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Novel structural co-expression analysis linking the NPM1-associated ribosomal biogenesis network to chronic myelogenous leukemia.


ABSTRACT: Co-expression analysis reveals useful dysregulation patterns of gene cooperativeness for understanding cancer biology and identifying new targets for treatment. We developed a structural strategy to identify co-expressed gene networks that are important for chronic myelogenous leukemia (CML). This strategy compared the distributions of expressional correlations between CML and normal states, and it identified a data-driven threshold to classify strongly co-expressed networks that had the best coherence with CML. Using this strategy, we found a transcriptome-wide reduction of co-expression connectivity in CML, reflecting potentially loosened molecular regulation. Conversely, when we focused on nucleophosmin 1 (NPM1) associated networks, NPM1 established more co-expression linkages with BCR-ABL pathways and ribosomal protein networks in CML than normal. This finding implicates a new role of NPM1 in conveying tumorigenic signals from the BCR-ABL oncoprotein to ribosome biogenesis, affecting cellular growth. Transcription factors may be regulators of the differential co-expression patterns between CML and normal.

SUBMITTER: Chan LW 

PROVIDER: S-EPMC4513283 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Novel structural co-expression analysis linking the NPM1-associated ribosomal biogenesis network to chronic myelogenous leukemia.

Chan Lawrence W C LW   Lin Xihong X   Yung Godwin G   Lui Thomas T   Chiu Ya Ming YM   Wang Fengfeng F   Tsui Nancy B Y NB   Cho William C S WC   Yip S P SP   Siu Parco M PM   Wong S C Cesar SC   Yung Benjamin Y M BY  

Scientific reports 20150724


Co-expression analysis reveals useful dysregulation patterns of gene cooperativeness for understanding cancer biology and identifying new targets for treatment. We developed a structural strategy to identify co-expressed gene networks that are important for chronic myelogenous leukemia (CML). This strategy compared the distributions of expressional correlations between CML and normal states, and it identified a data-driven threshold to classify strongly co-expressed networks that had the best co  ...[more]

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