Systematic review on recent potential biomarkers of chronic obstructive pulmonary disease.
Eda CelikbasDeborah PenqueJerome ZoidakisPublished in: Expert review of molecular diagnostics (2018)
Introduction: Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide and associated with decreased lung function and inflammation. The heterogeneity of COPD and its molecular and clinical features hinder efficient patient stratification and introduction of personalized therapeutic approaches. The available clinical tools do not efficiently predict the progression and exacerbations of the disease. Areas covered: An overview of the most recent studies on putative COPD protein biomarkers and the challenges for implementing their use in the clinical setting is presented. Expert commentary: Proteomics biomarker discovery in COPD has mostly focused on approaches evaluating specific proteins on a limited number of samples. The most promising protein candidates can be classified into five main biological categories: extracellular matrix (ECM) remodeling, inflammation/immune response, oxidative stress response, vascular tone regulation, and lipid metabolism. To efficiently stratify COPD patients and predict exacerbations, it will be necessary to implement biomarker panels to better represent the complex pathophysiology of this disease. The application of unbiased proteomics and bioinformatics followed by appropriate clinical validation studies will contribute to the achievement of this aim while increasing the number of validated biomarkers that can enter the qualification processes by the regulatory entities.
Keyphrases
- chronic obstructive pulmonary disease
- lung function
- cystic fibrosis
- extracellular matrix
- systematic review
- immune response
- air pollution
- oxidative stress
- mass spectrometry
- end stage renal disease
- small molecule
- randomized controlled trial
- amino acid
- protein protein
- prognostic factors
- single molecule
- dendritic cells
- transcription factor
- single cell
- meta analyses
- high throughput