Biomedical subjects

Taizo Hanai

Publications and source records attributed to Taizo Hanai.

13 recordsLinked to original sources

PubMed2007

A specific gene-expression signature quantifies the degree of hepatic fibrosis in patients with chronic liver disease.

AIM: To study a more accurate quantification of hepatic fibrosis which would provide clinically useful information for monitoring the progression of chronic liver disease. METHODS: Using a cDNA microarray containing over 22000 clones, we analyzed the gene-expression profiles of non-cancerous liver in 74 patients who underwent hepatic resection. We calculated the ratio of azan-stained: total area, and determined the morphologic fibrosis index (MFI), as a mean of 9 section-images. We used the MFI as a reference standard to evaluate our method for assessing liver fibrosis. RESULTS: We identified 39 genes that collectively showed a good correlation (r > 0.50) between gene-expression and the severity of liver fibrosis. Many of the identified genes were involved in immune responses and cell signaling. To quantify the extent of liver fibrosis, we developed a new genetic fibrosis index (GFI) based on gene-expression profiling of 4 clones using a linear support vector regression analysis. This technique, based on a supervised learning analysis, correctly quantified the various degrees of fibrosis in both 74 training samples (r = 0.76, 2.2% vs 2.8%, P < 0.0001) and 12 independent additional test samples (r = 0.75, 9.8% vs 8.6%, P < 0.005). It was far better in assessing liver fibrosis than blood markers such as prothrombin time (r = -0.53), type IV collagen 7s (r = 0.48), hyaluronic acid (r = 0.41), and aspartate aminotransferase to platelets ratio index (APRI) (r = 0.38). CONCLUSION: Our cDNA microarray-based strategy may help clinicians to precisely and objectively monitor the severity of liver fibrosis.

Biomarkers↗

PubMed2006

Specific gene-expression profiles of noncancerous liver tissue predict the risk for multicentric occurrence of hepatocellular carcinoma in hepatitis C virus-positive patients.

BACKGROUND: Hepatitis C virus (HCV) infection produces chronic hepatitis, cirrhosis, and, ultimately, hepatocellular carcinoma (HCC). A molecular analysis of the damaged liver tissues infected with HCV may identify specific gene-expression profiles associated with a risk for liver carcinogenesis. METHODS: Forty patients with HCV-positive HCC were classified into two groups: single nodular HCC group (n = 28) and multicentric HCC group (n = 12). Using a complementary DNA microarray, we compared the gene-expression patterns of the noncancerous liver tissue specimens between the two groups. We also identified the differentially expressed genes related to multicentric recurrence in the liver remnant. We then evaluated whether a specific gene-expression profile can accurately estimate the risk for multicentric hepatocarcinogenesis. RESULTS: We selected the 230 differentially expressed genes in the multicentric HCC group. A hierarchical clustering analysis identified a cluster that might be closely associated with the multicentric occurrence of HCC. On the basis of the gene-expression profiling of the 36 genes commonly associated with both multicentric HCC and multicentric recurrence, we created a scoring system to estimate the risk for multicentric hepatocarcinogenesis. The prediction score of patients in the multicentric HCC group with multicentric recurrence (19.9 +/- 9.2) was significantly higher (P < .05) than that in the single nodular HCC group without multicentric recurrence (-1.8 +/- 12.7). CONCLUSIONS: Specific gene-expression signatures in noncancerous liver tissue may help to accurately predict the risk for developing HCC.

Aged↗

PubMed2006

Novel technique for preprocessing high dimensional time-course data from DNA microarray: mathematical model-based clustering.

MOTIVATION: Classifying genes into clusters depending on their expression profiles is one of the most important analysis techniques for microarray data. Because temporal gene expression profiles are indicative of the dynamic functional properties of genes, the application of clustering analysis to time-course data allows the more precise division of genes into functional classes. Conventional clustering methods treat the sampling data at each time point as data obtained under different experimental conditions without considering the continuity of time-course data between time periods t and t+1. Here, we propose a method designated mathematical model-based clustering (MMBC). RESULTS: The proposed method, designated MMBC, was applied to artificial data and time-course data obtained using Saccharomyces cerevisiae. Our method is able to divide data into clusters more accurately and coherently than conventional clustering methods. Furthermore, MMBC is more tolerant to noise than conventional clustering methods. AVAILABILITY: Software is available upon request. CONTACT: taizo@brs.kyushu-u.ac.jp.

