title: Endogenous network states predict gain or loss of functions for genetic mutations in hepatocellular carcinoma.
abstract: Cancers have been typically characterized by genetic mutations. Patterns of such mutations have traditionally been analysed by posteriori statistical association approaches. One may ponder the possibility of a priori determination of any mutation regularity. Here by exploring biological processes implied in a mechanistic theory recently developed (the endogenous molecular-cellular network theory), we found that the features of genetic mutations in cancers may be predicted without any prior knowledge of mutation propensities. With hepatocellular carcinoma (HCC) as an example, we found that the normal hepatocyte and cancerous hepatocyte can be represented by robust stable states of one single endogenous network. These stable states, specified by distinct patterns of expressions or activities of proteins in the network, provide means to directly identify a set of most probable genetic mutations and their effects in HCC. As the key proteins and main interactions in the network are conserved through cell types in an organism, similar mutational features may also be found in other cancers. This analysis yielded straightforward and testable predictions on accumulated and preferred mutation spectra in normal tissue. The validation of predicted cancer state mutation patterns demonstrates the usefulness and potential of a causal dynamical framework to understand and predict genetic mutations in cancer.
authors.affiliation: Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Gaowei
authors.initials: G
authors.lastname: Wang
authors.affiliation: Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Hang
authors.initials: H
authors.lastname: Su
authors.affiliation: Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Helin
authors.initials: H
authors.lastname: Yu
authors.affiliation: Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Ruoshi
authors.initials: R
authors.lastname: Yuan
authors.affiliation: GenMath, Seattle, WA, USA.
authors.forename: Xiaomei
authors.initials: X
authors.lastname: Zhu
authors.affiliation: Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China State Key Laboratory for Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China aoping@sjtu.edu.cn.
authors.forename: Ping
authors.initials: P
authors.lastname: Ao
mesh_terms: D000818:Animals; D006528:Carcinoma, Hepatocellular / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D053263:Gene Regulatory Networks; D022781:Hepatocytes / Q000378:metabolism / Q000473:pathology; D006801:Humans; D008113:Liver Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D008957:Models, Genetic; D009154:Mutation
keywords: dynamical systems; endogenous network theory; genetic mutations; stable state