Prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networks

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 allos...

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Published inJournal of translational medicine Vol. 19; no. 1; pp. 122 - 17
Main Authors Chen, Yinying, Yang, Wei, Chen, Qilong, Liu, Qiong, Liu, Jun, Zhang, Yingying, Li, Bing, Li, Dongfeng, Nan, Jingyi, Li, Xiaodong, Wu, Huikun, Xiang, Xinghua, Peng, Yehui, Wang, Jie, Su, Shibing, Wang, Zhong
Format Journal Article
LanguageEnglish
Published London BioMed Central 23.03.2021
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects
Algorithms
Allosteric properties
Animals
Biomedical and Life Sciences
Biomedicine
Carcinoma, Hepatocellular - genetics
Chronic hepatitis B (CHB)
Chronic liver disease
Cirrhosis
Classification
Complications and side effects
Computational Modelling and Epidemiology
CYP1A2 protein
Cytochrome P450
Data analysis
Development and progression
DNA methylation
DNA microarrays
Dynamic modular networks
Gene expression
Genomes
Health aspects
Hepatitis
Hepatitis B
Hepatitis B virus
Hepatitis B, Chronic
Hepatocellular carcinoma
Hepatocellular carcinoma (HCC)
Hepatoma
Humans
Interleukin 6
Liver cancer
Liver Cirrhosis
Liver diseases
Liver Neoplasms - genetics
Medicine/Public Health
Membrane proteins
Physiological aspects
Risk factors
RNA polymerase
Sequential allosteric modules
Software
Zebrafish
China
United States > US
Cirrhosis
Sequential allosteric modules
HCC risk
Chronic hepatitis B (CHB)
Hepatocellular carcinoma (HCC)
Chronic liver disease
Dynamic modular networks
Online AccessGet full text
ISSN1479-5876
1479-5876
DOI10.1186/s12967-021-02791-9

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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 AM s. 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.
AbstractList 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 AM s. 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.
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.BACKGROUNDDiscovering 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.In this study, we utilized a proposed sequential allosteric modules (AMs)-based approach and quantitatively calculated the topological structural variations of these AMs.METHODSIn this study, we utilized a proposed sequential allosteric modules (AMs)-based approach and quantitatively calculated the topological structural variations of these AMs.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.RESULTSWe 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.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.CONCLUSIONSThese 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. Keywords: Chronic liver disease, Hepatocellular carcinoma (HCC), Chronic hepatitis B (CHB), Cirrhosis, Dynamic modular networks, Sequential allosteric modules, HCC risk
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. In this study, we utilized a proposed sequential allosteric modules (AMs)-based approach and quantitatively calculated the topological structural variations of these AMs. 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. 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.
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. In this study, we utilized a proposed sequential allosteric modules (AMs)-based approach and quantitatively calculated the topological structural variations of these AMs. 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. 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.
ArticleNumber 122
Audience Academic
Author Chen, Yinying
Chen, Qilong
Li, Dongfeng
Li, Xiaodong
Zhang, Yingying
Wu, Huikun
Su, Shibing
Li, Bing
Yang, Wei
Wang, Jie
Nan, Jingyi
Liu, Qiong
Xiang, Xinghua
Peng, Yehui
Wang, Zhong
Liu, Jun
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/33757544$$D View this record in MEDLINE/PubMed
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Issue 1
Keywords Cirrhosis
Sequential allosteric modules
HCC risk
Chronic hepatitis B (CHB)
Hepatocellular carcinoma (HCC)
Chronic liver disease
Dynamic modular networks
Language English
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SSID ssj0024549
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Snippet Background Discovering potential predictive risks in the super precarcinomatous phase of hepatocellular carcinoma (HCC) without any clinical manifestations is...
Discovering potential predictive risks in the super precarcinomatous phase of hepatocellular carcinoma (HCC) without any clinical manifestations is impossible...
Background Discovering potential predictive risks in the super precarcinomatous phase of hepatocellular carcinoma (HCC) without any clinical manifestations is...
Abstract Background Discovering potential predictive risks in the super precarcinomatous phase of hepatocellular carcinoma (HCC) without any clinical...
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StartPage 122
SubjectTerms Algorithms
Allosteric properties
Animals
Biomedical and Life Sciences
Biomedicine
Carcinoma, Hepatocellular - genetics
Chronic hepatitis B (CHB)
Chronic liver disease
Cirrhosis
Classification
Complications and side effects
Computational Modelling and Epidemiology
CYP1A2 protein
Cytochrome P450
Data analysis
Development and progression
DNA methylation
DNA microarrays
Dynamic modular networks
Gene expression
Genomes
Health aspects
Hepatitis
Hepatitis B
Hepatitis B virus
Hepatitis B, Chronic
Hepatocellular carcinoma
Hepatocellular carcinoma (HCC)
Hepatoma
Humans
Interleukin 6
Liver cancer
Liver Cirrhosis
Liver diseases
Liver Neoplasms - genetics
Medicine/Public Health
Membrane proteins
Physiological aspects
Risk factors
RNA polymerase
Sequential allosteric modules
Software
Zebrafish
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Title Prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networks
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