Novel and simple transformation algorithm for combining microarray data sets

BACKGROUND: With microarray technology, variability in experimental environments such as RNA sources, microarray production, or the use of different platforms, can cause bias. Such systematic differences present a substantial obstacle to the analysis of microarray data, resulting in inconsistent and unreliable information. Therefore, one of the most pressing challenges in the field of microarray technology is how to integrate results from different microarray experiments or combine data sets prior to the specific analysis. RESULTS: Two microarray data sets based on a 17k cDNA microarray system were used, consisting of 82 normal colon mucosa and 72 colorectal cancer tissues. Each data set was prepared from either total RNA or amplified mRNA, and the difference of RNA source between these two data sets was detected by ANOVA (Analysis of variance) model. A simple integration method was introduced which was based on the distributions of gene expression ratios among different microarray data sets. The method transformed gene expression ratios into the form of a reference data set on a gene by gene basis. Hierarchical clustering analysis, density and box plots, and mixture scores with correlation coefficients revealed that the two data sets were well intermingled, indicating that the proposed method minimized the experimental bias. In addition, any RNA source effect was not detected by the proposed transformation method. In the mixed data set, two previously identified subgroups of normal and tumor were well separated, and the efficiency of integration was more prominent in tumor groups than normal groups. The transformation method was slightly more effective when a data set with strong homogeneity in the same experimental group was used as a reference data set. CONCLUSION: Proposed method is simple but useful to combine several data sets from different experimental conditions. With this method, biologically useful information can be detectable by applying various analytic methods to the combined data set with increased sample size.ope

An Attempt for Combining Microarray Data Sets by Adjusting Gene Expressions

PURPOSE: The diverse experimental environments in microarray technology, such as the different platforms or different RNA sources, can cause biases in the analysis of multiple microarrays. These systematic effects present a substantial obstacle for the analysis of microarray data, and the resulting information may be inconsistent and unreliable. Therefore, we introduced a simple integration method for combining microarray data sets that are derived from different experimental conditions, and we expected that more reliable information can be detected from the combined data set rather than from the separated data sets. MATERIALS AND METHODS: This method is based on the distributions of the gene expression ratios among the different microarray data sets and it transforms, gene by gene, the gene expression ratios into the form of the reference data set. The efficiency of the proposed integration method was evaluated using two microarray data sets, which were derived from different RNA sources, and a newly defined measure, the mixture score. RESULTS: The proposed integration method intermixed the two data sets that were obtained from different RNA sources, which in turn reduced the experimental bias between the two data sets, and the mixture score increased by 24.2%. A data set combined by the proposed method preserved the inter-group relationship of the separated data sets. CONCLUSION: The proposed method worked well in adjusting systematic biases, including the source effect. The ability to use an effectively integrated microarray data set yields more reliable results due to the larger sample size and this also decreases the chance of false negatives.ope

Determination of genes related to gastrointestinal tract origin cancer cells using a cDNA microarray

PURPOSE: We evaluated the genome-wide gene expression profiles of various cancer cell lines to identify the gastrointestinal tract cancer cell-related genes. EXPERIMENTAL DESIGN: Gene expression profilings of 27 cancer cell lines and 9 tissues using 7.5K human cDNA microarrays in indirect design with Yonsei reference RNA composed of 11 cancer cell line RNAs were done. The significant genes were selected using significant analysis of microarray in various sets of data. The selected genes were validated using real-time PCR analysis. RESULTS: After intensity-dependent, within-print-tip normalization by loess method, we observed that expression patterns of cell lines and tissues were substantially different, divided in two discrete clusters. Next, we selected 115 genes that discriminate gastrointestinal cancer cell lines from others using significant analysis of microarray. Among the expression profiles of five gastric cancer cell lines, 66 genes were identified as differentially expressed genes related to metastatic phenotype. YCC-16, which was established from the peripheral blood of one advanced gastric cancer patient, produced a unique gene expression pattern resembling the profiles of lymphoid cell lines. Quantitative real-time reverse transcription-PCR results of selected genes, including PXN, KRT8, and ITGB5, were correlated to microarray data and successfully discriminate the gastrointestinal tract cancer cell lines from hematologic malignant cell lines. CONCLUSIONS: A gene expression database could serve as a useful source for the further investigation of cancer biology using the cell lines.ope

Gene copy number change events at chromosome 20 and their association with recurrence in gastric cancer patients

PURPOSE: This study examined the gene copy number change events at chromosome 20 in gastric cancer, and their possible relationship with recurrence using cDNA microarray-based comparative genomic hybridization. EXPERIMENTAL DESIGN: Thirty pairs of gastric tumor and normal gastric tissues were used in the cDNA microarray-based comparative genomic hybridization. The cDNA microarrays containing 17,000 sequence-verified human gene probes were used in a direct comparison design, where genomic DNAs from the normal and tumor tissues were labeled with fluorescent dyes Cy3 and Cy5, respectively, and cohybridized. Genes with log(2) (Cy5/Cy3) > or = 0.58 in at least one case were selected as the amplified genes. In order to search for the association between gene copy number changes and the recurrence status, patients were grouped according to their recurrence status. Gene selection between the two groups was done, and each patient was given a score based on the sum of the selected genes' ratios. Logistic regression analysis was carried out in order to determine if the score of a group of patients was correlated with a recurrence. RESULTS: A group of genes including NCOA6, CYP24A1, PTPN1, and ZNF217 was amplified in gastric cancer. Another group of 39 genes, whose sum of copy number change levels was significantly associated with a poor prognosis for recurrence, was selected (P < 0.05). CONCLUSION: Ninety-six amplified genes at chromosome 20 of gastric cancer are reported. A scoring system based on gene copy changes at chromosome 20 can provide an independent patient grouping system that can distinguish patient recurrence status and survival.ope

체육교사양성프로그램의 실기수업 사례연구

학위논문(석사) --서울대학교 대학원 :체육교육과,2008. 8.Maste

Identification of genes with correlated patterns of variations in DNA copy number and gene expression level in gastric cancer

To identify DNA copy number changes that had a direct influence on mRNA expression in gastric cancer, cDNA microarray-based comparative genomic hybridization (aCGH) and gene expression profiling were performed using 17 K cDNA microarrays. A set of 158 genes showing Pearson correlation coefficients over 0.6 between DNA copy number changes and mRNA expression level variations was selected. In an independent gene expression profiling of 60 tissue samples, the 158 genes were able to distinguish most of the normal and tumor tissues in an unsupervised hierarchical clustering, suggesting that the differential expression patterns displayed by this specific group of genes are most likely based on the gene copy number changes. Furthermore, 43 statistically significant (P < 0.01) genes were selected that correctly distinguished all of the tissue samples. The copy number changes detected by aCGH can be verified by fluorescence in situ hybridization and real-time polymerase chain reaction. The selected genes include those that were previously identified as being tumor suppressors or deleted in various tumors, including GATA binding protein 4 (GATA4), monoamine oxidase A (MAOA), cyclin C (CCNC), and oncogenes including malignant fibrous histiocytoma amplified sequence 1 (MFHAS1/MASL1), high mobility group AT-hook 2 (HMGA2), PPAR binding protein (PPARBP), growth factor receptor-bound protein 7 (GRB7), and TBC1 (tre-2, BUB2, cdc16) domain family, member 1 (TBC1D1).ope