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1.  Combined analysis of genome-wide expression and copy number profiles to identify key altered genomic regions in cancer 
BMC Genomics  2012;13(Suppl 5):S5.
Analysis of DNA copy number alterations and gene expression changes in human samples have been used to find potential target genes in complex diseases. Recent studies have combined these two types of data using different strategies, but focusing on finding gene-based relationships. However, it has been proposed that these data can be used to identify key genomic regions, which may enclose causal genes under the assumption that disease-associated gene expression changes are caused by genomic alterations.
Following this proposal, we undertake a new integrative analysis of genome-wide expression and copy number datasets. The analysis is based on the combined location of both types of signals along the genome. Our approach takes into account the genomic location in the copy number (CN) analysis and also in the gene expression (GE) analysis. To achieve this we apply a segmentation algorithm to both types of data using paired samples. Then, we perform a correlation analysis and a frequency analysis of the gene loci in the segmented CN regions and the segmented GE regions; selecting in both cases the statistically significant loci. In this way, we find CN alterations that show strong correspondence with GE changes. We applied our method to a human dataset of 64 Glioblastoma Multiforme samples finding key loci and hotspots that correspond to major alterations previously described for this type of tumors.
Identification of key altered genomic loci constitutes a first step to find the genes that drive the alteration in a malignant state. These driver genes can be found in regions that show high correlation in copy number alterations and expression changes.
PMCID: PMC3476997  PMID: 23095915
2.  Prognostic Impact of del(17p) and del(22q) as Assessed by Interphase FISH in Sporadic Colorectal Carcinomas 
PLoS ONE  2012;7(8):e42683.
Most sporadic colorectal cancer (sCRC) deaths are caused by metastatic dissemination of the primary tumor. New advances in genetic profiling of sCRC suggest that the primary tumor may contain a cell population with metastatic potential. Here we compare the cytogenetic profile of primary tumors from liver metastatic versus non-metastatic sCRC.
Methodology/Principal Findings
We prospectively analyzed the frequency of numerical/structural abnormalities of chromosomes 1, 7, 8, 13, 14, 17, 18, 20, and 22 by iFISH in 58 sCRC patients: thirty-one non-metastatic (54%) vs. 27 metastatic (46%) disease. From a total of 18 probes, significant differences emerged only for the 17p11.2 and 22q11.2 chromosomal regions. Patients with liver metastatic sCRC showed an increased frequency of del(17p11.2) (10% vs. 67%;p<.001) and del(22q11.2) (0% vs. 22%;p = .02) versusnon-metastatic cases. Multivariate analysis of prognostic factors for overall survival (OS) showed that the only clinical and cytogenetic parameters that had an independent adverse impact on patient outcome were the presence of del(17p) with a 17p11.2 breakpoint and del(22q11.2). Based on these two cytogenetic variables, patients were classified into three groups: low- (no adverse features), intermediate- (one adverse feature) and high-risk (two adverse features)- with significantly different OS rates at 5-years (p<.001): 92%, 53% and 0%, respectively.
Our results unravel the potential implication of del(17p11.2) in sCRC patients with liver metastasis as this cytogenetic alteration appears to be intrinsically related to an increased metastatic potential and a poor outcome, providing additional prognostic information to that associated with other cytogenetic alterations such as del(22q11.2). Additional prospective studies in larger series of patients would be required to confirm the clinical utility of the new prognostic markers identified.
PMCID: PMC3422354  PMID: 22912721
3.  Functional Analysis beyond Enrichment: Non-Redundant Reciprocal Linkage of Genes and Biological Terms 
PLoS ONE  2011;6(9):e24289.
Functional analysis of large sets of genes and proteins is becoming more and more necessary with the increase of experimental biomolecular data at omic-scale. Enrichment analysis is by far the most popular available methodology to derive functional implications of sets of cooperating genes. The problem with these techniques relies in the redundancy of resulting information, that in most cases generate lots of trivial results with high risk to mask the reality of key biological events. We present and describe a computational method, called GeneTerm Linker, that filters and links enriched output data identifying sets of associated genes and terms, producing metagroups of coherent biological significance. The method uses fuzzy reciprocal linkage between genes and terms to unravel their functional convergence and associations. The algorithm is tested with a small set of well known interacting proteins from yeast and with a large collection of reference sets from three heterogeneous resources: multiprotein complexes (CORUM), cellular pathways (SGD) and human diseases (OMIM). Statistical Precision, Recall and balanced F-score are calculated showing robust results, even when different levels of random noise are included in the test sets. Although we could not find an equivalent method, we present a comparative analysis with a widely used method that combines enrichment and functional annotation clustering. A web application to use the method here proposed is provided at
PMCID: PMC3174934  PMID: 21949701
4.  Mapping of Genetic Abnormalities of Primary Tumours from Metastatic CRC by High-Resolution SNP Arrays 
PLoS ONE  2010;5(10):e13752.
For years, the genetics of metastatic colorectal cancer (CRC) have been studied using a variety of techniques. However, most of the approaches employed so far have a relatively limited resolution which hampers detailed characterization of the common recurrent chromosomal breakpoints as well as the identification of small regions carrying genetic changes and the genes involved in them.
