Biomedical subjects

Shigeyuki Oba

Publications and source records attributed to Shigeyuki Oba.

8 recordsLinked to original sources

PubMed2005

A review of DNA microarray analysis of human neuroblastomas.

Neuroblastoma (NBL) is an enigmatic tumor with heterogeneous clinical behaviors including maturation, regression, and aggressive growth. Despite recent progress in therapeutic strategies against advanced NBL, long-term outcomes still remain very poor. The prediction of cancer prognosis is one of the most urgent demands to initiate the suitable treatment of NBL. Recent papers have demonstrated that cancers can be diagnosed on the basis of gene expression profiling. We have been proceeded NBL cDNA project to collect a large number of genes expressed in NBLs, to identify the genes differentially expressed between favorable and unfavorable NBLs, and to make an NBL-proper cDNA chip for large-scale analysis of NBL tumors. Computational analysis of gene expression data in NBLs identified many prognosis-related genes and provided a classifier to predict the patient prognosis with high efficiency. Conversion of these findings into better diagnosis and treatment is now underway. Thus, molecular profiling of NBL has become a feasible tool for clinical applications.

DNA, Complementary↗

PubMed2005

Prediction of recurrence in advanced gastric cancer patients after curative resection by gene expression profiling.

The prognosis of patients with advanced gastric cancer remains unfavorable. Even after curative resection, 40% of patients with advanced gastric cancer die of recurrence. Conventional clinicopathlogic findings are sometimes inadequate for predicting recurrence in individuals. Hence, we tried to construct a new diagnostic system, which predicts recurrence in patients with advanced gastric cancer after curative resection based on molecular analysis. Gastric cancer progression is a function of multiple genetic events that may affect the expression of large number of genes. We performed gene expression profiling with 2,304 genes in 60 advanced gastric cancer patients who underwent curative resection using a PCR array technique, a high-throughput quantitative RT-PCR technique. The diagnostic system, which was constructed from the learning set comprised of 40 patients with the most informative 29 genes, classified each case into a good-signature or poor-signature group. Then, we confirmed the predictive performance in an additional test set comprised of 20 patients, and the prediction accuracy for recurrence was 75%. Kaplan-Meier analysis revealed significant difference between the good-signature and the poor-signature group (p = 0.0125). Especially in patients with smaller tumor (< or = 5 cm), less developed LN metastasis (N(0,1)), or earlier stage (stages I and II), the prediction accuracy was high (88.9%, 84.6%, or 81.8%, respectively). Our diagnostic system based on systematic analysis of gene expression profiling can predict the recurrence at clinically meaningful level. By combining our system with conventional clinicopathologic factors, we can improve the prediction of recurrence in patients with advanced gastric cancer who underwent curative surgery.

Aged↗

PubMed2005

Adaptor-tagged competitive polymerase chain reaction: amplification bias and quantified gene expression levels.

Adaptor-tagged competitive polymerase chain reaction (ATAC-PCR) is an advanced version of quantitative competitive PCR characterized by the addition of unique adaptors to different cDNA samples. It is currently the only quantitative PCR technique that enables large-scale gene expression analysis. Multiplex application of ATAC-PCR employs seven adaptors, two or three of which are used as controls to generate a calibration curve. The characteristics of the ATAC-PCR method for large-scale data production, including any adaptor- and gene-dependent amplification biases, were evaluated by using this method to analyze the expression of 384 mouse brain genes. Short adaptors tended to amplify at higher efficiency than did long adaptors. The population of genes with a high amplification bias increased with the use of short adaptors. Subtracting the median value of all adaptor-dependent biases could reduce this bias; the majority of genes displayed a small gene-dependent bias, which facilitated reliable quantification. We modified ATAC-PCR to estimate molecular numbers of transcripts by introducing synthetic standards. This modification demonstrated that gene expression levels in mammalian cells are varied over seven orders of magnitude.

Animals↗

PubMed2005

Expression profiling using a tumor-specific cDNA microarray predicts the prognosis of intermediate risk neuroblastomas.

To predict the prognosis of neuroblastoma patients and choose a better therapeutic protocol, we developed a cDNA microarray carrying 5340 genes obtained from primary neuroblastomas and examined 136 tumor samples. We made a probabilistic output statistical classifier that provided a high accuracy in prognosis prediction (89% at 5 years) and a highly reliable method to validate it. Kaplan-Meier analysis indicated that the patients in an intermediate group defined by existing markers are divided by microarray into two further groups with 5 year survivals for 36% and 89% of patients (p < 10(-4)), i.e., with unfavorably and favorably predicted neuroblastomas, respectively. According to these results, we developed a gene subset chip for a clinical tool, for which our classifier exhibited 88% prediction accuracy.

Algorithms↗

PubMed2004

Molecular-based prediction of early recurrence in hepatocellular carcinoma.