Algorithms↗

PubMed2006

Application of bioinformatics for DNA microarray data to bioscience, bioengineering and medical fields.

In the 1990s, DNA microarray or DNA chip as a novel biological experimental technology was developed, which enables the comprehensive measurement of the expression levels of hundreds of genes, simultaneously. Using this technique, a comprehensive understanding of the cell can be achieved. However, because even simple life forms, such as microorganisms, have more than a thousand kinds of genes, the data from a DNA chip cannot be analyzed without statistical and informational technology. Bioinformatics is the interdisciplinary research field integrating molecular biology with informatics, and it is expected to have a huge impact on the bioscientific, bioengineering and medical fields. There are many techniques in bioinformatics for the analysis of DNA microarray data; however, these are mainly divided into fold-change analysis, clustering, classification, genetic network analysis, and simulation. In this review, these techniques are briefly explained by using some examples.

Animals↗

PubMed2005

Global analysis of altered gene expressions during the process of esophageal squamous cell carcinogenesis in the rat: a study combined with a laser microdissection and a cDNA microarray.

The genetic alterations that occur during esophageal tumorigenesis have yet to be determined. We previously established a Wister rat carcinogenesis model of esophageal squamous cell carcinoma. To understand more about the molecular mechanisms during carcinogenesis, we produced esophageal neoplastic lesions by administering N-amyl-N-methylnitrosamine and 12-O-tetradecanoylphorbol-13-acetate to rats. We used laser microdissection to specifically isolate the cells from the normal epithelium, papilloma, dysplasia, and invasive carcinoma. Using a cDNA microarray representing 14,815 clones, we then analyzed the gene expression profiles for each esophageal lesion. The number of differentially expressed genes compared with the normal control dramatically increased in a step-by-step fashion from normal epithelium (1,151 +/- 119 genes) to papilloma (1,899 +/- 543 genes), dysplasia (1,991 +/- 193 genes), and invasive carcinoma (2,756 +/- 87 genes). A hierarchical clustering analysis showed that the three stages of normal epithelium, dysplasia (papilloma), and invasive carcinoma could be clearly classified, whereas the gene expression patterns of papilloma and dysplasia were indistinguishable. Using the Fisher criterion, we also identified 50 genes whose expression level had either significantly increased or decreased in a step-by-step manner from the normal epithelium to dysplasia and then finally to invasive carcinoma. Many of these genes were not previously known to be associated with esophageal carcinogenesis. The present findings in our rat model thus seem to provide us with a better understanding of the molecular alterations that occur during esophageal carcinogenesis and hopefully will also help lead to the development of novel diagnostic and therapeutic targets.

Animals↗

PubMed2004

Application of knowledge information processing methods to biochemical engineering, biomedical and bioinformatics fields.

In biochemical and biomedical engineering fields there are a variety of phenomena with many complex chemical reactions, in which many genes and proteins affect transcription or enzyme activity of others. It is difficult to analyze and estimate many of these phenomena using conventional mathematical models. Recently some knowledge information processing methods, such as the artificial neural network (ANN), fuzzy reasoning, fuzzy neural network (FNN), fuzzy adaptive resonance theory (fuzzy ART) and the genetics algorithm (GA), were developed in the computer science field and have been applied to analysis in a variety of research fields. In this chapter, these methods will be briefly reviewed. Next, the application of these methods in the biochemical field will be introduced, instancing two examples in actual industrial processes. In addition, the application in the biomedical and bioinformatics field as another attractive field will be reviewed. Two examples are our research such as the prediction of prognosis for cancer patients from DNA microarray data using FNN and gene clustering for DNA microarray data using fuzzy ART.

