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Prediction of Recurrence-Free Survival in Postoperative Non-Small Cell Lung Cancer Patients by Using an Integrated Model of Clinical Information and Gene Expression

Authors
Lee, Eung-SirkSon, Dae-SoonKim, Sung-HyunLee, JinseonJo, JisukHan, JounghoKim, HeesueLee, Hyun JooChoi, Hye YoungJung, YoungjaPark, MiyeonLim, Yu SungKim, KwhanmienShim, Young MogKim, Byung ChulLee, KyusangHuh, NamKo, ChristopherPark, KyungheeLee, Jae WonChoi, Yong SooKim, Jhingook
Issue Date
15-11월-2008
Publisher
AMER ASSOC CANCER RESEARCH
Citation
CLINICAL CANCER RESEARCH, v.14, no.22, pp.7397 - 7404
Indexed
SCIE
SCOPUS
Journal Title
CLINICAL CANCER RESEARCH
Volume
14
Number
22
Start Page
7397
End Page
7404
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/122404
DOI
10.1158/1078-0432.CCR-07-4937
ISSN
1078-0432
Abstract
Purpose: One of the main challenges of lung cancer research is identifying patients at high risk for recurrence after surgical resection. Simple, accurate, and reproducible methods of evaluating individual risks of recurrence are needed. Experimental Design: Based on a combined analysis of time-to-recurrence data, censoring information, and microarray data from a set of 138 patients, we selected statistically significant genes thought to be predictive of disease recurrence. The number of genes was further reduced by eliminating those whose expression levels were not reproducible by real-time quantitative PCR. Within these variables, a recurrence prediction model was constructed using Cox proportional hazard regression and validated via two independent cohorts (n = 56 and n = 59). Results: After performing a log-rank test of the microarray data and successively selecting genes based on real-time quantitative PCR analysis, the most significant 18 genes had P values of < 0.05. After subsequent stepwise variable selection based on gene expression information and clinical variables, the recurrence prediction model consisted of six genes (CALB1, MMP7 SLC1A7, GSTA1, CCL19, and IFI44). Two pathologic variables, pStage and cellular differentiation, were developed. Validation by two independent cohorts confirmed that the proposed model is significantly accurate (P = 0.0314 and 0.0305, respectively). The predicted median recurrence-free survival times for each patient correlated well with the actual data. Conclusions: We have developed an accurate, technically simple, and reproducible method for predicting individual recurrence risks. This model would potentially be useful in developing customized strategies for managing lung cancer.
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정경대학 (통계학과)
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