MicroRNA与肿瘤的发生、转移、预后和耐药密切相关。临床上乳腺癌新辅助化疗缺乏有效的疗效预测标志物，目前对miR-621的功能是否影响细胞毒药物敏感性知之甚少。我们的前期研究结果提示miR-621与乳腺癌PC（紫杉醇+卡铂）方案新辅助化疗疗效相关，其表达水平上调可显著提高有效率。据此推测，miR-621与乳腺癌化疗敏感性相关，可能是通过miR-621在肿瘤细胞中的表达，进而对耐药基因的调控造成的。我们将在前期研究基础上，利用人乳腺癌细胞系及接受新辅助化疗的乳腺癌患者样本，采用QPCR、Western blot、病毒载体转染、RNA干扰、TargetScan与miRanda预测软件等手段，从细胞、荷瘤裸鼠模型和人乳腺癌组织水平，探讨miR-621及其基因调控在乳腺癌新辅助治疗疗效预测中的作用，对阐明miR-621相关通路对药物敏感性的调控机制有重要意义，为改进乳腺癌治疗疗效提供新思路。

MicroRNAs (miRNA) are short, 20- to 22-nucleotide RNA molecules that repress the translation of mRNAs by base pairing to partially complementary sites in the 3'-untranslated region (UTR). MiRNAs have diverse functions, including the regulation of cellular development, differentiation, proliferation and apoptosis. Recent evidences indicate that miRNAs can function as tumor suppressors and oncogenes.Although the estrogen-receptor status is predictive of response to hormonal treatments, there are no clinically useful molecular markers to predict responses to chemotherapy used in breast cancer. MiRNAs regulate the biology of cancer cells, including their sensitivity to chemotherapy. Aberrant expression of miRNAs is involved in a number of molecular pathways which are related to the mechanisms of chemotherapeutic resistance. Our previous study has shown that overexpression of miR-621 in breast cancer tissue leads to be sensitive to PCb [paclitaxel (Taxol) plus carboplatin] regimen neoadjuvant chemotherapy and easily to achieve pCR(Pathologic Complete Response). Though more than 530 miRNAs have been identified in human, much remains to be little understood about their precise cellular functions and roles in the development of diseases. So far we have no idea about the functions of miR-621 and response to breast cancer neoadjuvant chemotherapy. So, We will study human breast cancer cell lines and tissue samples to discuss the importance of miR-621 and its target genes in breast cancer neoadjuvant therapy efficacy predict, and clear miR-621 pathway mechanisms related to drug sensitivity by using QPCR, Western blot, virus transfection, RNA interference, TargetScan and miRanda prediction softwares, and other methods. We hope the present study would bring new ideas to breast cancer personalized therapy .