Artificial Intelligence↗

PubMed2004

A preprocessing method for inferring genetic interaction from gene expression data using Boolean algorithm.

Unknown genetic regulation mechanisms are expected to be discovered by information technology using large amount of biological data especially for gene expression data. In this study, we propose a novel inferring method for genetic interactions that combines our original preprocessing method and the Boolean algorithm. First, the performance of our method was evaluated using artificial data. The results showed that our method was able to infer genetic interactions with high specificity (specificity=0.629). Then, using our method, the genetic interaction was inferred from the experimental time course data collected using microarray on 69 genes of cell cycle for Saccharomyces cerevisiae. Our method estimated about 80% of all genetic interactions in Kyoto Encyclopedia Genes and Genomes (KEGG) for these genes. Furthermore, our method was able to infer several other genetic interactions that are not included in KEGG but whose existence is supported by other biological reports.

Journal Article↗

PubMed2003

Analysis of organic solvent tolerance in Escherichia coli using gene expression profiles from DNA microarrays.

To investigate the biological mechanism of organic solvent tolerance (OST), DNA microarrays were used to collect and compare the gene expression profiles of normal and organic solvent-tolerant Escherichia coli strains. First, we compared the tolerant-strain OST3410 to its sensitive parent strain JA300 in the absence of organic solvents. Numerous genes showed higher expression levels in OST3410, and Northern analysis was used to confirm the higher expression level of some genes. Next, the gene expression profiles of JA300 and OST3410 exposed to hexane as an organic solvent were investigated and compared with JA300 before exposure to organic solvent. In OST3410 and JA300, 115 and 47 hexane-induced genes were found, respectively. As candidates for genes related to OST, we focused on six genes: cysD, marA, mg1B, tnaA, tnaB and yihM, which were upregulated by hexane in both strains. When these genes were over-expressed on plasmids, only the marA plasmid increased OST activity. It should be noted that we succeeded in finding a gene related to OST activity using only DNA microarray data, without any biochemical or biological knowledge.

Journal Article↗

PubMed2003

Prognostic models in patients with non-small-cell lung cancer using artificial neural networks in comparison with logistic regression.

It is difficult to precisely predict the outcome of each individual patient with non-small-cell lung cancer (NSCLC) by using conventional statistical methods and ordinary clinico-pathological variables. We applied artificial neural networks (ANN) for this purpose. We constructed a prognostic model for 125 NSCLC patients with 17 potential input variables, including 12 clinico-pathological variables (age, sex, smoking index, tumor size, p factor, pT, pN, stage, histology) and 5 immunohistochemical variables (p27 percentage, p27 intensity, p53, cyclin D1, retinoblastoma (RB)), by using the parameter-increasing method (PIM). Using the resultant ANN model, prediction was possible in 104 of 125 patients (83%, judgment ratio (JR)) and accuracy for prediction of survival at 5 years was 87%. On the other hand, JR and survival prediction accuracy in the logistic regression (LR) model were 37% and 78%, respectively. In addition, ANN outperformed LR for prediction of survival at 1 or 3 years. In these cases, PIM selected p27 intensity and cyclin D1 for the 3-year survival model and p53 for the 1-year survival model in addition to clinico-pathological variables. Finally, even in an independent validation data set of 48 patients, who underwent surgery 10 years later, the present ANN model could predict outcome of patients at 5 years with the JR and accuracy of 81% and 77%, respectively. This study demonstrates that ANN is a potentially more useful tool than conventional statistical methods for predicting survival of patients with NSCLC and that inclusion of relevant molecular markers as input variables enhances its predictive ability.

Adenocarcinoma↗

PubMed2002

Artificial neural network predictive model for allergic disease using single nucleotide polymorphisms data.

