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Current status of clinical proteogenomics in lung cancer.
Nishimura, Toshihide; Nakamura, Haruhiko; Végvári, Ákos; Marko-Varga, György; Furuya, Naoki; Saji, Hisashi.
Afiliación
  • Nishimura T; Department of Translational Medicine Informatics, St. Marianna University School of Medicine , Kawasaki, Kanagawa , Japan.
  • Nakamura H; Department of Translational Medicine Informatics, St. Marianna University School of Medicine , Kawasaki, Kanagawa , Japan.
  • Végvári Á; Department of Chest Surgery, St. Marianna University School of Medicine , Kawasaki, Kanagawa , Japan.
  • Marko-Varga G; Proteomics Biomedicum, Division of Physiological Chemistry I, Department of Medical Biochemistry & Biophysics (MBB), Karolinska Institutet , Solna , Sweden.
  • Furuya N; Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University , Lund , Sweden.
  • Saji H; Section for Clinical Chemistry, Department of Translational Medicine, Lund University, Skåne University Hospital Malmö , Malmö , Sweden.
Expert Rev Proteomics ; 16(9): 761-772, 2019 09.
Article en En | MEDLINE | ID: mdl-31402712
ABSTRACT

Introduction:

Lung cancer is the leading cause of cancer death worldwide. Proteogenomics, a way to integrate genomics, transcriptomics, and proteomics, have emerged as a way to understand molecular causes in cancer tumorigenesis. This understanding will help identify therapeutic targets that are urgently needed to improve individual patient outcomes. Areas covered To explore underlying molecular mechanisms of lung cancer subtypes, several efforts have used proteogenomic approaches that integrate next generation sequencing (NGS) and mass spectrometry (MS)-based technologies. Expert opinion A large-scale, MS-based, proteomic analysis, together with both NGS-based genomic data and clinicopathological information, will facilitate establishing extensive databases for lung cancer subtypes that can be used for further proteogenomic analyzes. Proteogenomic strategies will further be understanding of how major driver mutations affect downstream molecular networks, resulting in lung cancer progression and malignancy, and how therapy-resistant cancers resistant are molecularly structured. These strategies require advanced bioinformatics based on a dynamic theory of network systems, rather than statistics, to accurately identify mutant proteins and their affected key networks.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Biología Computacional / Proteogenómica / Neoplasias Pulmonares Límite: Humans Idioma: En Revista: Expert Rev Proteomics Asunto de la revista: BIOQUIMICA Año: 2019 Tipo del documento: Article País de afiliación: Japón

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Biología Computacional / Proteogenómica / Neoplasias Pulmonares Límite: Humans Idioma: En Revista: Expert Rev Proteomics Asunto de la revista: BIOQUIMICA Año: 2019 Tipo del documento: Article País de afiliación: Japón