Inflammation has an important role in cancer development through various mechanisms. It has been shown that dysregulation of microRNAs (miRNAs) that function as oncogenes or tumor suppressors contributes to tumorigenesis. However, the relationship between inflammation and cancer-related miRNA expression in tumorigenesis has not yet been fully understood. Using K19-C2mE and Gan mouse models that develop gastritis and gastritis-associated tumors, respectively, we found that 21 miRNAs were upregulated, and that 29 miRNAs were downregulated in gastric tumors in an inflammation-dependent manner. Among these miRNAs, the expression of miR-7, a possible tumor suppressor, significantly decreased in both gastritis and gastric tumors. Moreover, the expression of miR-7 in human gastric cancer was inversely correlated with the levels of interleukin-1β and tumor necrosis factor-α, suggesting that miR-7 downregulation is related to the severity of inflammatory responses. In the normal mouse stomach, miR-7 expression was at a basal level in undifferentiated gastric epithelial cells, and was induced during differentiation. Moreover, transfection of a miR-7 precursor into gastric cancer cells suppressed cell proliferation and soft agar colony formation. These results suggest that suppression of miR-7 expression is important for maintaining the undifferentiated status of gastric epithelial cells, and thus contributes to gastric tumorigenesis. Although epigenetic changes were not found in the CpG islands around miR-7-1 of gastritis and gastric tumor cells, we found that activated macrophage-derived small molecule(s) (<3 kDa) are responsible for miR-7 repression in gastric cancer cells. Furthermore, the miR-7 expression level significantly decreased in the inflamed gastric mucosa of Helicobacter-infected mice, whereas it increased in the stomach of germfree K19-C2mE and Gan mice wherein inflammatory responses were suppressed. Taken together, these results indicate that downregulation of tumor suppressor miR-7 is a novel mechanism by which the inflammatory response promotes gastric tumorigenesis.

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

MicroRNAs (miRNAs) are a class of small non-coding RNA molecules (20-24 nt), which are believed to participate in repression of gene expression. They play important roles in several biological processes (e.g. cell death and cell growth). Both experimental and computational approaches have been used to determine the function of miRNAs in cellular processes. Most efforts have concentrated on identification of miRNAs and their target genes. However, understanding the regulatory mechanism of miRNAs in the gene regulatory network is also essential to the discovery of functions of miRNAs in complex cellular systems. To understand the regulatory mechanism of miRNAs in complex cellular systems, we need to identify the functional modules involved in complex interactions between miRNAs and their target genes. We propose a rule-based learning method to identify groups of miRNAs and target genes that are believed to participate cooperatively in the post-transcriptional gene regulation, so-called miRNA regulatory modules (MRMs). Applying our method to human genes and miRNAs, we found 79 MRMs. The MRMs are produced from multiple information sources, including miRNA-target binding information, gene expression and miRNA expression profiles. Analysis of two first MRMs shows that these MRMs consist of highly-related miRNAs and their target genes with respect to biological processes. The MRMs found by our method have high correlation in expression patterns of miRNAs as well as mRNAs. The mRNAs included in the same module shared similar biological functions, indicating the ability of our method to detect functionality-related genes. Moreover, review of the literature reveals that miRNAs in a module are involved in several types of human cancer.

Motivation: Protein--protein interactions are systematically examined using the yeast two-hybrid method. Consequently, a lot of protein--protein interaction data are currently being accumulated. Nevertheless, general information or knowledge on protein--protein interactions is poorly extracted from these data. Thus we have been trying to extract the knowledge from the protein--protein interaction data using data mining. Results: A data mining method is proposed to discover association rules related to protein--protein interactions. To evaluate the detected rules by the method, a new scoring measure of the rules is introduced. The method allowed us to detect popular interaction rules such as ‘An SH3 domain binds to a proline-rich region.’ These results indicate that the method may detect novel knowledge on protein--protein interactions. Contact: oyama@isl.intec.co.jp * To whom correspondence should be addressed.

Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA.