TY - JOUR
T1 - Gene expression profile of peripheral blood in colorectal cancer
AU - Chang, Yu Tien
AU - Huang, Chi Shuan
AU - Yao, Chung Tay
AU - Su, Sui Lung
AU - Terng, Harn Jing
AU - Chou, Hsiu Ling
AU - Chou, Yu Ching
AU - Chen, Kang Hua
AU - Shih, Yun Wen
AU - Lu, Chian Yu
AU - Lai, Ching Huang
AU - Jian, Chen En
AU - Lin, Chiao Huang
AU - Chen, Chien Ting
AU - Wu, Yi Syuan
AU - Lin, Ke Shin
AU - Wetter, Thomas
AU - Chang, Chi Wen
AU - Chu, Chi Ming
N1 - Publisher Copyright:
© 2014 Baishideng Publishing Group Inc. All rights reserved.
PY - 2014/10/21
Y1 - 2014/10/21
N2 - 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.
AB - 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.
KW - Colorectal cancer
KW - Gene expression
KW - Internet
KW - Microarray
UR - http://www.scopus.com/inward/record.url?scp=84910130145&partnerID=8YFLogxK
U2 - 10.3748/wjg.v20.i39.14463
DO - 10.3748/wjg.v20.i39.14463
M3 - 文章
C2 - 25339833
AN - SCOPUS:84910130145
SN - 1007-9327
VL - 20
SP - 14463
EP - 14471
JO - World Journal of Gastroenterology
JF - World Journal of Gastroenterology
IS - 39
ER -
