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Constructing metabolism-protein interaction relationship to identify glioma prognosis using deep learning.
Lai, Qingpei; Liu, Xiang; Yang, Fan; Li, Jie; Xie, Yaoqin; Qin, Wenjian.
Afiliação
  • Lai Q; Shenzhen Institute of Advanced Technology, Chinese Academy of Science, 518055, Shenzhen, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, 518055, Shenzhen, China.
  • Liu X; Shenzhen Institute of Advanced Technology, Chinese Academy of Science, 518055, Shenzhen, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, 518055, Shenzhen, China.
  • Yang F; Shenzhen Institute of Advanced Technology, Chinese Academy of Science, 518055, Shenzhen, China.
  • Li J; Department of Infectious Diseases, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 210008, Nanjing, Jiangsu, China.
  • Xie Y; Shenzhen Institute of Advanced Technology, Chinese Academy of Science, 518055, Shenzhen, China.
  • Qin W; Shenzhen Institute of Advanced Technology, Chinese Academy of Science, 518055, Shenzhen, China. Electronic address: wj.qin@siat.ac.cn.
Comput Biol Med ; 158: 106875, 2023 05.
Article em En | MEDLINE | ID: mdl-37058759
Glioma is heterogeneous disease that requires classification into subtypes with similar clinical phenotypes, prognosis or treatment responses. Metabolic-protein interaction (MPI) can provide meaningful insights into cancer heterogeneity. Moreover, the potential of lipids and lactate for identifying prognostic subtypes of glioma remains relatively unexplored. Therefore, we proposed a method to construct an MPI relationship matrix (MPIRM) based on a triple-layer network (Tri-MPN) combined with mRNA expression, and processed the MPIRM by deep learning to identify glioma prognostic subtypes. These Subtypes with significant differences in prognosis were detected in glioma (p-value < 2e-16, 95% CI). These subtypes had a strong correlation in immune infiltration, mutational signatures and pathway signatures. This study demonstrated the effectiveness of node interaction from MPI networks in understanding the heterogeneity of glioma prognosis.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / Glioma Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / Glioma Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article