The Research On Lung Cancer Biomarkers In Exhaled Breath And Clinical Diagnostic Method Of E-Nose

The research on disease diagnosis through exhaled breath detecting has been almost 40 years. In the recent years, the detection of Volatile Organic Compounds (VOCs) has been applied in lung cancer diagnosis by many researchers. But the researchers don't have an agreement on the VOCs biomarkers for lung cancer and the production mechanism of these VOCs biomarkers. In addition, the biomarkers for lung cancer in Exhaled Breath Condensate (EBC), like Carcinoma Embryonic Antigen (CEA), have also been studied by many researchers in recent years. The detection of biomarkers in VOCs and EBC is a quick, noninvasive and novel way to diagnose lung cancer. Its application in clinical diagnosis for lung cancer has a vast prospect.The VOCs biomarkers and the nonvolatile biomarkers for lung cancer in exhaled breath were analyzed in this paper. And the e-Nose was applied in this paper to detect the VOCs biomarkers for lung cancer. This paper includes four parts as in the following.The first part is the research on VOCs biomarkers for lung cancer. The exhaled breath of 85 lung cancer patients,70 lung benign disease patients and 88 healthy people were analyzed using GCMS by Jin Yu in the early days, and 41 endogenous VOCs were extracted out from the GCMS data. These data were reanalyzed in this paper. The availablity of every endogenous VOCs was evaluated through their Receiver Operating Characteristic (ROC) curves.25 VOCs biomarkers for lung cancer were selected out according their AUC (Area Under ROC Cureve) and the values of p. The Linear Discriminant Analysis (LDA) was finally employed to build models to discriminate the lung cancer group and the control group. The sensitivity and specificity of the best model are 95.29% and 96.20% respectively.Our institute has designed two e-Noses to detect VOCs in exhaled breath. One is based on Metal Oxide Semiconductor (MOS) sensors and the other is based on Surface Acoustic Wave (SAW) sensors. This paper developed two sets of software. One is for the CN e-NoseⅡbreath detecting e-Nose based on the MOS sensors. This software can control the e-Nose and dispose the data of sensors. The other software is used to build lung cancer diagnosis models based on the data of MOS sensors and SAW sensors.These two e-Noses were used to analyze the exhaled breath of 42 healthy people and 47 lung cancer patients.138 features were selected out from the sensor curves for every sample. The independent variables of the diagnosis models were selected according the AUC of ROC curve of every feature. Finally, four pattern recognition algorithms:Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN) and Partial Least Squares (PLS), were employed to build six diagnosis models:LDA model, ANN model, PLS model, PCA-LDA model, PCA-ANN model and PCA-PLS model. The PCA-ANN model performed best with high sensitivity and specificity (93.62% and 90.48% respectively) and high efficiency.The EBC samples were also collected and the concentrations of Carcinoma Embryonic Antigen (CEA), Neuron Specific Enolase (NSE) and Squamous Cell Carcinoma (SCC) in these samples were measured. Although the lung cancer biomarkers in EBC have been researched by many researchers recently, the detection of these three biomarkers in EBC was rarely researched. According to the result, they all existed in EBC and the detectable ratio of CEA and SCC was about 30%. The relationship between these biomarkers and the types of lung cancer pathology was also analysed)...