Perfil de expresión génica en el cáncer de próstata: identificación de marcadores candidatos para el diagnóstico no invasivo / Gene expression profiles in prostate cancer: Identification of candidate non-invasive diagnostic markers

Objetivo: Analizar los perfiles de expresión génica del cáncer de próstata (CaP) e identificar los genes diferencialmente expresados. Determinar si la expresión diferencial en tejido se mantiene en muestras de orina-posmasaje prostático (PMP). Material y métodos: Un total de 46 muestras de tejido prostático (36 de pacientes con CaP y 10 controles) y 158 orinas-PMP (113 de pacientes con CaP y 45 controles) se recogieron entre diciembre de 2003 y mayo de 2007. Se utilizaron microarrays de ADN para identificar los genes diferencialmente expresados entre las muestras de tejido tumorales y las controles. Diez genes fueron seleccionados para la validación técnica de los microarrays en las mismas muestras tisulares mediante PCR cuantitativa (RT-qPCR). Se seleccionaron 42 genes para ser validados en muestras de orina-PMP mediante RT-qPCR. Resultados: El gráfico de escalado multidimensional mostró una clara separación entre las muestras de tejido tumorales y las controles. Se han identificado 1.047 genes diferencialmente expresados (FDR ≤ 0,1) entre los 2 grupos. La correlación entre los datos de microarrays y RT-qPCR fue alta (r = 0,928, p < 0,001). Trece genes mantuvieron el mismo sentido de expresión diferencial al ser analizados en orinas-PMP y 4 de ellos (HOXC6, PCA3, PDK4 y TMPRSS2-ERG) mostraron diferencias de expresión estadísticamente significativas entre orinas-PMP tumorales y controles (p < 0,05). Conclusión: Existe un perfil de expresión génica diferencial en el CaP. Aunque la extrapolación de la expresión génica obtenida en tejido prostático a orina-PMP se debe realizar con precaución, el análisis del tejido prostático permite la identificación de nuevos biomarcadores para diagnóstico no invasivo del CaP

Objective: To analyze gene expression profiles of prostate cancer (PCa) with the aim of determining the relevant differentially expressed genes and subsequently ascertain whether this differential expression is maintained in post-prostatic massage (PPM) urine samples. Material and methods: Forty-six tissue specimens (36 from PCa patients and 10 controls) and158 urine PPM-urines (113 from PCa patients and 45 controls) were collected between December 2003 and May 2007. DNA microarrays were used to identify genes differentially expressed between tumour and control samples. Ten genes were technically validated in the same tissue samples by quantitative RT-PCR (RT-qPCR). Forty two selected differentially expressed genes were validated in an independent set of PPM-urines by qRT-PCR. Results: Multidimensional scaling plot according to the expression of all the microarray genes showed a clear distinction between control and tumour samples. A total of 1047 differentially expressed genes (FDR≤0.1) were indentified between both groups of samples. We found a high correlation in the comparison of microarray and RT-qPCR gene expression levels (r = 0.928,P < 0.001). Thirteen genes maintained the same fold change direction when analyzed in PPM urine samples and in four of them (HOXC6, PCA3, PDK4 and TMPRSS2-ERG), these differences were statistically significant (P < 0.05). Conclusion: The analysis of PCa by DNA microarrays provides new putative mRNA markers for PCa diagnosis that, with caution, can be extrapolated to PPM-urines

Gene expression profiles in prostate cancer: identification of candidate non-invasive diagnostic markers.

OBJECTIVE: To analyze gene expression profiles of prostate cancer (PCa) with the aim of determining the relevant differentially expressed genes and subsequently ascertain whether this differential expression is maintained in post-prostatic massage (PPM) urine samples. MATERIAL AND METHODS: Forty-six tissue specimens (36 from PCa patients and 10 controls) and 158 urine PPM-urines (113 from PCa patients and 45 controls) were collected between December 2003 and May 2007. DNA microarrays were used to identify genes differentially expressed between tumour and control samples. Ten genes were technically validated in the same tissue samples by quantitative RT-PCR (RT-qPCR). Forty two selected differentially expressed genes were validated in an independent set of PPM-urines by qRT-PCR. RESULTS: Multidimensional scaling plot according to the expression of all the microarray genes showed a clear distinction between control and tumour samples. A total of 1047 differentially expressed genes (FDR≤.1) were indentified between both groups of samples. We found a high correlation in the comparison of microarray and RT-qPCR gene expression levels (r=.928, P<.001). Thirteen genes maintained the same fold change direction when analyzed in PPM-urine samples and in four of them (HOXC6, PCA3, PDK4 and TMPRSS2-ERG), these differences were statistically significant (P<.05). CONCLUSION: The analysis of PCa by DNA microarrays provides new putative mRNA markers for PCa diagnosis that, with caution, can be extrapolated to PPM-urines.

