Identification of genes correlated with early-stage bladder cancer progression.

Transitional cell carcinoma (TCC) of the bladder ranks fourth in incidence of all cancers in the developed world, yet the mechanisms of its origin and progression remain poorly understood. There are also few useful diagnostic or prognostic biomarkers for this disease. We have combined a transgenic mouse model for invasive bladder cancer (UPII-SV40Tag mice) with DNA microarray technology to determine molecular mechanisms involved in early TCC development and to identify new biomarkers for detection, diagnosis, and prognosis of TCC. We have identified genes that are differentially expressed between the bladders of UPII-SV40Tag mice and their age-matched wild-type littermates at 3, 6, 20, and 30 weeks of age. These are ages that correspond to premalignant, carcinoma in situ, and early-stage and later stage invasive TCC, respectively. Our preliminary analysis of the microarray data sets has revealed approximately 1,900 unique genes differentially expressed (> or =3-fold difference at one or more time points) between wild-type and UPII-SV40Tag urothelium during the time course of tumor development. Among these, there were a high proportion of cell cycle regulatory genes and a proliferation signaling genes that are more strongly expressed in the UPII-SV40Tag bladder urothelium. We show that several of the genes upregulated in UPII-SV40Tag urothelium, including RacGAP1, PCNA, and Hmmr, are expressed at high levels in superficial bladder TCC patient samples. These findings provide insight into the earliest events in the development of bladder TCC as well as identify several promising early-stage biomarkers.

Integrating diverse information to gain more insight into microarray analysis.

Microarray technology provides an opportunity to view transcriptions at genomic level under different conditions controlled by an experiment. From an array experiment using a human cancer cell line that is engineered to differ in expression of tumor antigen, integrin alpha6beta4, few hundreds of differentially expressed genes are selected and are clustered using one of several standard algorithms. The set of genes in a cluster is expected to have similar expression patterns and are most likely to be coregulated and thereby expected to have similar function. The highly expressed set of upregulated genes become candidates for further evaluation as potential biomarkers. Besides these benefits, microarray experiment by itself does not help us to understand or discover potential pathways or to identify important set of genes for potential drug targets. In this paper we discuss about integrating protein-to-protein interaction information, pathway information with array expression data set to identify a set of "important" genes, and potential signal transduction networks that help to target and reverse the oncogenic phenotype induced by tumor antigen such as integrin alpha6beta4. We will illustrate the proposed method with our recent microarray experiment conducted for identifying transcriptional targets of integrin alpha6beta4 for cancer progression.

Beyond clustering of array expressions.

Microarray technology provides an opportunity to view transcriptions at genomic level under different experimental conditions. Generally, co-expressed genes, which are members of the same cluster, are expected to have similar function, but unfortunately it is not true due to various reasons including co-expression does not necessarily imply co-regulation. To improve the results of clustering, we investigate a method based on singular value decomposition (SVD) for integrating diverse data sources. We also introduce a new cluster evaluation method based on mutual information. Using time series data sets on yeast, we have empirically demonstrated the effectiveness of SVD as a data integrator.

On predicting secondary structure transition.

A function of a protein is dependent on its structure; therefore, predicting a protein structure from an amino acid sequence is an active area of research. To improve the accuracy of validation of structures, we are studying the predictability of secondary structure transitions using the following machine learning algorithms: naive Bayes, C4.5 decision tree, and random forest. The annotated data sets from PDB that have agreement with DSSP and STRIDE are used for training and testing. We have demonstrated that predicting structure transition with high degree of certainty is possible and we were able to get as high as 97.5% of prediction accuracy.

Towards validating the hypothesis of phylogenetic profiling.

BACKGROUND: As the number of fully sequenced genome increases, the need is greater for bioinformatics to predict or annotate genes of a newly sequenced genome. Ever since Eisenberg and his colleagues introduced phylogenetic profiling for assigning or predicting protein functions using comparative genomic analysis, the approach has been used in predicting function of some prokaryotic genomes quite successfully. Very little work has been reported in functional prediction of eukaryotes such as mouse and Homo sapiens species from phylogenetic profiles. RESULTS: We have proposed a general methodology for validating the hypothesis underlying phylogenetic profiling techniques, and have demonstrated it using eukaryotic target genomes such as Homo sapiens and mouse. The gene ontology is used as the gold standard for validating functional similarity among the genes in each cluster. We compute the functional cohesiveness of each cluster and the results appeared to be not encouraging towards finding functionally cohesive phylogenetic profiles. This result complements one recent work on the poor performance on functional linkage in some eukaryotic genome using phylogenetic profiling techniques. If we introduce a broad interpretation for functionally related genes as functional sub-clustering within a phylogenetic profile, then we have a very strong support for the hypothesis as we have shown in the paper.

Metric for measuring the effectiveness of clustering of DNA microarray expression.

BACKGROUND: The recent advancement of microarray technology with lower noise and better affordability makes it possible to determine expression of several thousand genes simultaneously. The differentially expressed genes are filtered first and then clustered based on the expression profiles of the genes. A large number of clustering algorithms and distance measuring matrices are proposed in the literature. The popular ones among them include hierarchal clustering and k-means clustering. These algorithms have often used the Euclidian distance or Pearson correlation distance. The biologists or the practitioners are often confused as to which algorithm to use since there is no clear winner among algorithms or among distance measuring metrics. Several validation indices have been proposed in the literature and these are based directly or indirectly on distances; hence a method that uses any of these indices does not relate to any biological features such as biological processes or molecular functions. RESULTS: In this paper we have proposed a metric to measure the effectiveness of clustering algorithms of genes by computing inter-cluster cohesiveness and as well as the intra-cluster separation with respect to biological features such as biological processes or molecular functions. We have applied this metric to the clusters on the data set that we have created as part of a larger study to determine the cancer suppressive mechanism of a class of chemicals called retinoids.We have considered hierarchal and k-means clustering with Euclidian and Pearson correlation distances. Our results show that genes of similar expression profiles are more likely to be closely related to biological processes than they are to molecular functions. The findings have been supported by many works in the area of gene clustering. CONCLUSION: The best clustering algorithm of genes must achieve cohesiveness within a cluster with respect to some biological features, and as well as maximum separation between clusters in terms of the distribution of genes of a behavioral group across clusters. We claim that our proposed metric is novel in this respect and that it provides a measure of both inter and intra cluster cohesiveness. Best of all, computation of the proposed metric is easy and it provides a single quantitative value, which makes comparison of different algorithms easier. The maximum cluster cohesiveness and the maximum intra-cluster separation are indicated by the metric when its value is 0.We have demonstrated the metric by applying it to a data set with gene behavioral groupings such as biological process and molecular functions. The metric can be easily extended to other features of a gene such as DNA binding sites and protein-protein interactions of the gene product, special features of the intron-exon structure, promoter characteristics, etc. The metric can also be used in other domains that use two different parametric spaces; one for clustering and the other one for measuring the effectiveness.

Discriminating TATA box from putative TATA boxes in plant genome.

The TATA box has been used successfully to identify a transcription start site (TSS) and thereby a promoter. Unfortunately, there are many substrings which fit the profile of a TATA box and such substrings are called putative TATA boxes. We have applied linear and non linear classifiers for discriminating TATA box from putative TATA boxes and have compared their performances. We have also investigated the influence of the length of the pair of sequences flanking a putative TATA box on the prediction accuracy. The techniques we have presented in this paper are general enough to be applicable to other domains or to other genomes.