Methodology/Principal Findings
Here we applied 500K SNP arrays to map the most common chromosomal lesions present at diagnosis in a series of 23 primary tumours from sporadic CRC patients who had developed liver metastasis. Overall our results confirm that the genetic profile of metastatic CRC is defined by imbalanced gains of chromosomes 7, 8q, 11q, 13q, 20q and X together with losses of the 1p, 8p, 17p and 18q chromosome regions. In addition, SNP-array studies allowed the identification of small (<1.3 Mb) and extensive/large (>1.5 Mb) altered DNA sequences, many of which contain cancer genes known to be involved in CRC and the metastatic process. Detailed characterization of the breakpoint regions for the altered chromosomes showed four recurrent breakpoints at chromosomes 1p12, 8p12, 17p11.2 and 20p12.1; interestingly, the most frequently observed recurrent chromosomal breakpoint was localized at 17p11.2 and systematically targeted the FAM27L gene, whose role in CRC deserves further investigations.
In summary, in the present study we provide a detailed map of the genetic abnormalities of primary tumours from metastatic CRC patients, which confirm and extend on previous observations as regards the identification of genes potentially involved in development of CRC and the metastatic process.
PMCID: PMC2966422  PMID: 21060790
6.  GATExplorer: Genomic and Transcriptomic Explorer; mapping expression probes to gene loci, transcripts, exons and ncRNAs 
BMC Bioinformatics  2010;11:221.
Genome-wide expression studies have developed exponentially in recent years as a result of extensive use of microarray technology. However, expression signals are typically calculated using the assignment of "probesets" to genes, without addressing the problem of "gene" definition or proper consideration of the location of the measuring probes in the context of the currently known genomes and transcriptomes. Moreover, as our knowledge of metazoan genomes improves, the number of both protein-coding and noncoding genes, as well as their associated isoforms, continues to increase. Consequently, there is a need for new databases that combine genomic and transcriptomic information and provide updated mapping of expression probes to current genomic annotations.
GATExplorer (Genomic and Transcriptomic Explorer) is a database and web platform that integrates a gene loci browser with nucleotide level mappings of oligo probes from expression microarrays. It allows interactive exploration of gene loci, transcripts and exons of human, mouse and rat genomes, and shows the specific location of all mappable Affymetrix microarray probes and their respective expression levels in a broad set of biological samples. The web site allows visualization of probes in their genomic context together with any associated protein-coding or noncoding transcripts. In the case of all-exon arrays, this provides a means by which the expression of the individual exons within a gene can be compared, thereby facilitating the identification and analysis of alternatively spliced exons. The application integrates data from four major source databases: Ensembl, RNAdb, Affymetrix and GeneAtlas; and it provides the users with a series of files and packages (R CDFs) to analyze particular query expression datasets. The maps cover both the widely used Affymetrix GeneChip microarrays based on 3' expression (e.g. human HG U133 series) and the all-exon expression microarrays (Gene 1.0 and Exon 1.0).
GATExplorer is an integrated database that combines genomic/transcriptomic visualization with nucleotide-level probe mapping. By considering expression at the nucleotide level rather than the gene level, it shows that the arrays detect expression signals from entities that most researchers do not contemplate or discriminate. This approach provides the means to undertake a higher resolution analysis of microarray data and potentially extract considerably more detailed and biologically accurate information from existing and future microarray experiments.
PMCID: PMC2875241  PMID: 20429936
7.  Human Gene Coexpression Landscape: Confident Network Derived from Tissue Transcriptomic Profiles 
PLoS ONE  2008;3(12):e3911.
Analysis of gene expression data using genome-wide microarrays is a technique often used in genomic studies to find coexpression patterns and locate groups of co-transcribed genes. However, most studies done at global “omic” scale are not focused on human samples and when they correspond to human very often include heterogeneous datasets, mixing normal with disease-altered samples. Moreover, the technical noise present in genome-wide expression microarrays is another well reported problem that many times is not addressed with robust statistical methods, and the estimation of errors in the data is not provided.
Methodology/Principal Findings
Human genome-wide expression data from a controlled set of normal-healthy tissues is used to build a confident human gene coexpression network avoiding both pathological and technical noise. To achieve this we describe a new method that combines several statistical and computational strategies: robust normalization and expression signal calculation; correlation coefficients obtained by parametric and non-parametric methods; random cross-validations; and estimation of the statistical accuracy and coverage of the data. All these methods provide a series of coexpression datasets where the level of error is measured and can be tuned. To define the errors, the rates of true positives are calculated by assignment to biological pathways. The results provide a confident human gene coexpression network that includes 3327 gene-nodes and 15841 coexpression-links and a comparative analysis shows good improvement over previously published datasets. Further functional analysis of a subset core network, validated by two independent methods, shows coherent biological modules that share common transcription factors. The network reveals a map of coexpression clusters organized in well defined functional constellations. Two major regions in this network correspond to genes involved in nuclear and mitochondrial metabolism and investigations on their functional assignment indicate that more than 60% are house-keeping and essential genes. The network displays new non-described gene associations and it allows the placement in a functional context of some unknown non-assigned genes based on their interactions with known gene families.
The identification of stable and reliable human gene to gene coexpression networks is essential to unravel the interactions and functional correlations between human genes at an omic scale. This work contributes to this aim, and we are making available for the scientific community the validated human gene coexpression networks obtained, to allow further analyses on the network or on some specific gene associations.
The data are available free online at
PMCID: PMC2597745  PMID: 19081792

Results 1-7 (7)