SpliceDB: database of canonical and non-canonical mammalian splice sites.

A database (SpliceDB) of known mammalian splice site sequences has been developed. We extracted 43 337 splice pairs from mammalian divisions of the gene-centered Infogene database, including sites from incomplete or alternatively spliced genes. Known EST sequences supported 22 815 of them. After discarding sequences with putative errors and ambiguous location of splice junctions the verified dataset includes 22 489 entries. Of these, 98.71% contain canonical GT-AG junctions (22 199 entries) and 0.56% have non-canonical GC-AG splice site pairs. The remainder (0.73%) occurs in a lot of small groups (with a maximum size of 0.05%). We especially studied non-canonical splice sites, which comprise 3.73% of GenBank annotated splice pairs. EST alignments allowed us to verify only the exonic part of splice sites. To check the conservative dinucleotides we compared sequences of human non-canonical splice sites with sequences from the high throughput genome sequencing project (HTG). Out of 171 human non-canonical and EST-supported splice pairs, 156 (91.23%) had a clear match in the human HTG. They can be classified after sequence analysis as: 79 GC-AG pairs (of which one was an error that corrected to GC-AG), 61 errors corrected to GT-AG canonical pairs, six AT-AC pairs (of which two were errors corrected to AT-AC), one case was produced from a non-existent intron, seven cases were found in HTG that were deposited to GenBank and finally there were only two other cases left of supported non-canonical splice pairs. The information about verified splice site sequences for canonical and non-canonical sites is presented in SpliceDB with the supporting evidence. We also built weight matrices for the major splice groups, which can be incorporated into gene prediction programs. SpliceDB is available at the computational genomic Web server of the Sanger Centre: http://genomic.sanger.ac. uk/spldb/SpliceDB.html and at http://www.softberry. com/spldb/SpliceDB.html.

Fusion of the human gene for the polyubiquitination coeffector UEV1 with Kua, a newly identified gene.

UEV proteins are enzymatically inactive variants of the E2 ubiquitin-conjugating enzymes that regulate noncanonical elongation of ubiquitin chains. In Saccharomyces cerevisiae, UEV is part of the RAD6-mediated error-free DNA repair pathway. In mammalian cells, UEV proteins can modulate c-FOS transcription and the G2-M transition of the cell cycle. Here we show that the UEV genes from phylogenetically distant organisms present a remarkable conservation in their exon-intron structure. We also show that the human UEV1 gene is fused with the previously unknown gene Kua. In Caenorhabditis elegans and Drosophila melanogaster, Kua and UEV are in separated loci, and are expressed as independent transcripts and proteins. In humans, Kua and UEV1 are adjacent genes, expressed either as separate transcripts encoding independent Kua and UEV1 proteins, or as a hybrid Kua-UEV transcript, encoding a two-domain protein. Kua proteins represent a novel class of conserved proteins with juxtamembrane histidine-rich motifs. Experiments with epitope-tagged proteins show that UEV1A is a nuclear protein, whereas both Kua and Kua-UEV localize to cytoplasmic structures, indicating that the Kua domain determines the cytoplasmic localization of Kua-UEV. Therefore, the addition of a Kua domain to UEV in the fused Kua-UEV protein confers new biological properties to this regulator of variant polyubiquitination.

Analysis of canonical and non-canonical splice sites in mammalian genomes.

