Prediction of Lung Cancer Based on Serum Biomarkers by Gene Expression Programming Methods

Autor: Zhuang Yu, Haijiao Lu, Xiao-Zheng Chen, Shihai Liu, Lian-Hua Cui, Hong-Zong Si
Rok vydání: 2014
Předmět:
Male
Oncology
Cancer Research
medicine.medical_specialty
Lung Neoplasms
Epidemiology
Bioinformatics
Sensitivity and Specificity
Cohort Studies
Diagnosis
Differential
chemistry.chemical_compound
Predictive Value of Tests
Carcinoma
Non-Small-Cell Lung
Internal medicine
Lactate dehydrogenase
Biomarkers
Tumor
medicine
Carcinoma
Humans
Neoplasm Invasiveness
Lung cancer
neoplasms
Aged
Neoplasm Staging
Retrospective Studies
Lung
business.industry
Gene Expression Profiling
Public Health
Environmental and Occupational Health
Middle Aged
medicine.disease
Small Cell Lung Carcinoma
Carcinoembryonic Antigen
respiratory tract diseases
Gene Expression Regulation
Neoplastic
Clinical trial
C-Reactive Protein
medicine.anatomical_structure
chemistry
CA-125 Antigen
Phosphopyruvate Hydratase
Predictive value of tests
Biomarker (medicine)
Female
Gene expression programming
business
Zdroj: Asian Pacific Journal of Cancer Prevention. 15:9367-9373
ISSN: 1513-7368
Popis: In diagnosis of lung cancer, rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important. Serum markers, including lactate dehydrogenase (LDH), C-reactive protein (CRP), carcino-embryonic antigen (CEA), neurone specific enolase (NSE) and Cyfra21-1, are reported to reflect lung cancer characteristics. In this study classification of lung tumors was made based on biomarkers (measured in 120 NSCLC and 60 SCLC patients) by setting up optimal biomarker joint models with a powerful computerized tool - gene expression programming (GEP). GEP is a learning algorithm that combines the advantages of genetic programming (GP) and genetic algorithms (GA). It specifically focuses on relationships between variables in sets of data and then builds models to explain these relationships, and has been successfully used in formula finding and function mining. As a basis for defining a GEP environment for SCLC and NSCLC prediction, three explicit predictive models were constructed. CEA and NSE are frequently- used lung cancer markers in clinical trials, CRP, LDH and Cyfra21-1 have significant meaning in lung cancer, basis on CEA and NSE we set up three GEP models-GEP 1(CEA, NSE, Cyfra21-1), GEP2 (CEA, NSE, LDH), GEP3 (CEA, NSE, CRP). The best classification result of GEP gained when CEA, NSE and Cyfra21-1 were combined: 128 of 135 subjects in the training set and 40 of 45 subjects in the test set were classified correctly, the accuracy rate is 94.8% in training set; on collection of samples for testing, the accuracy rate is 88.9%. With GEP2, the accuracy was significantly decreased by 1.5% and 6.6% in training set and test set, in GEP3 was 0.82% and 4.45% respectively. Serum Cyfra21-1 is a useful and sensitive serum biomarker in discriminating between NSCLC and SCLC. GEP modeling is a promising and excellent tool in diagnosis of lung cancer.
Databáze: OpenAIRE
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