ArticleJournal of breast cancer2017
Gene Regulatory Network Analysis for Triple-Negative Breast Neoplasms by Using Gene Expression Data.
Article in Journal of breast cancer, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Discovering biomarkers for hormone-dependent tumors: in silico study on signaling pathways implicated in cell cycle and cytoskeleton regulation.Molecular genetics and genomics : MGG · 2022Article
- Modified Immune Evolutionary Algorithm for Medical Data Clustering and Feature Extraction under Cloud Computing Environment.Journal of healthcare engineering · 2020Article
- Condition-adaptive fused graphical lasso (CFGL): An adaptive procedure for inferring condition-specific gene co-expression network.PLoS computational biology · 2018Article
- The Traditional Chinese Medicine Kangai Injection as an Adjuvant Method in Combination with Chemotherapy for the Treatment of Breast Cancer in Chinese Patients: A Meta-Analysis.Evidence-based complementary and alternative medicine : eCAM · 2018Review
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Authors and funding
5 authors.
Funding
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Abstract
purposeTo better identify the physiology of triple-negative breast neoplasm (TNBN), we analyzed the TNBN gene regulatory network using gene expression data.
methodsWe collected TNBN gene expression data from The Cancer Genome Atlas to construct a TNBN gene regulatory network using least absolute shrinkage and selection operator regression. In addition, we constructed a triple-positive breast neoplasm (TPBN) network for comparison. Furthermore, survival analysis based on gene expression levels and differentially expressed gene (DEG) analysis were carried out to support and compare the network analysis results, respectively.
resultsThe TNBN gene regulatory network, which followed a power-law distribution, had 10,237 vertices and 17,773 edges, with an average vertex-to-vertex distance of 8.6. The genes
conclusionOur TNBN gene regulatory network was a scale-free one, which means that the network would be easily destroyed if the hub vertices were attacked. Thus, it is important to identify the hub vertices in the network analysis. In the TNBN gene regulatory network,
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