A set of 43 337 splice junction pairs was extracted from mammalian GenBank annotated genes. Expressed sequence tag (EST) sequences support 22 489 of them. Of these, 98.71% contain canonical dinucleotides GT and AG for donor and acceptor sites, respectively; 0.56% hold non-canonical GC-AG splice site pairs; and the remaining 0.73% occurs in a lot of small groups (with a maximum size of 0.05%). Studying these groups we observe that many of them contain splicing dinucleotides shifted from the annotated splice junction by one position. After close examination of such cases we present a new classification consisting of only eight observed types of splice site pairs (out of 256 a priori possible combinations). EST alignments allow us to verify the exonic part of the splice sites, but many non-canonical cases may be due to intron sequencing errors. This idea is given substantial support when we compare the sequences of human genes having non-canonical splice sites deposited in GenBank by high throughput genome sequencing projects (HTG). A high proportion (156 out of 171) of the human non-canonical and EST-supported splice site sequences had a clear match in the human HTG. They can be classified after corrections as: 79 GC-AG pairs (of which one was an error that corrected to GC-AG), 61 errors that were corrected to GT-AG canonical pairs, six AT-AC pairs (of which two were errors that corrected to AT-AC), one case was produced from non-existent intron, seven cases were found in HTG that were deposited to GenBank and finally there were only two cases left of supported non-canonical splice sites. If we assume that approximately the same situation is true for the whole set of annotated mammalian non-canonical splice sites, then the 99.24% of splice site pairs should be GT-AG, 0.69% GC-AG, 0.05% AT-AC and finally only 0.02% could consist of other types of non-canonical splice sites. We analyze several characteristics of EST-verified splice sites and build weight matrices for the major groups, which can be incorporated into gene prediction programs. We also present a set of EST-verified canonical splice sites larger by two orders of magnitude than the current one (22 199 entries versus approximately 600) and finally, a set of 290 EST-supported non-canonical splice sites. Both sets should be significant for future investigations of the splicing mechanism.

An assessment of gene prediction accuracy in large DNA sequences.

One of the first useful products from the human genome will be a set of predicted genes. Besides its intrinsic scientific interest, the accuracy and completeness of this data set is of considerable importance for human health and medicine. Though progress has been made on computational gene identification in terms of both methods and accuracy evaluation measures, most of the sequence sets in which the programs are tested are short genomic sequences, and there is concern that these accuracy measures may not extrapolate well to larger, more challenging data sets. Given the absence of experimentally verified large genomic data sets, we constructed a semiartificial test set comprising a number of short single-gene genomic sequences with randomly generated intergenic regions. This test set, which should still present an easier problem than real human genomic sequence, mimics the approximately 200kb long BACs being sequenced. In our experiments with these longer genomic sequences, the accuracy of GENSCAN, one of the most accurate ab initio gene prediction programs, dropped significantly, although its sensitivity remained high. Conversely, the accuracy of similarity-based programs, such as GENEWISE, PROCRUSTES, and BLASTX was not affected significantly by the presence of random intergenic sequence, but depended on the strength of the similarity to the protein homolog. As expected, the accuracy dropped if the models were built using more distant homologs, and we were able to quantitatively estimate this decline. However, the specificities of these techniques are still rather good even when the similarity is weak, which is a desirable characteristic for driving expensive follow-up experiments. Our experiments suggest that though gene prediction will improve with every new protein that is discovered and through improvements in the current set of tools, we still have a long way to go before we can decipher the precise exonic structure of every gene in the human genome using purely computational methodology.

Evaluation of gene structure prediction programs.

We evaluate a number of computer programs designed to predict the structure of protein coding genes in genomic DNA sequences. Computational gene identification is set to play an increasingly important role in the development of the genome projects, as emphasis turns from mapping to large-scale sequencing. The evaluation presented here serves both to assess the current status of the problem and to identify the most promising approaches to ensure further progress. The programs analyzed were uniformly tested on a large set of vertebrate sequences with simple gene structure, and several measures of predictive accuracy were computed at the nucleotide, exon, and protein product levels. The results indicated that the predictive accuracy of the programs analyzed was lower than originally found. The accuracy was even lower when considering only those sequences that had recently been entered and that did not show any similarity to previously entered sequences. This indicates that the programs are overly dependent on the particularities of the examples they learn from. For most of the programs, accuracy in this test set ranged from 0.60 to 0.70 as measured by the Correlation Coefficient (where 1.0 corresponds to a perfect prediction and 0.0 is the value expected for a random prediction), and the average percentage of exons exactly identified was less than 50%. Only those programs including protein sequence database searches showed substantially greater accuracy. The accuracy of the programs was severely affected by relatively high rates of sequence errors. Since the set on which the programs were tested included only relatively short sequences with simple gene structure, the accuracy of the programs is likely to be even lower when used for large uncharacterized genomic sequences with complex structure. While in such cases, programs currently available may still be of great use in pinpointing the regions likely to contain exons, they are far from being powerful enough to elucidate its genomic structure completely.