Objective To explore the expression and significance of matrix metalloproteinase (MMP)-9, IV collagen and CD34 in epithelial ovarian tumor. Methods Eighty-two patients with epithelial ovarian tumor, among them,there were 48 malignant epithelial ovarian carcinomas, 23 borderline epithelial ovarian tumors and 11 benign epithelial ovarian tumors. The expression of MMP-9, IV collagen and CD34 were detected by immunohistochemistry. Results The expression of MMP-9 was strongly linked to the degree of malignant ovarian carcinomas (F= 39.306,P< 0.01). The expression of CD34 was also strongly linked to the degree of malignant ovarian carcinomas [benign epithelial ovarian tumors was (17.18±5.64)%,borderline epithelial ovarian tumors was (29.76±7.18)%,well-differentiated malignant epithelial ovarian carcinomas was (57.20±8.55)%,moderately-differentiated malignant epithelial ovarian carcinomas was (71.20±8.48)%, poorly-differentiated malignant epithelial ovarian carcinomas was(90.38±20.03)%](F= 100.072, P < 0.01). The expression of IV collagen in malignant epithelial ovarian carcinomas was different from that in borderline epithelial ovarian tumors and benign epithelial ovarian tumors (F = 11.554,P<0.0l). The expression of MMP-9 was positive correlation with the loss expression of IV collagen and the expression of CD34 (r=0.796,0.802,P< 0.01).Conclusions The positive expression of MMP-9,CD34 and the negative expression of IV collagen are obviously relevant to degree of malignant ovarian carcinomas.The combined testing on expression of MMP-9,CD34, IV collagen on ovarian carcinomas is significant to decide degree of malignant ovarian carcinomas and evaluate future development.

Objective To explore the relationship between radiomics signatures based on DWI and dynamic contrast-enhanced MRI (DCE-MRI) and molecular subtypes of breast cancer.Methods A retrospective analysis of 79 female breast cancer patients, with single mass, clear molecular subtypes and preoperative breast MRI scanning (obtaining DCE-MRI and ADC images), of Guangdong General Hospital from June 2015 to June 2016,were performed.Traditional quantitative parameters,including ADC value and initial enhancement rate(IER),were recorded.Texture analysis were performed on ADC map and DCE map, with manual segmentation and extraction of radiomic features,and Manual segmentation was performed on ADC map and DCE map, radiomics features were extracted and 10 radiomics signatures were finally selected after dimension reduction. Four molecular subtypes of breast cancer were classified by immunohistochemical detection of pathological specimens, including Luminal A, Luminal B, human epidermal growth factor receptor 2 (HER2) overexpression and triple negative (TN). Univariate logistic regression analysis was used for assessing the performance of ADC values, IER values and radiomics signatures to independently predict molecular subtypes groups.Multivariate logistic regression analysis was performed to establish predicting models, then receiver operating characteristic curves (ROC) were drawn and areas under ROC curve were calculated to compare the diagnostic performance of each model. The Hosmer-Lemeshow test was performed to test the goodness of model fitness. Results There were 29 cases of Luminal A, 39 cases of Luminal B, 5 cases of HER2 overexpression and 6 cases of TN breast cancer patients.Univariate logistic regression analysis was used to assess the ability of traditonal MRI parameters of ADC and IER values and ten of the radiomics siganitures in classifying molecular subtypes,results showed that the AUC values of ADC and IER values, were both less than 0.70 (range 0.516 to 0.605), which indicated valueless;at least one radiomic signature had AUC greater than 0.70 when identifying each molecular subtype, and AUC of DCE_L_G_2.5_autocorrelation achieved the highest value of 0.941 in identifying TN and non-TN subtypes.Multivariate logistic regression analysis were performed to obtain the best model, results showed that the AUCs for classifying Luminal A and non-Luminal A, Luminal B and non-Luminal B, TN and non-TN subtypes were 0.786 and 0.733 And 0.941, respectively. The Hosmer-Lemeshow test showed that the P values of all models were larger than 0.10 (0.156, 0.204 and 0.820,respectively),indicating that there was no significant difference between the predicted and observed values of each model established, these models were all fitted good. Conclusion The radiomics features based on ADC map and DCE map can help to identify the molecular subtypes of breast cancer,especially for the identification of TN type breast cancer.