대한골대사학회지 (Journal of Bone Metabolism)

  • 제25권4호
  • /
  • Pages.251-266
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  • 2018
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  • 2287-6375(pISSN)
  • /
  • 2287-7029(eISSN)
대한골대사학회 (The Korean Society for Bone and Mineral Research)
권호 표지
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Causal Inference Network of Genes Related with Bone Metastasis of Breast Cancer and Osteoblasts Using Causal Bayesian Networks

  • Park, Sung Bae (Department of Neurosurgery, Seoul National University Boramae Medical Center) ;
  • Chung, Chun Kee (Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, Clinical Research Institute) ;
  • Gonzalez, Efrain (Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University) ;
  • Yoo, Changwon (Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University)
  • 투고 : 2018.10.07
  • 심사 : 2018.11.02
  • 발행 : 2018.11.30
⟨ 이전 논문 다음 논문 ⟩

초록

Background: The causal networks among genes that are commonly expressed in osteoblasts and during bone metastasis (BM) of breast cancer (BC) are not well understood. Here, we developed a machine learning method to obtain a plausible causal network of genes that are commonly expressed during BM and in osteoblasts in BC. Methods: We selected BC genes that are commonly expressed during BM and in osteoblasts from the Gene Expression Omnibus database. Bayesian Network Inference with Java Objects (Banjo) was used to obtain the Bayesian network. Genes registered as BC related genes were included as candidate genes in the implementation of Banjo. Next, we obtained the Bayesian structure and assessed the prediction rate for BM, conditional independence among nodes, and causality among nodes. Furthermore, we reported the maximum relative risks (RRs) of combined gene expression of the genes in the model. Results: We mechanistically identified 33 significantly related and plausibly involved genes in the development of BC BM. Further model evaluations showed that 16 genes were enough for a model to be statistically significant in terms of maximum likelihood of the causal Bayesian networks (CBNs) and for correct prediction of BM of BC. Maximum RRs of combined gene expression patterns showed that the expression levels of UBIAD1, HEBP1, BTNL8, TSPO, PSAT1, and ZFP36L2 significantly affected development of BM from BC. Conclusions: The CBN structure can be used as a reasonable inference network for accurately predicting BM in BC.

키워드

과제정보

연구 과제 주관 기관 : SNUH

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