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DNA microarray technologies are used extensively to profile the expression levels of thousands of genes under various conditions, yielding extremely large data-matrices. Thus, analyzing this information and extracting biologically relevant knowledge becomes a considerable challenge. A classical approach for tackling this challenge is to use clustering (also known as one-way clustering) methods where genes (or respectively samples) are grouped together based on the similarity of their expression profiles across the set of all samples (or respectively genes). An alternative approach is to develop biclustering methods to identify local patterns in the data. These methods extract subgroups of genes that are co-expressed across only a subset of samples and may feature important biological or medical implications. In this study we evaluate 13 biclustering and 2 clustering (k-means and hierarchical) methods. We use several approaches to compare their performance on two real gene expression data sets. For this purpose we apply four evaluation measures in our analysis: (1) we examine how well the considered (bi)clustering methods differentiate various sample types; (2) we evaluate how well the groups of genes discovered by the (bi)clustering methods are annotated with similar Gene Ontology categories; (3) we evaluate the capability of the methods to differentiate genes that are known to be specific to the particular sample types we study and (4) we compare the running time of the algorithms. In the end, we conclude that as long as the samples are well defined and annotated, the contamination of the samples is limited, and the samples are well replicated, biclustering methods such as Plaid and SAMBA are useful for discovering relevant subsets of genes and samples.

Background: Meta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given tumour type ('outlier genes'), a hallmark of potential oncogenes., Methodology: A new statistical method (the gene tissue index, GTI) was developed by modifying and adapting algorithms originally developed for statistical problems in economics. We compared the potential of the GTI to detect outlier genes in meta-datasets with four previously defined statistical methods, COPA, the OS statistic, the t-test and ORT, using simulated data. We demonstrated that the GTI performed equally well to existing methods in a single study simulation. Next, we evaluated the performance of the GTI in the analysis of combined Affymetrix gene expression data from several published studies covering 392 normal samples of tissue from the central nervous system, 74 astrocytomas, and 353 glioblastomas. According to the results, the GTI was better able than most of the previous methods to identify known oncogenic outlier genes. In addition, the GTI identified 29 novel outlier genes in glioblastomas, including TYMS and CDKN2A. The over-expression of these genes was validated in vivo by immunohistochemical staining data from clinical glioblastoma samples. Immunohistochemical data were available for 65% (19 of 29) of these genes, and 17 of these 19 genes (90%) showed a typical outlier staining pattern. Furthermore, raltitrexed, a specific inhibitor of TYMS used in the therapy of tumour types other than glioblastoma, also effectively blocked cell proliferation in glioblastoma cell lines, thus highlighting this outlier gene candidate as a potential therapeutic target., Conclusions/significance: Taken together, these results support the GTI as a novel approach to identify potential oncogene outliers and drug targets. The algorithm is implemented in an R package (Text S1).

Our previous microarray study showed that the non-specific cytotoxic cell receptor protein 1 (Nccrp1) transcript is significantly upregulated in the gastric mucosa of carbonic anhydrase IX (CA IX)-deficient (Car9(-/-)) mice. In this paper, we aimed to characterize human NCCRP1 and to elucidate its relationship to CA IX. Recombinant NCCRP1 protein was expressed in Escherichia coli, and a novel polyclonal antiserum was raised against the purified full-length protein. Immunocytochemistry showed that NCCRP1 is expressed intracellularly, even though it has previously been described as a transmembrane protein. Using bioinformatic analyses, we identified orthologs of NCCRP1 in 35 vertebrate genomes, and up to five paralogs per genome. These paralogs are FBXO genes whose protein products are components of the E3 ubiquitin ligase complexes. NCCRP1 proteins have no signal peptides or transmembrane domains. NCCRP1 has mainly been studied in fish and was thought to be responsible for the cytolytic function of nonspecific cytotoxic cells (NCCs). Our analyses showed that in humans, NCCRP1 mRNA is expressed in tissues containing squamous epithelium, whereas it shows a more ubiquitous tissue expression pattern in mice. Neither human nor mouse NCCRP1 expression is specific to immune tissues. Silencing CA9 using siRNAs did not affect NCCRP1 levels, indicating that its expression is not directly regulated by CA9. Interestingly, silencing NCCRP1 caused a statistically significant decrease in the growth of HeLa cells. These studies provide ample evidence that the current name, "non-specific cytotoxic cell receptor protein 1," is not appropriate. We therefore propose that the gene name be changed to FBXO50.

Kinases play key roles in cell signaling and represent major targets for drug development, but the regulation of their activation and their associations with health and disease have not been systematically analyzed. Here, we carried out a bioinformatic analysis of the expression levels of 459 human kinase genes in 5681 samples consisting of 44 healthy and 55 malignant human tissues. Defining the tissues where the kinase genes were transcriptionally active led to a functional genomic taxonomy of the kinome and a classification of human tissues and disease types based on the similarity of their kinome gene expression. The co-expression network around each of the kinase genes was defined in order to determine the functional context, i.e. the biological processes that were active in the cells and tissues where the kinase gene was expressed. Strong associations for individual kinases were found for mitosis (69 genes, including AURKA and BUB1), cell cycle control (73 genes, including PLK1 and AURKB), DNA repair (49 genes, including CHEK1 and ATR), immune response (72 genes, including MATK), neuronal (131 genes, including PRKCE) and muscular (72 genes, including MYLK2) functions. We then analyzed which kinase genes gain or lose transcriptional activity in the development of prostate and lung cancers and elucidated the functional associations of individual cancer associated kinase genes. In summary, we report here a systematic classification of kinases based on the bioinformatic analysis of their expression in human tissues and diseases, as well as grouping of tissues and tumor types according to the similarity of their kinome transcription.