ArticleJournal of Cancer2017
Network Biomarkers Constructed from Gene Expression and Protein-Protein Interaction Data for Accurate Prediction of Leukemia.
Article in Journal of Cancer, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Improving the Stability of Protein-Protein Interaction Assay FlimPIA Using a Thermostabilized Firefly Luciferase.Frontiers in bioengineering and biotechnology · 2021Article
- Single-cell network biology for resolving cellular heterogeneity in human diseases.Experimental & molecular medicine · 2020Review
- Network-based identification of biomarkers for colon adenocarcinoma.BMC cancer · 2020Article
- Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior.Bayesian analysis · 2020Article
- Article
- A Comprehensive Survey of Tools and Software for Active Subnetwork Identification.Frontiers in genetics · 2019Review
- The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine.Journal of molecular biology · 2018Review
- Towards integrated oncogenic marker recognition through mutual information-based statistically significant feature extraction: an association rule mining based study on cancer expression and methylation profiles.Quantitative biology (Beijing, China) · 2017Article
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Authors and funding
8 authors.
Funding
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Abstract
Leukemia is a leading cause of cancer deaths in the developed countries. Great efforts have been undertaken in search of diagnostic biomarkers of leukemia. However, leukemia is highly complex and heterogeneous, involving interaction among multiple molecular components. Individual molecules are not necessarily sensitive diagnostic indicators. Network biomarkers are considered to outperform individual molecules in disease characterization. We applied an integrative approach that identifies active network modules as putative biomarkers for leukemia diagnosis. We first reconstructed the leukemia-specific PPI network using protein-protein interactions from the Protein Interaction Network Analysis (PINA) and protein annotations from GeneGo. The network was further integrated with gene expression profiles to identify active modules with leukemia relevance. Finally, the candidate network-based biomarker was evaluated for the diagnosing performance. A network of 97 genes and 400 interactions was identified for accurate diagnosis of leukemia. Functional enrichment analysis revealed that the network biomarkers were enriched in pathways in cancer. The network biomarkers could discriminate leukemia samples from the normal controls more effectively than the known biomarkers. The network biomarkers provide a useful tool to diagnose leukemia and also aids in further understanding the molecular basis of leukemia.
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