ArticleOncoTargets and therapy2016
Identifying module biomarkers from gastric cancer by differential correlation network.
Article in OncoTargets and therapy, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Identifying network biomarkers of cancer by sample-specific differential network.BMC bioinformatics · 2022Article
- Identification of HCC-Related Genes Based on Differential Partial Correlation Network.Frontiers in genetics · 2021Article
- Identification of Long Noncoding RNA Biomarkers for Hepatocellular Carcinoma Using Single-Sample Networks.BioMed research international · 2020Article
- Optimizing miRNA-module diagnostic biomarkers of gastric carcinoma via integrated network analysis.PloS one · 2018Article
- A data-driven approach links microglia to pathology and prognosis in amyotrophic lateral sclerosis.Acta neuropathologica communications · 2017Article
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2 authors.
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Abstract
Gastric cancer (stomach cancer) is a severe disease caused by dysregulation of many functionally correlated genes or pathways instead of the mutation of individual genes. Systematic identification of gastric cancer biomarkers can provide insights into the mechanisms underlying this deadly disease and help in the development of new drugs. In this paper, we present a novel network-based approach to predict module biomarkers of gastric cancer that can effectively distinguish the disease from normal samples. Specifically, by assuming that gastric cancer has mainly resulted from dysfunction of biomolecular networks rather than individual genes in an organism, the genes in the module biomarkers are potentially related to gastric cancer. Finally, we identified a module biomarker with 27 genes, and by comparing the module biomarker with known gastric cancer biomarkers, we found that our module biomarker exhibited a greater ability to diagnose the samples with gastric cancer.
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