A Highly Efficient Gene Expression Programming (GEP) Model for Auxiliary Diagnosis of Small Cell Lung Cancer

• Published in: PloS one Vol. 10; no. 5; p. e0125517
• Main Authors: Yu, Zhuang, Lu, Haijiao, Si, Hongzong, Liu, Shihai, Li, Xianchao, Gao, Caihong, Cui, Lianhua, Li, Chuan, Yang, Xue, Yao, Xiaojun
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 21.05.2015; Public Library of Science (PLoS)
• Subjects: Aged; Aged, 80 and over; Algorithms; Antigens; Artificial neural networks; Bioindicators; Biomarkers; Biomarkers, Tumor; Blood levels; C-reactive protein; Cancer; Cancer diagnosis; Cancer therapies; Carcinoembryonic antigen; Case-Control Studies; Cell survival; Chloride; Chlorides; Cytokines; Development and progression; Diagnosis; Efficiency; Embryos; Female; Gene expression; Gene Expression Profiling; Genetic aspects; Hospitals; Humans; Internal medicine; L-Lactate dehydrogenase; Laboratories; Lactate dehydrogenase; Lactic acid; Lung cancer; Lung diseases; Lung Neoplasms - blood; Lung Neoplasms - diagnosis; Lung Neoplasms - genetics; Lungs; Male; Measurement methods; Medical diagnosis; Medical prognosis; Medical research; Medical screening; Medicine; Middle Aged; Model accuracy; Models, Biological; Neoplasm Staging; Neural networks; NMR; Non-small cell lung carcinoma; Nuclear magnetic resonance; Oncology; Parameter modification; Patients; Pharmacy; Phosphopyruvate hydratase; Physiological aspects; Proteins; Public health; Reproducibility of Results; Risk Factors; ROC Curve; Sensitivity; Small cell lung cancer; Small cell lung carcinoma; Small Cell Lung Carcinoma - blood; Small Cell Lung Carcinoma - diagnosis; Small Cell Lung Carcinoma - genetics; Test sets; Thoracic surgery; Tomography; Training; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0125517

Lung cancer is an important and common cancer that constitutes a major public health problem, but early detection of small cell lung cancer can significantly improve the survival rate of cancer patients. A number of serum biomarkers have been used in the diagnosis of lung cancers; however, they exhibit low sensitivity and specificity. We used biochemical methods to measure blood levels of lactate dehydrogenase (LDH), C-reactive protein (CRP), Na+, Cl-, carcino-embryonic antigen (CEA), and neuron specific enolase (NSE) in 145 small cell lung cancer (SCLC) patients and 155 non-small cell lung cancer and 155 normal controls. A gene expression programming (GEP) model and Receiver Operating Characteristic (ROC) curves incorporating these biomarkers was developed for the auxiliary diagnosis of SCLC. After appropriate modification of the parameters, the GEP model was initially set up based on a training set of 115 SCLC patients and 125 normal controls for GEP model generation. Then the GEP was applied to the remaining 60 subjects (the test set) for model validation. GEP successfully discriminated 281 out of 300 cases, showing a correct classification rate for lung cancer patients of 93.75% (225/240) and 93.33% (56/60) for the training and test sets, respectively. Another GEP model incorporating four biomarkers, including CEA, NSE, LDH, and CRP, exhibited slightly lower detection sensitivity than the GEP model, including six biomarkers. We repeat the models on artificial neural network (ANN), and our results showed that the accuracy of GEP models were higher than that in ANN. GEP model incorporating six serum biomarkers performed by NSCLC patients and normal controls showed low accuracy than SCLC patients and was enough to prove that the GEP model is suitable for the SCLC patients. We have developed a GEP model with high sensitivity and specificity for the auxiliary diagnosis of SCLC. This GEP model has the potential for the wide use for detection of SCLC in less developed regions.