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

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 Cy...

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Vydáno v:Asian Pacific journal of cancer prevention : APJCP Ročník 15; číslo 21; s. 9367 - 9373
Hlavní autoři: Yu, Zhuang, Chen, Xiao-Zheng, Cui, Lian-Hua, Si, Hong-Zong, Lu, Hai-Jiao, Liu, Shi-Hai
Médium: Journal Article
Jazyk:angličtina
Vydáno: Thailand 01.01.2014
Témata:
Aged
Biomarkers, Tumor - blood
Biomarkers, Tumor - genetics
C-Reactive Protein - analysis
CA-125 Antigen - blood
Carcinoembryonic Antigen - blood
Carcinoma, Non-Small-Cell Lung - blood
Carcinoma, Non-Small-Cell Lung - diagnosis
Carcinoma, Non-Small-Cell Lung - genetics
Cohort Studies
Diagnosis, Differential
Female
Gene Expression Profiling - methods
Gene Expression Regulation, Neoplastic
Humans
Lung Neoplasms - blood
Lung Neoplasms - diagnosis
Male
Middle Aged
Neoplasm Invasiveness - pathology
Neoplasm Staging
Phosphopyruvate Hydratase - blood
Predictive Value of Tests
Retrospective Studies
Sensitivity and Specificity
Small Cell Lung Carcinoma - blood
Small Cell Lung Carcinoma - diagnosis
Small Cell Lung Carcinoma - genetics
ISSN:1513-7368, 2476-762X, 2476-762X
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Shrnutí: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.
Bibliografie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1513-7368
2476-762X
2476-762X
DOI:10.7314/APJCP.2014.15.21.9367