2 - 19863813
AN - SCOPUS:71049131644
SN - 1747-1028
VL - 4
JO - Cell Division
JF - Cell Division
M1 - 20
ER -