| Literature DB >> 31402712 |
Toshihide Nishimura1, Haruhiko Nakamura1,2, Ákos Végvári3, György Marko-Varga4,5, Naoki Furuya6, Hisashi Saji2.
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.Entities:
Keywords: Lung cancer; clinical proteogenomics; mutant identification; network-based bioinformatics; next generation sequencing; proteomics⋅mass; spectrometry
Mesh:
Year: 2019 PMID: 31402712 DOI: 10.1080/14789450.2019.1654861
Source DB: PubMed Journal: Expert Rev Proteomics ISSN: 1478-9450 Impact factor: 3.940