A dual-channel self-calibrating multi-parameter sensor for lung cancer-related exhaled marker rapid identification

Abstract

Lung cancer remains the leading cancer killer worldwide. Early diagnosis can effectively increase the patient cure rate but existing diagnostic methods limit early lung cancer diagnosis. Therefore, the development of a convenient but efficient lung cancer screening method is important to the improvement of both the diagnosis rate and the survival rate of lung cancer patients. In this study, ten photosensitive materials with high sensitivity and high specificity were screened accurately to construct a microarray sensor that can rapidly identify six types of lung cancer biomarkers in exhaled breath. Parameters from hierarchical cluster analysis (HCA), principal component analysis (PCA) and difference maps showed that the classification of the analytes agreed with structure similarity laws, the fingerprints of analytes with different structures have specific response regions, and positive correlation is significant between the response signal and concentration of analytes over the range of 1–1000 ppb. The sensor chips made in the same batch and closely adjacent have a high degree of consistency; in addition, the dual-channel structure solves the field calibration problem in rapid identification of lung cancer expiratory markers. The sensor has potential application prospects for use in extensive screening of high-risk populations for lung cancer.