Prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networks
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 sequent...
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doaj-28ab375fb8114da9a700a3e2a14e974d2021-03-28T11:10:58ZengBMCJournal of Translational Medicine1479-58762021-03-0119111710.1186/s12967-021-02791-9Prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networksYinying Chen0Wei Yang1Qilong Chen2Qiong Liu3Jun Liu4Yingying Zhang5Bing Li6Dongfeng Li7Jingyi Nan8Xiaodong Li9Huikun Wu10Xinghua Xiang11Yehui Peng12Jie Wang13Shibing Su14Zhong Wang15Guang’anmen Hospital, China Academy of Chinese Medical SciencesInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical SciencesResearch Center for Traditional Chinese Medicine Complexity System, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese MedicineInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical SciencesInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical SciencesInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical SciencesInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical SciencesSchool of Mathematical Sciences, Peking UniversityShandong Danhong Pharmaceutical Co. Ltd.Hubei Provincial Hospital of Traditional Chinese MedicineHubei Provincial Hospital of Traditional Chinese MedicineSchool of Mathematics and Computational Science, Hunan University of Science and TechnologySchool of Mathematics and Computational Science, Hunan University of Science and TechnologyGuang’anmen Hospital, China Academy of Chinese Medical SciencesResearch Center for Traditional Chinese Medicine Complexity System, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese MedicineInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical SciencesAbstract 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.https://doi.org/10.1186/s12967-021-02791-9Chronic liver diseaseHepatocellular carcinoma (HCC)Chronic hepatitis B (CHB)CirrhosisDynamic modular networksSequential allosteric modules |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yinying Chen Wei Yang Qilong Chen Qiong Liu Jun Liu Yingying Zhang Bing Li Dongfeng Li Jingyi Nan Xiaodong Li Huikun Wu Xinghua Xiang Yehui Peng Jie Wang Shibing Su Zhong Wang |
spellingShingle |
Yinying Chen Wei Yang Qilong Chen Qiong Liu Jun Liu Yingying Zhang Bing Li Dongfeng Li Jingyi Nan Xiaodong Li Huikun Wu Xinghua Xiang Yehui Peng Jie Wang Shibing Su Zhong Wang Prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networks Journal of Translational Medicine Chronic liver disease Hepatocellular carcinoma (HCC) Chronic hepatitis B (CHB) Cirrhosis Dynamic modular networks Sequential allosteric modules |
author_facet |
Yinying Chen Wei Yang Qilong Chen Qiong Liu Jun Liu Yingying Zhang Bing Li Dongfeng Li Jingyi Nan Xiaodong Li Huikun Wu Xinghua Xiang Yehui Peng Jie Wang Shibing Su Zhong Wang |
author_sort |
Yinying Chen |
title |
Prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networks |
title_short |
Prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networks |
title_full |
Prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networks |
title_fullStr |
Prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networks |
title_full_unstemmed |
Prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networks |
title_sort |
prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networks |
publisher |
BMC |
series |
Journal of Translational Medicine |
issn |
1479-5876 |
publishDate |
2021-03-01 |
description |
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. |
topic |
Chronic liver disease Hepatocellular carcinoma (HCC) Chronic hepatitis B (CHB) Cirrhosis Dynamic modular networks Sequential allosteric modules |
url |
https://doi.org/10.1186/s12967-021-02791-9 |
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