Abstract. The aim of the present study was to investigate the association between the expression levels of transforming growth factor-β1 (TGF-β1) and the clinical pathological characteristics and prognosis of triple negative breast cancer (TNBC) through study of TNBC patient tissue samples. The biological effects of TGF-β1 on TNBC cells and the potential signal transduction pathway are additoinally investigated. Immunohistochemistry was utilized to investigate expression changes of the positive rate of TGF-β1 in the TNBC, compared with the non-TNBC group, to explain the association between TGF-β1 and clinical pathological characteristics and prognosis. MDA-MB-231 cells were treated with TGF-β1 and subsequently the invasion and migration abilities, and the expression of proteins in certain signaling pathways were assessed before and after the treatment. Positive expression of TGF-β1 was observed in 52.5% of TNBC tissue samples, which was higher than that observed in non-TNBC group (27.5%). High levels of TGF-β1 expression were not significantly associated age, menopausal status, family history of cancer or tumor size; however, tumor histological grade and axillary lymph node metastasis were significantly associated (P<0.05). In addition, when the TGF-β1 expression levels are higher, the 5-year disease-free survival rate is lower. TGF-β1 expression promoted the invasion and migration of MDA-MB-231 cells, and the expression of Smad2 protein and P38 protein was increased, indicating that Smad2 protein and the P38 signaling pathway may serve an important role in TNBC.

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Background: Triple-negative breast cancer (TNBC) is the most malignant type of breast cancer. MicroRNAs (miRs) and their corresponding molecular targets are associated with the occurrence and development of various human malignancies. However, the roles of the microRNA-153 (miR-153) and zinc finger E-box-binding homeobox 2 (ZEB2)-induced epithelial-mesenchymal transition (EMT) in TNBC and predictive effect of miR-153 on the prognosis of TNBC have not been fully elucidated. Materials and methods: Relative miR-153 expression level was examined by RT-qPCR assay in TNBC tissues of 60 patients and TNBC cell lines (SKBR3, BT-549 and MDA-MB-231). Cell proliferation ability, invasion ability and migration ability were measured by CCK8 assay, Transwell invasion assay and wound healing assay, respectively. Luciferase reporting experiment was used to confirm that there was a miR-153-binding site in ZEB2 3ʹ-UTR. The expression of ZEB2 in tissues and its relationship with miR-153 were analyzed with immunohistochemistry method. Relative ZEB2, E-cadherin, N-cadherin and Vimentin mRNA and protein expression levels were observed with RT-qPCR and Western blot, respectively. Based on risk factors, a prognostic model was established according to the Cox proportional risk model, and the prognostic risk factors of TNBC patients were predicted and analyzed. Results: The expression of miR-153 in TNBC tissues and cells was declined (all P<0.01), and upregulation of miR-153 inhibited proliferation, invasion and migration of TNBC cells (all P<0.01). In addition, miR-153 regulated ZEB2/EMT link in TNBC, and ZEB2 overexpression reversed the tumor-suppressive effect of miR-153 in TNBC. Moreover, miR-153 was an independent predictive factor that was associated with excellent prognosis in TNBC patients. Conclusion: miR-153 may inhibit TNBC proliferation, invasion and migration by regulating ZEB2/EMT link. Therefore, miR-153 is expected to be a molecular target and prognostic marker for TNBC.

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Purpose Breast cancer is a prominent cancer type with high mortality. Early detection of breast cancer could serve to improve clinical outcomes. Ultrasonography is a digital imaging technique used to differentiate benign and malignant tumors. Several artificial intelligence techniques have been suggested in the literature for breast cancer detection using breast ultrasonography (BUS). Nowadays, particularly deep learning methods have been applied to biomedical images to achieve high classification performances. Patients and Methods This work presents a new deep feature generation technique for breast cancer detection using BUS images. The widely known 16 pre-trained CNN models have been used in this framework as feature generators. In the feature generation phase, the used input image is divided into rows and columns, and these deep feature generators (pre-trained models) have applied to each row and column. Therefore, this method is called a grid-based deep feature generator. The proposed grid-based deep feature generator can calculate the error value of each deep feature generator, and then it selects the best three feature vectors as a final feature vector. In the feature selection phase, iterative neighborhood component analysis (INCA) chooses 980 features as an optimal number of features. Finally, these features are classified by using a deep neural network (DNN). Results The developed grid-based deep feature generation-based image classification model reached 97.18% classification accuracy on the ultrasonic images for three classes, namely malignant, benign, and normal. Conclusion The findings obviously denoted that the proposed grid deep feature generator and INCA-based feature selection model successfully classified breast ultrasonic images.