S100A4 is a member of the S100 family of calcium-binding proteins that is directly involved in tumor metastasis. In the present study, we examined the potential role of S100A4 in metastasis in breast cancer and its relation with matrix metalloproteinase-13 (MMP-13). Analysis of 100 breast cancer specimens including 50 with and 50 without lymph node metastasis showed a significant upregulation of S100A4 and MMP-13 expression in metastatic breast cancer tissues. Positive immunoreactivity for S100A4 was associated with MMP-13 expression. Overexpression of S100A4 in the MDA-MB-231 breast cancer cell line upregulated MMP13 expression leading to increased cell migration and angiogenesis. SiRNA-mediated silencing of S100A4 downregulated MMP13 expression and suppressed cell migration and angiogenesis. Moreover, neutralization of MMP-13 activity with a specific antibody blocked cell migration and angiogenesis in MDA-MB-231/S100A4 cells. In vivo siRNA silencing of S100A4 significantly inhibited lung metastasis in transgenic mice. The present results suggest that the S100A4 gene may control the invasive potential of human breast cancer cells by modulating MMP-13 levels, thus regulating metastasis and angiogenesis in breast tumors. S100A4 could therefore be of value as a biomarker of breast cancer progression and a novel therapeutic target for human breast cancer treatment.

Imbalanced data are very common in the real world, and it may deteriorate the performance of the conventional classification algorithms. In order to resolve the imbalanced classification problems, we propose an ensemble classification method that combines evolutionary under-sampling and feature selection. We employ the Bootstrap method in original data to generate many sample subsets. V -statistic is developed to measure the distribution of imbalanced data, and it is also taken as the optimization objective of the genetic algorithm for the under-sampling sample subsets. Moreover, we take F 1 and Gmean indicators as two optimization objectives and employ the multiobjective ant colony optimization algorithm for feature selection of resampled data to construct an ensemble system. Ten low-dimensional and four high-dimensional typical imbalanced datasets are used in experiments. The six state-of-the-art algorithms and four measures are taken for a fair comparison. The experimental results show that our proposed system has a better classification performance compared with other algorithms, especially for the high-dimensional imbalanced data.

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