Retrospective Study
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World J Gastroenterol. Oct 21, 2014; 20(39): 14463-14471
Published online Oct 21, 2014. doi: 10.3748/wjg.v20.i39.14463
Gene expression profile of peripheral blood in colorectal cancer
Yu-Tien Chang, Chi-Shuan Huang, Chung-Tay Yao, Sui-Lung Su, Harn-Jing Terng, Hsiu-Ling Chou, Yu-Ching Chou, Kang-Hua Chen, Yun-Wen Shih, Chian-Yu Lu, Ching-Huang Lai, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Thomas Wetter, Chi-Wen Chang, Chi-Ming Chu
Yu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan
Chi-Shuan Huang, Division of Colorectal Surgery, Cheng Hsin Rehabilitation Medical Center, Taipei 112, Taiwan
Chung-Tay Yao, Department of Surgery, Cathay General Hospital, Taipei 114, Taiwan
Sui-Lung Su, Ching-Huang Lai, Yu-Ching Chou, Department of Epidemiology, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan
Harn-Jing Terng, Advpharma, Inc., New Taipei 114, Taiwan
Hsiu-Ling Chou, Department of Nursing, Far Eastern Memorial Hospital and Oriental Institute of Technology, New Taipei 114, Taiwan
Kang-Hua Chen, Chi-Wen Chang, Department of Nursing, School of Medicine, Chang Gung University, Taoyuan 333, Taiwan
Chian-Yu Lu, Air Force Combatant Command, National Defense Ministry, Taipei 114, Taiwan
Thomas Wetter, Institute of Medical Biometry and Informatics, Heidelberg University, Germany and Department of Biomedical Informatics and Medical Education, University of Washington Seattle, United States
Author contributions: Chu CM and Chang CW designed the research; Shih YW, Chang YT, Terng HJ and Wetter T performed the research; Chu CM, Chang CW, Shih YW, Chang YT, Terng HJ, Wetter T, Chou YC, Su SL, Lai CH, Chen KH, Yao CT, Chou HL, Huang CS, Shih YW, Lu CY, Chen KH, Jian CE, Lin CH, Chen CT, Wu YS and Lin KS analyzed the data; Chu CM, Chang YT, Chang CW, Shih YW, Terng HJ and Wetter T wrote the paper.
Supported by Taiwan’s SBIR promoting program from the Department of Industrial Technology of the Ministry of Economic Affairs, Advpharma, Inc., and the National Defense Medical Center (NDMC), Bureau of Military Medicine, Ministry of Defense, Taiwan
Correspondence to: Chi-Ming Chu, PhD, Professor, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Mingquan East Road 161, Taipei 114, Taiwan. chuchiming@web.de
Telephone: +886-963367484 Fax: +886-2-87923147
Received: December 31, 2013
Revised: April 8, 2014
Accepted: June 12, 2014
Published online: October 21, 2014
Abstract

AIM: Optimal molecular markers for detecting colorectal cancer (CRC) in a blood-based assay were evaluated.

METHODS: A matched (by variables of age and sex) case-control design (111 CRC and 227 non-cancer samples) was applied. Total RNAs isolated from the 338 blood samples were reverse-transcribed, and the relative transcript levels of candidate genes were analyzed. The training set was made of 162 random samples of the total 338 samples. A logistic regression analysis was performed, and odds ratios for each gene were determined between CRC and non-cancer. The samples (n = 176) in the testing set were used to validate the logistic model, and an inferred performance (generality) was verified. By pooling 12 public microarray datasets(GSE 4107, 4183, 8671, 9348, 10961, 13067, 13294, 13471, 14333, 15960, 17538, and 18105), which included 519 cases of adenocarcinoma and 88 controls of normal mucosa, we were able to verify the selected genes from logistic models and estimate their external generality.

RESULTS: The logistic regression analysis resulted in the selection of five significant genes (P < 0.05; MDM2, DUSP6, CPEB4, MMD, and EIF2S3), with odds ratios of 2.978, 6.029, 3.776, 0.538 and 0.138, respectively. The five-gene model performed stably for the discrimination of CRC cases from controls in the training set, with accuracies ranging from 73.9% to 87.0%, a sensitivity of 95% and a specificity of 95%. In addition, a good performance in the test set was obtained using the discrimination model, providing 83.5% accuracy, 66.0% sensitivity, 92.0% specificity, a positive predictive value of 89.2% and a negative predictive value of 73.0%. Multivariate logistic regressions analyzed 12 pooled public microarray data sets as an external validation. Models that provided similar expected and observed event rates in subgroups were termed well calibrated. A model in which MDM2, DUSP6, CPEB4, MMD, and EIF2S3 were selected showed the result in logistic regression analysis (H-L P = 0.460, R2= 0.853, AUC = 0.978, accuracy = 0.949, specificity = 0.818 and sensitivity = 0.971).

CONCLUSION: A novel gene expression profile was associated with CRC and can potentially be applied to blood-based detection assays.

Keywords: Colorectal cancer, Gene expression, Microarray, Internet

Core tip: A novel gene expression profile was associated with colorectal cancer and can potentially be applied to blood-based detection assays. The model that selected MDM2, DUSP6, CPEB4, MMD, and EIF2S3 showed the result in logistic regression analysis (H-L P = 0.460, R2 = 0.853, AUC = 0.978, accuracy = 0.949, specificity = 0.818 and sensitivity = 0.971).