Recent mass spectrometry-based proteomics for biomarker discovery in lung cancer, COPD, and asthma

• Published in: Expert review of proteomics Vol. 14; no. 4; p. 373
• Main Authors: Fujii, Kiyonaga, Nakamura, Haruhiko, Nishimura, Toshihide
• Format: Journal Article
• Language: English
• Published: England 03.04.2017
• Subjects: Asthma - genetics; Asthma - pathology; Biomarkers; Humans; Lung Neoplasms - genetics; Lung Neoplasms - pathology; Mass Spectrometry; Proteomics - methods; Pulmonary Disease, Chronic Obstructive - genetics; Pulmonary Disease, Chronic Obstructive - pathology; lung cancer; clinical specimens; protein–protein interaction network; Biomarkers; clinical proteomics; diagnosis; mass spectrometry; asthma; proteogenomics; COPD
• ISSN: 1744-8387
• DOI: 10.1080/14789450.2017.1304215

Lung cancer and related diseases have been one of the most common causes of deaths worldwide. Genomic-based biomarkers may hardly reflect the underlying dynamic molecular mechanism of functional protein interactions, which is the center of a disease. Recent developments in mass spectrometry (MS) have made it possible to analyze disease-relevant proteins expressed in clinical specimens by proteomic challenges. Areas covered: To understand the molecular mechanisms of lung cancer and its subtypes, chronic obstructive pulmonary disease (COPD), asthma and others, great efforts have been taken to identify numerous relevant proteins by MS-based clinical proteomic approaches. Since lung cancer is a multifactorial disease that is biologically associated with asthma and COPD among various lung diseases, this study focused on proteomic studies on biomarker discovery using various clinical specimens for lung cancer, COPD, and asthma. Expert commentary: MS-based exploratory proteomics utilizing clinical specimens, which can incorporate both experimental and bioinformatic analysis of protein-protein interaction and also can adopt proteogenomic approaches, makes it possible to reveal molecular networks that are relevant to a disease subgroup and that could differentiate between drug responders and non-responders, good and poor prognoses, drug resistance, and so on.