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J Biol Chem ; 291(29): 14927-38, 2016 07 15.
Article in English | MEDLINE | ID: mdl-27226542

ABSTRACT

Tumor development driven by inflammation is now an established phenomenon, but the role that complement plays remains uncertain. Recent evidence has suggested that various components of the complement (C) cascade may influence tumor development in disparate ways; however, little attention has been paid to that of the membrane attack complex (MAC). This is despite abundant evidence documenting the effects of this complex on cell behavior, including cell activation, protection from/induction of apoptosis, release of inflammatory cytokines, growth factors, and ECM components and regulators, and the triggering of the NLRP3 inflammasome. Here we present a novel approach to this issue by using global gene expression studies in conjunction with a systems biology analysis. Using network analysis of MAC-responsive expression changes, we demonstrate a cluster of co-regulated genes known to have impact in the extracellular space and on the supporting stroma and with well characterized tumor-promoting roles. Network analysis highlighted the central role for EGF receptor activation in mediating the observed responses to MAC exposure. Overall, the study sheds light on the mechanisms by which sublytic MAC causes tumor cell responses and exposes a gene expression signature that implicates MAC as a driver of tumor progression. These findings have implications for understanding of the roles of complement and the MAC in tumor development and progression, which in turn will inform future therapeutic strategies in cancer.


Subject(s)
Carcinogenesis/immunology , Complement Membrane Attack Complex/immunology , Algorithms , Animals , Carcinogenesis/genetics , Cell Line, Tumor , Cell Transformation, Neoplastic/genetics , Cell Transformation, Neoplastic/immunology , Complement Membrane Attack Complex/genetics , Gene Expression Profiling , Gene Regulatory Networks , Humans , Models, Immunological , Real-Time Polymerase Chain Reaction , Systems Biology
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