Anti-MDM2 polyclonal antibody

Backgrounds p53 is a tumor suppressor that prevents cancer onset and progression, and mutations in the p53 gene cause loss of the tumor suppressor function of the protein. The mutant p53 protein in tumor cells can form aggregates which contribute to the dominant-negative effect over the wild-type p53 protein, causing loss of p53 tumor suppression or gain of novel oncogenic functions. Mutations in p53 have been implicated in the pathogenesis of primary prostate cancer (PCa), and are often detected in recurrent and metastatic disease. Thus, targeting mutant p53 may constitute an alternative therapeutic strategy for advanced PCa for which there are no other viable options. Methods In this study, we used immunoprecipitation, immunofluorescence, clonogenic survival, and cell proliferation assays, flow cytometric analysis and in vivo xenograft to investigate the biological effects of ReACp53, a cell-permeable peptide inhibitor of p53 aggregation, on mutant p53-carrying PCa cells. Results Our results show that ReACp53 targets amyloid aggregates of mutant p53 protein and restores the p53 nuclear function as transcriptional factor, induces mitochondrial cell death and reduces DNA synthesis of mutant p53-carrying PCa cells; ReACp53 also inhibits xenograft tumor growth in vivo. Conclusions The data presented here suggest a therapeutic potential of targeting mutant p53 protein in advanced PCa setting, which has a clinical impact for aggressive PCa with transforming how such tumors are managed.

Breast cancer leads to most of cancer deaths among women worldwide. Systematically analyzing the competing endogenous RNA (ceRNA) network and their functional modules may provide valuable insight into the pathogenesis of breast cancer. In this study, we constructed a lncRNA-TF-associated ceRNA network via combining all the significant lncRNA-TF ceRNA pairs and TF-TF PPI pairs. We computed important topological features of the network, such as degree and average path length. Hub nodes in the lncRNA-TF-associated ceRNA network were extracted to detect differential expression in different subtypes and tumor stages of breast cancer. MCODE was used for identifying the closely connected modules from the ceRNA network. Survival analysis was further used for evaluating whether the modules had prognosis effects on breast cancer. TF motif searching analysis was performed for investigating the binding potentials between lncRNAs and TFs. As a result, a lncRNA-TF-associated ceRNA network in breast cancer was constructed, which had a scale-free property. Hub nodes such as MDM4, ZNF410, AC0842-19, and CTB-89H12 were differentially expressed between cancer and normal sample in different subtypes and tumor stages. Two closely connected modules were identified to significantly classify patients into a low-risk group and high-risk group with different clinical outcomes. TF motif searching analysis suggested that TFs, such as NFAT5, might bind to the promoter and enhancer regions of hub lncRNAs and function in breast cancer biology. The results demonstrated that the synergistic, competitive lncRNA-TF ceRNA network and their functional modules played important roles in the biological processes and molecular functions of breast cancer.