BACKGROUND/AIMS: Hepatocellular carcinoma (HCC) has a very poor prognosis, due to the high incidence of tumor recurrence. As the current morphological indicators are often insufficient for therapeutic decisions, we sought to identify additional biologic indicators for early recurrence. METHODS: We analyzed gene expression using a PCR-based array of 3,072 genes in 100 HCC patients. Informative genes predicting early intrahepatic recurrence were selected by random permutation testing, and a weighted voting prediction method was constructed. Following estimation of prediction accuracy, a multivariate Cox analysis was performed. RESULTS: By permutation testing, we selected 92 genes demonstrated distinct expression patterns differing significantly between recurrence cases and recurrence-free cases. Our prediction method, using the 20 top-ranked genes, correctly predicted the early intrahepatic recurrence for 29 of 40 cases within the validation group, and the odds ratio was 6.8 (95%CI 1.7-27.5, P = 0.010). The 2-year recurrence rates in the patients with the good signature and those with the poor signature were 29.4 and 73.9%, respectively. Multivariate Cox analysis revealed that molecular-signature was an independent indicator for recurrence (hazard ratio 3.82, 95%CI 1.44-10.10, P = 0.007). CONCLUSIONS: Our molecular-based prediction method using 20 genes is clinically useful to predict early recurrence of HCC.

Aged↗

PubMed2003

A Bayesian missing value estimation method for gene expression profile data.

MOTIVATION: Gene expression profile analyses have been used in numerous studies covering a broad range of areas in biology. When unreliable measurements are excluded, missing values are introduced in gene expression profiles. Although existing multivariate analysis methods have difficulty with the treatment of missing values, this problem has received little attention. There are many options for dealing with missing values, each of which reaches drastically different results. Ignoring missing values is the simplest method and is frequently applied. This approach, however, has its flaws. In this article, we propose an estimation method for missing values, which is based on Bayesian principal component analysis (BPCA). Although the methodology that a probabilistic model and latent variables are estimated simultaneously within the framework of Bayes inference is not new in principle, actual BPCA implementation that makes it possible to estimate arbitrary missing variables is new in terms of statistical methodology. RESULTS: When applied to DNA microarray data from various experimental conditions, the BPCA method exhibited markedly better estimation ability than other recently proposed methods, such as singular value decomposition and K-nearest neighbors. While the estimation performance of existing methods depends on model parameters whose determination is difficult, our BPCA method is free from this difficulty. Accordingly, the BPCA method provides accurate and convenient estimation for missing values. AVAILABILITY: The software is available at http://hawaii.aist-nara.ac.jp/~shige-o/tools/.

Algorithms↗

PubMed2003

Identification of expressed genes linked to malignancy of human colorectal carcinoma by parametric clustering of quantitative expression data.

BACKGROUND: Individual human carcinomas have distinct biological and clinical properties: gene-expression profiling is expected to unveil the underlying molecular features. Particular interest has been focused on potential diagnostic and therapeutic applications. Solid tumors, such as colorectal carcinoma, present additional obstacles for experimental and data analysis. RESULTS: We analyzed the expression levels of 1,536 genes in 100 colorectal cancer and 11 normal tissues using adaptor-tagged competitive PCR, a high-throughput reverse transcription-PCR technique. A parametric clustering method using the Gaussian mixture model and the Bayes inference revealed three groups of expressed genes. Two contained large numbers of genes. One of these groups correlated well with both the differences between tumor and normal tissues and the presence or absence of distant metastasis, whereas the other correlated only with the tumor/normal difference. The third group comprised a small number of genes. Approximately half showed an identical expression pattern, and cancer tissues were classified into two groups by their expression levels. The high-expression group had strong correlation with distant metastasis, and a poorer survival rate than the low-expression group, indicating possible clinical applications of these genes. In addition to c-yes, a homolog of a viral oncogene, prognostic indicators included genes specific to glial cells, which gives a new link between malignancy and ectopic gene expression. CONCLUSIONS: The malignancy of human colorectal carcinoma is correlated with a unique expression pattern of a specific group of genes, allowing the classification of tumor tissues into two clinically distinct groups.

Cluster Analysis↗

PubMed2003

Molecular features of non-B, non-C hepatocellular carcinoma: a PCR-array gene expression profiling study.

BACKGROUND/AIMS: Hepatocellular carcinoma (HCC) usually develops following chronic liver inflammation caused by hepatitis C or B virus. Through expression profiling in a rare type of HCC, for which the causes are unknown, we sought to find key genes responsible for each step of hepatocarcinogenesis in the absence of viral influence. METHODS: We used 68 non-B, non-C liver tissues (20 HCC, 17 non-tumor, 31 normal liver) for expression profiling with PCR-array carrying 3072 genes known to be expressed in liver tissues. To select the differentially expressed genes, we performed random permutation testing. A weighted voting classification algorithm was used to confirm the reliability of gene selection. We then compared these genes with the results of previous expression profiling studies. RESULTS: A total of 220 differentially expressed genes were selected by random permutation tests. The classification accuracies using these genes were 91.8, 92.0 and 100.0% by a leave-one-out cross-validation, an additional PCR-array dataset and a Stanford DNA microarray dataset, respectively. By comparing our results with previous reports on virus-infected HCC, four genes (ALB, A2M, ECHS1 and IGFBP3) were commonly selected in some studies. CONCLUSIONS: The 220 differentially expressed genes selected by PCR-array are potentially responsible for hepatocarcinogenesis in the absence of viral influence.

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