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BackgroundMetformin is the first line of oral antidiabetic drug in the biguanide class for treatment of type 2 diabetes. Increasing evidence has suggested that it is a potential anti-tumor drug. However, the mechanisms underlying inhibiting tumor development remain elusive, especially in bladder tumors.MethodsT24 and J82 cell lines were used as an in vitro model, and 24 female SD rats were used to build an N-methyl-N-nitrosourea (MNU)-induced orthotopic rat bladder cancer model. Transfection of lentivirus-based shRNA was used to construct the STAT3-KNOCKDOWN T24 cell line. After metformin treatment, the viability of bladde cancer cells was determined by CCK8. Cell cycle distribution and apoptosis were assessed by flow cytometry. The migration and invasion abilities of cells were evaluated by wound healing and transwell asssays. The inactivation of stat3 pahtway was examined by qRTPCR, western blot and Immunofluorescence.ResultsMetformin can effectively inhibit precancerous progression to invasive cancer in an MNU-induced rat orthotopic bladder tumor model, although it could not completely suppress normal cells transforming into tumor cells. While the MNU could induce 50 % rats (4/8) to develop invasive bladder cancers, the rats co-administrated with metformin failed to develop invasive tumors but retained at precancerous or non-invasive stages, exhibiting as dysplasia, papillary tumor and/or carcinoma in situ (CIS). Accordingly, phosphorylation of signal transducer and activator of transcription 3 (STAT3), which is a well known oncogene, was significantly inhibited in the tumors of rats treated with metformin. In vitro experiments revealed that the metformin could efficiently inhibit STAT3 activation, which was associated with the cell cycle arrest, reduction of cell proliferation, migration and invasiveness, and increase in apoptotic cell death of bladder cancer cell lines.ConclusionsThese findings provide for the first time the evidence that metformin can block precancerous lesions progressing to invasive tumors through inhibiting the activation of STAT3 pathway, and may be used for treatment of the non-invasive bladder cancers to prevent them from progression to invasive tumors.Electronic supplementary materialThe online version of this article (doi:10.1186/s13046-015-0183-0) contains supplementary material, which is available to authorized users.

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Protein-DNA bindings are critical to many biological processes. However, the structural mechanisms underlying these interactions are not fully understood. Here, we analyzed the residues shape (peak, flat, or valley) and the surrounding environment of double-stranded DNA-binding proteins (DSBs) and single-stranded DNA-binding proteins (SSBs) in protein-DNA interfaces. In the results, we found that the interface shapes, hydrogen bonds, and the surrounding environment present significant differences between the two kinds of proteins. Built on the investigation results, we constructed a random forest (RF) classifier to distinguish DSBs and SSBs with satisfying performance. In conclusion, we present a novel methodology to characterize protein interfaces, which will deepen our understanding of the specificity of proteins binding to ssDNA (single-stranded DNA) or dsDNA (double-stranded DNA). Proteins 2016; 84:979-989. © 2016 Wiley Periodicals, Inc.

Background: DNA-binding proteins perform important functions in a great number of biological activities. DNA-binding proteins can interact with ssDNA (single-stranded DNA) or dsDNA (double-stranded DNA), and DNA-binding proteins can be categorized as single-stranded DNA-binding proteins (SSBs) and double-stranded DNA-binding proteins (DSBs). The identification of DNA-binding proteins from amino acid sequences can help to annotate protein functions and understand the binding specificity. In this study, we systematically consider a variety of schemes to represent protein sequences: OAAC (overall amino acid composition) features, dipeptide compositions, PSSM (position-specific scoring matrix profiles) and split amino acid composition (SAA), and then we adopt SVM (support vector machine) and RF (random forest) classification model to distinguish SSBs from DSBs. Results: Our results suggest that some sequence features can significantly differentiate DSBs and SSBs. Evaluated by 10 fold cross-validation on the benchmark datasets, our prediction method can achieve the accuracy of 88.7% and AUC (area under the curve) of 0.919. Moreover, our method has good performance in independent testing.

Astrocyte elevated gene-1 (AEG-1), a novel oncoprotein, has been implicated in oncogenesis and cancer progression in various types of human cancers. Here, immunohistochemistry was used to detect AEG-1 expression in nonmuscle-invasive bladder cancer (NMIBC), and these data were examined for correlation with clinicopathological parameters, and prognosis. Immunohistochemical analysis revealed that AEG-1 expression was significantly higher in bladder cancer tissues than that in normal tissues. High expression of AEG-1 was found in 45 % of bladder cancers and significantly associated with tumor grade (P = 0.002) and progression (P = 0.028). The Kaplan-Meier survival analysis demonstrated that AEG-1 expression was significantly associated with shorter progression-free survival (P = 0.0011). Multivariate analysis further demonstrated that AEG-1 was an independent prognostic factor for patients with BC. AEG-1 protein may contribute to the malignant progression of bladder cancer, and present as a novel marker to predict the progression of NMIBC.