title: A Highly Efficient Gene Expression Programming (GEP) Model for Auxiliary Diagnosis of Small Cell Lung Cancer.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: The Affiliated Hospital of Qingdao University, Department of Oncology, Qingdao, Shandong, P.R. China.
authors.forename: Zhuang
authors.initials: Z
authors.lastname: Yu
authors.affiliation: The Affiliated Hospital of Qingdao University, Department of Oncology, Qingdao, Shandong, P.R. China.
authors.forename: Haijiao
authors.initials: H
authors.lastname: Lu
authors.affiliation: Institute for Computational Science and Engineering, Laboratory of New Fibrous Materials and Modern Textile, the Growing Base for State Key Laboratory, Department of Pharmacy, Qingdao University, Qingdao, Shandong, P.R. China.
authors.forename: Hongzong
authors.initials: H
authors.lastname: Si
authors.affiliation: The Affiliated Hospital of Qingdao University, The Central Laboratory, Qingdao, Shandong, P.R. China.
authors.forename: Shihai
authors.initials: S
authors.lastname: Liu
authors.affiliation: Department of Pharmacy, Qingdao University, Qingdao, Shandong, P.R. China.
authors.forename: Xianchao
authors.initials: X
authors.lastname: Li
authors.affiliation: The Affiliated Hospital of Qingdao University, Department of Oncology, Qingdao, Shandong, P.R. China.
authors.forename: Caihong
authors.initials: C
authors.lastname: Gao
authors.affiliation: Department of Public Health, Qingdao University Medical College, Qingdao, Shandong, P.R. China.
authors.forename: Lianhua
authors.initials: L
authors.lastname: Cui
authors.affiliation: The Affiliated Hospital of Qingdao University, Department of Thoracic Surgery, Qingdao, Shandong, P.R. China.
authors.forename: Chuan
authors.initials: C
authors.lastname: Li
authors.affiliation: The Affiliated Hospital of Qingdao University, Department of Oncology, Qingdao, Shandong, P.R. China.
authors.forename: Xue
authors.initials: X
authors.lastname: Yang
authors.affiliation: Department of Chemistry, Lanzhou University, Lanzhou, Gansu, P.R. China.
authors.forename: Xiaojun
authors.initials: X
authors.lastname: Yao
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D014408:Biomarkers, Tumor; D016022:Case-Control Studies; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D008175:Lung Neoplasms / Q000097:blood* / Q000175:diagnosis* / Q000235:genetics; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009367:Neoplasm Staging; D012372:ROC Curve; D015203:Reproducibility of Results; D012307:Risk Factors; D055752:Small Cell Lung Carcinoma / Q000097:blood* / Q000175:diagnosis* / Q000235:genetics
chemical_list: D014408:Biomarkers, Tumor