Breast cancer is a serious threat to women's physical and mental health. In recent years, its incidence has been on the rise and it has become the top female malignant tumor in China. At present, adjuvant chemotherapy for breast cancer has become the standard mode of breast cancer treatment, but the response results usually need to be completed after the implementation of adjuvant chemotherapy, and the optimization of the treatment plan and the implementation of breast-conserving therapy need to be based on accurate estimation of the pathological response. Therefore, to predict the efficacy of adjuvant chemotherapy for breast cancer patients is to find a predictive method that is conducive to individualized choice of chemotherapy regimens. This article introduces the research of DCE-MRI images based on deep transfer learning in breast cancer adjuvant curative effect prediction. Deep transfer learning algorithms are used to process images, and then, the features of breast cancer after adjuvant chemotherapy are collected through image feature collection. Predictions are made, and the research results show that the accuracy of the prediction reaches 70%.

Breast cancer (BC) is a quite prevalent cancer worldwide, and it is the leading cause of cancer-related deaths among female population worldwide. Increasingly more efforts have been made in exploration of circular RNA (circRNA) functions in various malignancies. In this study, the primary target was to verify the putative influences of circ_0041732 on BC progression and the corresponding regulatory mechanism. In addition to measurement of RNAs and proteins, functional assays were done to examine the changes in cell proliferation and cell cycle, and the potential association among genes was investigated by mechanism assays. According to experimental results, significant up-regulation of circ_0041732 was confirmed in BC tissues and cell lines. E2F4 was proved to transcriptionally modulate circ_0041732. Moreover, circ_0041732 was validated to accelerate BC cell proliferation and impede G2/M arrest and cell apoptosis, and the oncogenic role of circ_0041732 in BC was further verified via in vivo experiments. Circ_0041732 could sponge miR-541-3p to enhance expression levels of RelA and GLI4, thus activating NF-kB and Hedgehog pathways and affecting BC cell proliferation, cell cycle and apoptosis. In all, E2F4-mediated circ_0041732 could activate RelA/NF-kB and GLI4/Hedgehog signaling pathways via modulation on miR-541-3p/RelA/GLI4 to promote BC progression.

Chemotherapy is effectively used for treating breast cancer, but the problem of tumor resistance to chemotherapy drugs has been plaguing scientists. Our study investigated miR-34a?s effect on the sensitivity of drug-resistant strains to chemotherapeutic drugs using doxorubicin-resistant strains of breast cancer cells. Cell survival rate was detected by MTT assay. The doxorubicin-resistant strain rMCF-7 was obtained. The cell scratching method and CCK-8 method were used to detect cell migration and proliferation.Western Blot was performed for measuring SIRT1, p-AKT, AKT, p-mTOR and mTOR level. miR-34a significantly reduced the survival rate o doxorubicin-resistant breast cancer cell line rMCF-7 and significantly enhanced doxorubicin?s effect on inhibiting cell proliferation and cell migration. Compared with the doxorubicin group alone, miR-34a and doxorubicin combination group significantly downregulated SIRT1, p-AKT/AKT and p-mTOR/mTOR related proteins in rMCF-7 cells. miR-34a can reverse the resistance of doxorubicin in breast cancer in vitro and the mechanism may be through inhibition of SIRT and AKT signaling.