Seiya Imoto

Long Noncoding RNA HOTAIR Regulates Polycomb-Dependent Chromatin Modification and Is Associated with Poor Prognosis in Colorectal Cancers

The functional impact of recently discovered long noncoding RNAs (ncRNAs) in human cancer remains to be clarified. One long ncRNA which has attracted attention is the Hox transcript antisense intergenic RNA termed HOTAIR, a long ncRNA expressed from the developmental HOXC locus located on chromosome 12q13.13. In cooperation with Polycomb complex PRC2, the HOTAIR long ncRNA is reported to reprogram chromatin organization and promote breast cancer metastasis. In this study, we examined the status and function of HOTAIR in patients with stage IV colorectal cancer (CRC) who have liver metastases and a poor prognosis. HOTAIR expression levels were higher in cancerous tissues than in corresponding noncancerous tissues and high HOTAIR expression correlated tightly with the presence of liver metastasis. Moreover, patients with high HOTAIR expression had a relatively poorer prognosis. In a subset of 32 CRC specimens, gene set enrichment analysis using cDNA array data revealed a close correlation between expression of HOTAIR and members of the PRC2 complex (SUZ12, EZH2, and H3K27me3). Our findings suggest that HOTAIR expression is associated with a genome-wide reprogramming of PRC2 function not only in breast cancer but also in CRC, where upregulation of this long ncRNA may be a critical element in metastatic progression.

Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression.

We propose a new method for constructing genetic network from gene expression data by using Bayesian networks. We use nonparametric regression for capturing nonlinear relationships between genes and derive a new criterion for choosing the network in general situations. In a theoretical sense, our proposed theory and methodology include previous methods based on Bayes approach. We applied the proposed method to the S. cerevisiae cell cycle data and showed the effectiveness of our method by comparing with previous methods.

Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations.

We describe a new method to infer a gene regulatory network, in terms of a linear system of differential equations, from time course gene expression data. As biologically the gene regulatory network is known to be sparse, we expect most coefficients in such a linear system of differential equations to be zero. In previously proposed methods, the number of nonzero coefficients in the system was limited based on ad hoc assumptions. Instead, we propose to infer the degree of sparseness of the gene regulatory network from the data, where we use Akaikes Information Criterion to determine which coefficients are nonzero. We apply our method to MMGE time course data of Bacillus subtilis.

Plastin3 Is a Novel Marker for Circulating Tumor Cells Undergoing the Epithelial–Mesenchymal Transition and Is Associated with Colorectal Cancer Prognosis

Circulating tumor cells (CTC) in blood have attracted attention both as potential seeds for metastasis and as biomarkers. However, most CTC detection systems might miss epithelial-mesenchymal transition (EMT)-induced metastatic cells because detection is based on epithelial markers. First, to discover novel markers capable of detecting CTCs in which EMT has not been repressed, microarray analysis of 132 colorectal cancers (CRC) from Japanese patients was conducted, and 2,969 genes were detected that were overexpressed relative to normal colon mucosa. From the detected genes, we selected those that were overexpressed CRC with distant metastasis. Then, we analyzed the CRC metastasis-specific genes (n = 22) to determine whether they were expressed in normal circulation. As a result, PLS3 was discovered as a CTC marker that was expressed in metastatic CRC cells but not in normal circulation. Using fluorescent immunocytochemistry, we validated that PLS3 was expressed in EMT-induced CTC in peripheral blood from patients with CRC with distant metastasis. PLS3-expressing cells were detected in the peripheral blood of approximately one-third of an independent set of 711 Japanese patients with CRC. Multivariate analysis showed that PLS3-positive CTC was independently associated with prognosis in the training set (n = 381) and the validation set [n = 330; HR = 2.17; 95% confidence interval (CI) = 1.38-3.40 and HR = 3.92; 95% CI = 2.27-6.85]. The association between PLS3-positive CTC and prognosis was particularly strong in patients with Dukes B (HR = 4.07; 95% CI = 1.50-11.57) and Dukes C (HR = 2.57; 95% CI = 1.42-4.63). PLS3 is a novel marker for metastatic CRC cells, and it possesses significant prognostic value.

Finding optimal models for small gene networks.

Finding gene networks from microarray data has been one focus of research in recent years. Given search spaces of super-exponential size, researchers have been applying heuristic approaches like greedy algorithms or simulated annealing to infer such networks. However, the accuracy of heuristics is uncertain, which--in combination with the high measurement noise of microarrays--makes it very difficult to draw conclusions from networks estimated by heuristics. We present a method that finds optimal Bayesian networks of considerable size and show first results of the application to yeast data. Having removed the uncertainty due to the heuristic methods, it becomes possible to evaluate the power of different statistical models to find biologically accurate networks.

