Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning
Background: Early detection of complex diseases like hepatocellular carcinoma remains challenging due to their network-driven pathology. Dynamic network biomarkers (DNB) based on monitoring changes in molecular correlations may enable earlier predictions. However, DNB analysis often overlooks diseas...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037023002374 |
| _version_ | 1797384058141933568 |
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| author | Yukun Han Javed Akhtar Guozhen Liu Chenzhong Li Guanyu Wang |
| author_facet | Yukun Han Javed Akhtar Guozhen Liu Chenzhong Li Guanyu Wang |
| author_sort | Yukun Han |
| collection | DOAJ |
| description | Background: Early detection of complex diseases like hepatocellular carcinoma remains challenging due to their network-driven pathology. Dynamic network biomarkers (DNB) based on monitoring changes in molecular correlations may enable earlier predictions. However, DNB analysis often overlooks disease heterogeneity. Methods: We integrated DNB analysis with graph convolutional neural networks (GCN) to identify critical transitions during hepatocellular carcinoma development in a mouse model. A DNB-GCN model was constructed using transcriptomic data and gene expression levels as node features. Results: DNB analysis identified a critical transition point at 7 weeks of age despite histological examinations being unable to detect cancerous changes at that time point. The DNB-GCN model achieved 100% accuracy in classifying healthy and cancerous mice, and was able to accurately predict the health status of newly introduced mice. Conclusion: The integration of DNB analysis and GCN demonstrates potential for the early detection of complex diseases by capturing network structures and molecular features that conventional biomarker discovery methods overlook. The approach warrants further development and validation. |
| first_indexed | 2024-03-08T21:30:00Z |
| format | Article |
| id | doaj.art-43cd396083e34d6a8d8d989be73ed853 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-03-08T21:30:00Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-43cd396083e34d6a8d8d989be73ed8532023-12-21T07:31:44ZengElsevierComputational and Structural Biotechnology Journal2001-03702023-01-012134783489Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learningYukun Han0Javed Akhtar1Guozhen Liu2Chenzhong Li3Guanyu Wang4Institute of Modern Biology, Nanjing University, Nanjing 210023, China; Guangdong Provincial Key Laboratory of Computational Science and Material Design, Shenzhen 518055, China; Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, ChinaBiomedical Science and Engineering, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China; Center for Endocrinology and Metabolic Diseases, Second Affiliated Hospital, The Chinese University of Hong Kong, Shenzhen 518172, China; Guangdong Provincial Key Laboratory of Computational Science and Material Design, Shenzhen 518055, China; Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, ChinaBiomedical Science and Engineering, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, ChinaBiomedical Science and Engineering, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, ChinaBiomedical Science and Engineering, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China; Center for Endocrinology and Metabolic Diseases, Second Affiliated Hospital, The Chinese University of Hong Kong, Shenzhen 518172, China; Guangdong Provincial Key Laboratory of Computational Science and Material Design, Shenzhen 518055, China; Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China; Corresponding author at: Biomedical Science and Engineering, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China.Background: Early detection of complex diseases like hepatocellular carcinoma remains challenging due to their network-driven pathology. Dynamic network biomarkers (DNB) based on monitoring changes in molecular correlations may enable earlier predictions. However, DNB analysis often overlooks disease heterogeneity. Methods: We integrated DNB analysis with graph convolutional neural networks (GCN) to identify critical transitions during hepatocellular carcinoma development in a mouse model. A DNB-GCN model was constructed using transcriptomic data and gene expression levels as node features. Results: DNB analysis identified a critical transition point at 7 weeks of age despite histological examinations being unable to detect cancerous changes at that time point. The DNB-GCN model achieved 100% accuracy in classifying healthy and cancerous mice, and was able to accurately predict the health status of newly introduced mice. Conclusion: The integration of DNB analysis and GCN demonstrates potential for the early detection of complex diseases by capturing network structures and molecular features that conventional biomarker discovery methods overlook. The approach warrants further development and validation.http://www.sciencedirect.com/science/article/pii/S2001037023002374Hepatocellular carcinomaLatency detectionDynamic network biomarkersEarly warning of diseasesGraph convolutional neural networks |
| spellingShingle | Yukun Han Javed Akhtar Guozhen Liu Chenzhong Li Guanyu Wang Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning Computational and Structural Biotechnology Journal Hepatocellular carcinoma Latency detection Dynamic network biomarkers Early warning of diseases Graph convolutional neural networks |
| title | Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning |
| title_full | Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning |
| title_fullStr | Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning |
| title_full_unstemmed | Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning |
| title_short | Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning |
| title_sort | early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning |
| topic | Hepatocellular carcinoma Latency detection Dynamic network biomarkers Early warning of diseases Graph convolutional neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S2001037023002374 |
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