Gene expression profile of peripheral blood in colorectal cancer.

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.

The accuracy of magnetic resonance imaging for preoperative deep myometrium assessment in endometrial cancer.

OBJECTIVE: To evaluate the accuracy of preoperative magnetic resonance imaging (MRI) to detect deep myometrial invasion in patients with endometrial cancer. MATERIALS AND METHODS: We retrospectively reviewed 66 cases of women with endometrial cancer, who underwent preoperative MRI assessment and surgical staging between January 2006 and October 2010. The MRI findings were then compared with the pathology results. The diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of MRI in detecting deep myometrium invasion were evaluated. RESULTS: The sensitivity, specificity, accuracy, PPV, and NPV results of MRI for the detection of deep myometrium invasion were 92.52%, 74.35%, 81.81%,71.42%, and 93.54%, respectively, with a kappa of 0.64. In the postmenopausal group, the values were 100%, 55.5%, 74.19%, 61.9%, and 100%. In the premenopausal women, they improved to 85.7%, 90.47%, 88.57%, 88.71%, and 90.47%. The sensitivity (100%) was better than the specificity (55.56%) in the postmenopausal women. The predictive value was markedly higher in the premenopausal women than the postmenopausal women (85.7% vs. 61.9%). CONCLUSION: In patients with endometrial cancer, a preoperative MRI contributes to accurate staging, allowing planning for the scale of surgery and preoperative counseling. In our study, the pretreatment identification of myometrium invasion provided the opportunity for small-scale surgery in the premenopausal women with early endometrial cancer. However, for the postmenopausal patients, the standard surgical procedure is indicated even if the degree of myometrium invasion is low.