Biomedical subjects

Tatsuya Ando

Publications and source records attributed to Tatsuya Ando.

7 recordsLinked to original sources

PubMed2006

The expression profile of microRNAs in mouse embryos.

MicroRNAs (miRNAs), which are non-coding RNAs 18-25 nt in length, regulate a variety of biological processes, including vertebrate development. To identify new species of miRNA and to simultaneously obtain a comprehensive quantitative profile of small RNA expression in mouse embryos, we used the massively parallel signature sequencing technology that potentially identifies virtually all of the small RNAs in a sample. This approach allowed us to detect a total of 390 miRNAs, including 195 known miRNAs covering approximately 80% of previously registered mouse miRNAs as well as 195 new miRNAs, which are so far unknown in mouse. Some of these miRNAs showed temporal expression profiles during prenatal development (E9.5, E10.5 and E11.5). Several miRNAs were positioned in polycistron clusters, including one particular large transcription unit consisting of 16 known and 23 new miRNAs. Our results indicate existence of a significant number of new miRNAs expressed at specific stages of mammalian embryonic development and which were not detected by earlier methods.

Animals↗

PubMed2005

Analysis of SMAD4/DPC4 gene alterations in multiploid colorectal carcinomas.

BACKGROUND: Although recent animal studies have shown that SMAD4/DPC4 gene alterations are essential for late-stage intestinal tumorigenesis, the role of SMAD4/DPC4 gene alterations in primary human colorectal carcinomas is not fully understood. Therefore, we attempted to clarify the role of the SMAD4/DPC4 gene during tumor progression of colorectal carcinoma. METHODS: Differences in allelic imbalance (AI) and mutations of the SMAD4/DPC4 gene between diploid and aneuploid populations were analyzed for 30 sporadic DNA multiploid colorectal carcinomas (used as a tumor progression model and defined as the coexistence of diploid and aneuploid cells within the same tumor). The crypt isolation technique was coupled with DNA cytometric sorting and a polymerase chain reaction assay. In addition, hypermethylation of the promoter region was examined to clarify whether inactivation of gene expression occurred. RESULTS: Although a SMAD4/DPC4 gene AI was detected in only 5 of 27 informative diploid populations, 25 of 27 aneuploid populations had a SMAD4/DPC4 gene AI. Mutation of the SMAD4/DPC4 gene was detected in only one aneuploid population of multiploid colorectal carcinomas, but not in the corresponding diploid population. In total, 20 available multiploid carcinomas were selected for methylation analysis, and no evidence of hypermethylation of the promoter region was found. CONCLUSIONS: We suggest that, although mutation of the SMAD4/DPC4 gene and hypermethylation of the promoter region are infrequent events in colorectal tumorigenesis, AI at the SMAD4/DPC4 gene locus may play a key role in the progression of colorectal carcinomas.

Adenocarcinoma↗

PubMed2004

Prognostic predictor with multiple fuzzy neural models using expression profiles from DNA microarray for metastases of breast cancer.

Gene expression profiling data from DNA microarray were analyzed using the fuzzy neural network (FNN) modeling method for predicting the distant metastases of breast cancer. The best model consisting of five genes was able to predict metastases of breast cancer with 94% accuracy. Furthermore, 100% accuracy was achieved by majoritarian decision using only 25 genes from five noninferior models which were constructed independently. From the constructed model, gene expression rules, which may cause distant metastases, were explicitly extracted and 60% of the metastases cases could be explained by this rule. The FNN modeling method described in this paper enables precise extraction of significant biological markers affecting prognosis without prior knowledge.

Journal Article↗

PubMed2003

Selection of causal gene sets for lymphoma prognostication from expression profiling and construction of prognostic fuzzy neural network models.

To assess the response of lymphomas to chemotherapy, gene expression profiling data from DNA microarrays were analyzed using the fuzzy neural network (FNN) modeling method. We used the FNN modeling method to produce 10 noninferior models. Using these models, we were able to predict diffuse large B-cell lymphoma (DLBCL) patient outcome with 93% accuracy. Of the 37 genes in the 10 models, 13 genes were repeatedly selected, indicating that these genes are important for prognostication. On Kaplan-Meier plots of overall survival, patients predicted by the FNN model to be cured survived significantly longer than those predicted to be refractory (P<0.0001), indicating that the FNN could successfully identify patients with a relatively poor prognosis among low-clinical-risk patients. The FNN modeling method presented here is able to precisely extract significant biological markers affecting prognosis.

Journal Article↗

PubMed2003

Multiple fuzzy neural network system for outcome prediction and classification of 220 lymphoma patients on the basis of molecular profiling.

A fuzzy neural network (FNN) using gene expression profile data can select combinations of genes from thousands of genes, and is applicable to predict outcome for cancer patients after chemotherapy. However, wide clinical heterogeneity reduces the accuracy of prediction. To overcome this problem, we have proposed an FNN system based on majoritarian decision using multiple noninferior models. We used transcriptional profiling data, which were obtained from "Lymphochip" DNA microarrays (http://llmpp.nih.gov/DLBCL), reported by Rosenwald (N Engl J Med 2002; 346: 1937-47). When the data were analyzed by our FNN system, accuracy (73.4%) of outcome prediction using only 1 FNN model with 4 genes was higher than that (68.5%) of the Cox model using 17 genes. Higher accuracy (91%) was obtained when an FNN system with 9 noninferior models, consisting of 35 independent genes, was used. The genes selected by the system included genes that are informative in the prognosis of Diffuse large B-cell lymphoma (DLBCL), such as genes showing an expression pattern similar to that of CD10 and BCL-6 or similar to that of IRF-4 and BCL-4. We classified 220 DLBCL patients into 5 groups using the prediction results of 9 FNN models. These groups may correspond to DLBCL subtypes. In group A containing half of the 220 patients, patients with poor outcome were found to satisfy 2 rules, i.e., high expression of MAX dimerization with high expression of unknown A (LC_26146), or high expression of MAX dimerization with low expression of unknown B (LC_33144). The present paper is the first to describe the multiple noninferior FNN modeling system. This system is a powerful tool for predicting outcome and classifying patients, and is applicable to other heterogeneous diseases.

Cluster Analysis↗

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↗

PubMedDate not supplied

Upper gastrointestinal disorders induced by non-steroidal anti-inflammatory drugs.

BACKGROUND/AIMS: We examined the characteristics of upper gastrointestinal (GI) disorders induced by non-steroidal anti-inflammatory drugs (NSAIDs). METHODOLOGY: The questionnaire investigation was performed over a five-year period. RESULTS: A study was performed on 354 patients (161 men and 193 women with mean ages of 66.0 and 70.7 years, respectively) who developed NSAID associated upper GI disorders: 21 patients had acute gastric mucosal lesion (AGML), 212 had gastric ulcer, 63 had duodenal ulcer, 17 had gastroduodenal ulcers and 41 other cases. About 75% of patients received NSAIDs for orthopedic conditions. Sixty percent of gastric disorders induced by NSAIDs affected the antrum or angulus of the stomach. The incidence of disorders of the gastric antrum was significantly higher in women than in men whilst the incidence of disorders on the gastric angulus was significantly higher in men than in women (p<0.05). The proportion of patients with GI symptoms was significantly lower in patients over 65 years old than in those under 65 years old (p<0.05). The time taken to achieve the healing stage was significantly longer in patients with greater than 3 months NSAID ingestion compared to patients that had received NSAIDs for less than 3 months (p<0.05). CONCLUSIONS: We should examine the association between upper GI disorders induced by NSAIDs and H. pylori as well as the rate of GI disorders induced by new Cox-2 inhibitors.

Adult↗