A Top-r Feature Selection Algorithm for Microarray Gene Expression Data

Most of the conventional feature selection algorithms have a drawback whereby a weakly ranked gene that could perform well in terms of classification accuracy with an appropriate subset of genes will be left out of the selection. Considering this shortcoming, we propose a feature selection algorithm in gene expression data analysis of sample classifications. The proposed algorithm first divides genes into subsets, the sizes of which are relatively small (roughly of size h), then selects informative smaller subsets of genes (of size r <; h) from a subset and merges the chosen genes with another gene subset (of size r) to update the gene subset. We repeat this process until all subsets are merged into one informative subset. We illustrate the effectiveness of the proposed algorithm by analyzing three distinct gene expression data sets. Our method shows promising classification accuracy for all the test data sets. We also show the relevance of the selected genes in terms of their biological functions.

Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models

MOTIVATION Statistical inference of gene networks by using time-course microarray gene expression profiles is an essential step towards understanding the temporal structure of gene regulatory mechanisms. Unfortunately, most of the current studies have been limited to analysing a small number of genes because the length of time-course gene expression profiles is fairly short. One promising approach to overcome such a limitation is to infer gene networks by exploring the potential transcriptional modules which are sets of genes sharing a common function or involved in the same pathway. RESULTS In this article, we present a novel approach based on the state space model to identify the transcriptional modules and module-based gene networks simultaneously. The state space model has the potential to infer large-scale gene networks, e.g. of order 10(3), from time-course gene expression profiles. Particularly, we succeeded in the identification of a cell cycle system by using the gene expression profiles of Saccharomyces cerevisiae in which the length of the time-course and number of genes were 24 and 4382, respectively. However, when analysing shorter time-course data, e.g. of length 10 or less, the parameter estimations of the state space model often fail due to overfitting. To extend the applicability of the state space model, we provide an approach to use the technical replicates of gene expression profiles, which are often measured in duplicate or triplicate. The use of technical replicates is important for achieving highly-efficient inferences of gene networks with short time-course data. The potential of the proposed method has been demonstrated through the time-course analysis of the gene expression profiles of human umbilical vein endothelial cells (HUVECs) undergoing growth factor deprivation-induced apoptosis. AVAILABILITY Supplementary Information and the software (TRANS-MNET) are available at http://daweb.ism.ac.jp/~yoshidar/software/ssm/.

Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks.

We propose a statistical method to estimate gene networks from DNA microarray data and protein-protein interactions. Because physical interactions between proteins or multiprotein complexes are likely to regulate biological processes, using only mRNA expression data is not sufficient for estimating a gene network accurately. Our method adds knowledge about protein-protein interactions to the estimation method of gene networks under a Bayesian statistical framework. In the estimated gene network, a protein complex is modeled as a virtual node based on principal component analysis. We show the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle data. The proposed method improves the accuracy of the estimated gene networks, and successfully identifies some biological facts.

Inferring Gene Regulatory Networks from Time-Ordered Gene Expression Data Using Differential Equations

Spurred by advances in cDNA microarray technology, gene expression data are increasingly becoming available. In time-ordered data, the expression levels are measured at several points in time following some experimental manipulation. A gene regulatory network can be inferred by fitting a linear system of differential equations to the gene expression data. As biologically the gene regulatory network is known to be sparse, we expect most coefficients in such a linear system of differential equations to be zero. In previously proposed methods to infer such a linear system, ad hoc assumptions were made to limit the number of nonzero coefficients in the system. Instead, we propose to infer the degree of sparseness of the gene regulatory network from the data, where we determine which coefficients are nonzero by using Akaikes Information Criterion.

Null space based feature selection method for gene expression data

Feature selection is quite an important process in gene expression data analysis. Feature selection methods discard unimportant genes from several thousands of genes for finding important genes or pathways for the target biological phenomenon like cancer. The obtained gene subset is used for statistical analysis for prediction such as survival as well as functional analysis for understanding biological characteristics. In this paper we propose a null space based feature selection method for gene expression data in terms of supervised classification. The proposed method discards the redundant genes by applying the information of null space of scatter matrices. We derive the method theoretically and demonstrate its effectiveness on several DNA gene expression datasets. The method is easy to implement and computationally efficient.