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

Yu, Zhuang; Chen, Xiao-Zheng; Cui, Lian-Hua; Si, Hong-Zong; Lu, Hai-Jiao; Liu, Shi-Hai

추천

검색

자료유형: 학술저널

저자정보: Yu, Zhuang (Department of Oncology, The Affiliated Hospital of Qingdao University) Chen, Xiao-Zheng (Department of Oncology, The Affiliated Hospital of Qingdao University) Cui, Lian-Hua (Department of Public Health, Qingdao University Medical College) Si, Hong-Zong (Department of Pharmacy, Qingdao University, Institute for Computational Science and Engineering, Laboratory of New Fibrous Materials and Modern Textile, the Growing Base for State Key La) Lu, Hai-Jiao (Department of Oncology, The Affiliated Hospital of Qingdao University) Liu, Shi-Hai (The central Laboratory, The Affiliated Hospital of Qingdao University)

저널정보: 아시아태평양암예방학회 Asian Pacific journal of cancer prevention : APJCP Asian Pacific journal of cancer prevention : APJCP 제15권 제21호

발행연도: 2014.1

수록면: 9,367 - 9,373 (7page)

이용수

📌

연구주제

📖

연구배경

🔬

연구방법

🏆

연구결과

초록· 키워드

오류제보하기

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 requentlyused 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.

#Lung cancer #diagnosis #gene expression programming #biomarker #cyfra21-1

참고문헌 (0)

참고문헌 신청

참고문헌이 DBpia에서 서비스 중이라면, [참고문헌 신청]을 통해 등록해보세요