Abstract
Introduction: In the era of personalised medicine post-genomic biomarkers could be employed to evaluate respiratory diseases. Particularly important targets are the rapid diagnosis, typing and staging of lung cancer (LC) and predicting exacerbations in patients with chronic obstructive pulmonary disease (COPD) which often occur as co-morbidities. In such approaches screens based on sputum, represent an attractive prospect.
Materials and Methods: Spontaneous sputum was collected from 155 patients and correlated with the corresponding clinic-radiological diagnosis (COPD, LC, controls derived from heavy smokers). Global DNA extractions were performed and consequently amplified for the 16S rRNA gene for identification of the microbial load as an indication of potential infection pressure/ relative immunological status. Mass-spectrometry (MS)-based technologies were employed to identify protein and metabolites within the sputum sample. The proteomic and metabolite profiles were analysed using multivariate statistical and machine-learning approaches to identify components that were associated with clinically relevant classes.
Results: Analysis of sputum revealed over 60 major classes of proteins differentially expressed and regulated across the disease spectrum. The microbial communities appear to fluctuate in number and also species across the investigated group and GOLD COPD classification with p<0.05. Interrogation of metabolite profiles showed distinctive biochemical changes within each patient group.
Conclusion: ‘Omic approaches based on sputum can differentiate a large variety of pulmonary diseases and offer diagnostic biomarkers.
- Copyright ©the authors 2017