The purpose of this study was to develop a novel diagnostic prediction method for allergic diseases from the data of single nucleotide polymorphisms (SNPs) using an artificial neural network (ANN). We applied the prediction method to four allergic diseases, such as atopic dermatitis (AD), allergic conjunctivitis (AC), allergic rhinitis (AR) and bronchial asthma (BA), and verified its predictive ability. Almost all the learning data were precisely predicted. Regarding the evaluation data, the learned ANN model could correctly predict a diagnosis with more than 78% accuracy. We also analyzed the SNP data using multiple regression analysis (MRA). Using the MRA model, less than 10% of patients with the above allergic diseases were correctly diagnosed, while this figure was more than 75% for persons without allergic diseases. From these results, it was shown that the ANN model was superior to the MRA model with respect to predictive ability of allergic diseases. Moreover, we used two different methods to convert the genetic polymorphism data into numerical data. Using both methods, diagnostic predictions were quite precise and almost the same predictive abilities were observed. This is the first study showing the application and usefulness of an ANN for the prediction of allergic diseases based on SNP data.

Journal Article↗

PubMed2002

Analysis of expression profile using fuzzy adaptive resonance theory.

MOTIVATION: It is well understood that the successful clustering of expression profiles give beneficial ideas to understand the functions of uncharacterized genes. In order to realize such a successful clustering, we investigate a clustering method based on adaptive resonance theory (ART) in this report. RESULTS: We apply Fuzzy ART as a clustering method for analyzing the time series expression data during sporulation of Saccharomyces cerevisiae. The clustering result by Fuzzy ART was compared with those by other clustering methods such as hierarchical clustering, k-means algorithm and self-organizing maps (SOMs). In terms of the mathematical validations, Fuzzy ART achieved the most reasonable clustering. We also verified the robustness of Fuzzy ART using noised data. Furthermore, we defined the correctness ratio of clustering, which is based on genes whose temporal expressions are characterized biologically. Using this definition, it was proved that the clustering ability of Fuzzy ART was superior to other clustering methods such as hierarchical clustering, k-means algorithm and SOMs. Finally, we validate the clustering results by Fuzzy ART in terms of biological functions and evidence. AVAILABILITY: The software is available at http//www.nubio.nagoya-u.ac.jp/proc/index.html

Algorithms↗

PubMed2002

Fuzzy neural network applied to gene expression profiling for predicting the prognosis of diffuse large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is the largest category of aggressive lymphomas. Less than 50% of patients can be cured by combination chemotherapy. Microarray technologies have recently shown that the response to chemotherapy reflects the molecular heterogeneity in DLBCL. On the basis of published microarray data, we attempted to develop a long-overdue method for the precise and simple prediction of survival of DLBCL patients. We developed a fuzzy neural network (FNN) model to analyze gene expression profiling data for DLBCL. From data on 5857 genes, this model identified four genes (CD10, AA807551, AA805611 and IRF-4) that could be used to predict prognosis with 93% accuracy. FNNs are powerful tools for extracting significant biological markers affecting prognosis, and are applicable to various kinds of expression profiling data for any malignancy.

DNA-Binding Proteins↗

PubMed2002

Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules.

Elucidating the interaction between major histocompatibility complex (MHC) molecules and antigenic peptides is fundamental to better understanding of the processes involved in immune responses and for the development of innovative immunotherapies. In the present study, hidden Markov models (HMM) were combined with the successive state splitting (SSS) algorithm for optimization of the HMM structure, to predict peptide binders to the human MHC class II molecule HLA-DRB1*0101. The predictive performance of our model (S-HMM) was compared with fully connected HMM and artificial neural network (ANN) methods using the relative operating characteristic (ROC) analysis. The S-HMM predictions had values of ROC > or = 0.85 which was at least as good, or better than the comparison methods. In addition, S-HMM is trained on positive data only and does not require exhaustive data preprocessing, such as peptide alignment. Our results demonstrated that S-HMM combines the high accuracy of predictions with the simplicity of implementation and is therefore useful for analyzing MHC class II binding peptides. In particular the S-HMM may be trained using only positive data and, the preprocessing of training data, such as peptide alignment and the selection of binding cores, is not required in this method.

Journal Article↗