Yinying Chen1,2,3, Wei Yang2, Qilong Chen4, Qiong Liu2, Jun Liu2, Yingying Zhang2, Bing Li2, Dongfeng Li5, Jingyi Nan6, Xiaodong Li7, Huikun Wu7, Xinghua Xiang8, Yehui Peng8, Jie Wang9, Shibing Su10, Zhong Wang11. 1. Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5 Beixian Ge, Xicheng District, Beijing, 100053, China. 2. Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimen, Beijing, 100700, China. 3. Postdoctoral Research Station, China Academy of Chinese Medical Sciences, Beijing, China. 4. Research Center for Traditional Chinese Medicine Complexity System, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, 201203, China. 5. School of Mathematical Sciences, Peking University, Beijing, China. 6. Shandong Danhong Pharmaceutical Co. Ltd., Heze, China. 7. Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China. 8. School of Mathematics and Computational Science, Hunan University of Science and Technology, Xiangtan, China. 9. Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5 Beixian Ge, Xicheng District, Beijing, 100053, China. wangjie0103@126.com. 10. Research Center for Traditional Chinese Medicine Complexity System, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, 201203, China. shibingsu07@163.com. 11. Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimen, Beijing, 100700, China. zhonw@vip.sina.com.
Abstract
BACKGROUND: Discovering potential predictive risks in the super precarcinomatous phase of hepatocellular carcinoma (HCC) without any clinical manifestations is impossible under normal paradigm but critical to control this complex disease. METHODS: In this study, we utilized a proposed sequential allosteric modules (AMs)-based approach and quantitatively calculated the topological structural variations of these AMs. RESULTS: We found the total of 13 oncogenic allosteric modules (OAMs) among chronic hepatitis B (CHB), cirrhosis and HCC network used SimiNEF. We obtained the 11 highly correlated gene pairs involving 15 genes (r > 0.8, P < 0.001) from the 12 OAMs (the out-of-bag (OOB) classification error rate < 0.5) partial consistent with those in independent clinical microarray data, then a three-gene set (cyp1a2-cyp2c19-il6) was optimized to distinguish HCC from non-tumor liver tissues using random forests with an average area under the curve (AUC) of 0.973. Furthermore, we found significant inhibitory effect on the tumor growth of Bel-7402, Hep 3B and Huh7 cell lines in zebrafish treated with the compounds affected those three genes. CONCLUSIONS: These findings indicated that the sequential AMs-based approach could detect HCC risk in the patients with chronic liver disease and might be applied to any time-dependent risk of cancer.
BACKGROUND: Discovering potential predictive risks in the super precarcinomatous phase of hepatocellular carcinoma (HCC) without any clinical manifestations is impossible under normal paradigm but critical to control this complex disease. METHODS: In this study, we utilized a proposed sequential allosteric modules (AMs)-based approach and quantitatively calculated the topological structural variations of these AMs. RESULTS: We found the total of 13 oncogenic allosteric modules (OAMs) among chronic hepatitis B (CHB), cirrhosis and HCC network used SimiNEF. We obtained the 11 highly correlated gene pairs involving 15 genes (r > 0.8, P < 0.001) from the 12 OAMs (the out-of-bag (OOB) classification error rate < 0.5) partial consistent with those in independent clinical microarray data, then a three-gene set (cyp1a2-cyp2c19-il6) was optimized to distinguish HCC from non-tumor liver tissues using random forests with an average area under the curve (AUC) of 0.973. Furthermore, we found significant inhibitory effect on the tumor growth of Bel-7402, Hep 3B and Huh7 cell lines in zebrafish treated with the compounds affected those three genes. CONCLUSIONS: These findings indicated that the sequential AMs-based approach could detect HCC risk in the patients with chronic liver disease and might be applied to any time-dependent risk of cancer.
Authors: Sourav Bhattacharya; Robert Steele; Shubham Shrivastava; Sounak Chakraborty; Adrian M Di Bisceglie; Ratna B Ray Journal: Am J Pathol Date: 2016-02 Impact factor: 4.307
Authors: Vincent Wai-Sun Wong; Jun Yu; Alfred Sze-Lok Cheng; Grace Lai-Hung Wong; Hoi-Yun Chan; Eagle Siu-Hong Chu; Enders Kai-On Ng; Francis Ka-Leung Chan; Joseph Jao-Yao Sung; Henry Lik-Yuen Chan Journal: Int J Cancer Date: 2009-06-15 Impact factor: 7.396