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Developing effective and non-invasive biomarkers of HCC for individual patients remains an urgent task for early diagnosis and convenient monitoring. Analyzing the transcriptomic profiles of peripheral blood mononuclear cells from both healthy donors and patients with chronic HBV infection in different states (i.e. HBV carrier, chronic hepatitis B, cirrhosis, and HCC), we identified a set of 19 candidate genes according to our algorithm of dynamic network biomarkers. These genes can both characterize different stages during HCC progression and identify cirrhosis as the critical transition stage before carcinogenesis. The interaction effects (i.e. co-expressions) of candidate genes were used to build an accurate prediction model: the so-called edge-based biomarker. Considering the convenience and robustness of biomarkers in clinical applications, we performed functional analysis, validated candidate genes in other independent samples of our collected cohort, and finally selected COL5A1, HLA-DQB1, MMP2, and CDK4 to build edge panel as prediction models. We demonstrated that the edge panel had great performance in both diagnosis and prognosis in terms of precision and specificity for HCC, especially for patients with alpha-fetoprotein-negative HCC. Our study not only provides a novel edge-based biomarker for non-invasive and effective diagnosis of HBV-associated HCC to each individual patient but also introduces a new way to integrate the interaction terms of individual molecules for clinical diagnosis and prognosis from the network and dynamics perspectives.<\/jats:p>","DOI":"10.1093\/jmcb\/mjz025","type":"journal-article","created":{"date-parts":[[2019,3,24]],"date-time":"2019-03-24T20:05:50Z","timestamp":1553457950000},"page":"665-677","source":"Crossref","is-referenced-by-count":20,"title":["Dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing"],"prefix":"10.1093","volume":"11","author":[{"given":"Yiyu","family":"Lu","sequence":"first","affiliation":[{"name":"Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 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Hospital, Qidong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuefeng","family":"Xue","sequence":"additional","affiliation":[{"name":"Qidong Liver Cancer Institute, Qidong People\u2019s Hospital, Qidong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiyang","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Liver Disease, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luonan","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institute of Biological Sciences, Chinese Academy of Sciences, Shanghai, China"},{"name":"Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China"},{"name":"School of Life Science and 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