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Pan-cancer network disorders revealed by overall and local signaling entropy

摘要Tumor development is a process involving loss of the differentiation phenotype and acquisition of stem-like characteristics,which is driven by intracellular rewiring of signaling network.The measurement of network reprogramming and disorder would be challenging due to the complexity and heterogeneity of tumors.Here,we proposed signaling entropy (SR) to assess the degree of tumor network disorder.We calculated SR for 33 tumor types in The Cancer Genome Atlas database based on transcrip-tomic and proteomic data.The SR of tumors was significantly higher than that of normal samples and was highly correlated with cell stemness,cancer type,tumor grade,and metastasis.We further demonstrated the sensitivity and accuracy of using local SR in prognosis prediction and drug response evaluation.Overall,SR could reveal cancer network disorders related to tumor malignant potency,clinical prognosis,and drug response.

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作者 Li Feng [1] Yi-Di Sun [2] Chen Li [3] Yi-Xue Li [4] Luo-Nan Chen [5] Rong Zeng [5] 学术成果认领
作者单位 CAS Key Laboratory of Systems Biology,CAS Center for Excellence in Molecular Cell Science,Institute of Biochemistry and Cell Biology,Chinese Academy of Sciences,Shanghai 200031,China;University of Chinese Academy of Sciences,Shanghai 200031,China [1] Institute of Neuroscience,CAS Center for Excellence in Brain Science and intelligence Technology,Chinese Academy of Sciences,Shanghai 200031,China [2] Center for Single-Cell Omics,School of Public Health,Shanghai Jiao Tong University School of Medicine,Shanghai 200025,China [3] Bio-Med Big Data Center,CAS Key Laboratory of Computational Biology,CAS-MPG Partner Institute for Computational Biology,Shanghai Institute of Nutrition and Health,University of Chinese Academy of Sciences,Chinese Academy of Sciences,Shanghai 200031,China [4] CAS Key Laboratory of Systems Biology,CAS Center for Excellence in Molecular Cell Science,Institute of Biochemistry and Cell Biology,Chinese Academy of Sciences,Shanghai 200031,China;University of Chinese Academy of Sciences,Shanghai 200031,China;CAS Key Laboratory of Systems Biology,Hangzhou Institute for Advanced Study,University of Chinese Academy of Sciences,Chinese Academy of Sciences,Hangzhou 310024,China [5]
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DOI 10.1093/jmcb/mjab031
发布时间 2022-01-19(万方平台首次上网日期,不代表论文的发表时间)
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