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标题：Information-theoretic analysis of phenotype changes in early stages of carcinogenesis
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作者：F. Remacle ; Nataly Kravchenko-Balasha ; Alexander Levitzki 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2010
卷号：107
期号：22
页码：10324-10329
DOI：10.1073/pnas.1005283107
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Cancer is a multistep process characterized by altered signal transduction, cell growth, and metabolism. To identify such processes in early carcinogenesis we use an information theoretic approach to characterize gene expression quantified as mRNA levels in primary keratinocytes (K) and human papillomavirus 16 (HPV16)-transformed keratinocytes (HF1 cells) from early (E) and late (L) passages and from benzo(a)pyrene-treated (BP) L cells. Our starting point is that biological signaling processes are subjected to the same quantitative laws as inanimate, nonequilibrium chemical systems. Environmental and genomic constraints thereby limit the maximal thermodynamic entropy that the biological system can reach. The procedure uncovers the changes in gene expression patterns in different networks and defines the significance of each altered network in the establishment of a particular phenotype. The development of transformed HF1 cells is shown to be represented by one major transcription pattern that is important at all times. Two minor transcription patterns are also identified, one that contributes at early times and a distinguishably different pattern that contributes at later times. All three transcription patterns defined by our analysis were validated by gene expression values and biochemical means. The major transcription pattern includes reduced transcripts participating in the apoptotic network and enhanced transcripts participating in cell cycle, glycolysis, and oxidative phosphorylation. The two minor patterns identify genes that are mainly involved in lipid or carbohydrate metabolism.
关键词：microarray analysis ; oncogenic transformation ; surprisal analysis ; maximal entropy ; gene transcription patterns