Guelaguetza Vázquez-Ortiz

Microarray comparative genomic hybridization detection of chromosomal imbalances in uterine cervix carcinoma

BackgroundChromosomal Comparative Genomic Hybridization (CGH) has been applied to all stages of cervical carcinoma progression, defining a specific pattern of chromosomal imbalances in this tumor. However, given its limited spatial resolution, chromosomal CGH has offered only general information regarding the possible genetic targets of DNA copy number changes.MethodsIn order to further define specific DNA copy number changes in cervical cancer, we analyzed 20 cervical samples (3 pre-malignant lesions, 10 invasive tumors, and 7 cell lines), using the GenoSensor microarray CGH system to define particular genetic targets that suffer copy number changes.ResultsThe most common DNA gains detected by array CGH in the invasive samples were located at the RBP1-RBP2 (3q21-q22) genes, the sub-telomeric clone C84C11/T3 (5ptel), D5S23 (5p15.2) and the DAB2 gene (5p13) in 58.8% of the samples. The most common losses were found at the FHIT gene (3p14.2) in 47% of the samples, followed by deletions at D8S504 (8p23.3), CTDP1-SHGC- 145820 (18qtel), KIT (4q11-q12), D1S427-FAF1 (1p32.3), D9S325 (9qtel), EIF4E (eukaryotic translation initiation factor 4E, 4q24), RB1 (13q14), and DXS7132 (Xq12) present in 5/17 (29.4%) of the samples.ConclusionOur results confirm the presence of a specific pattern of chromosomal imbalances in cervical carcinoma and define specific targets that are suffering DNA copy number changes in this neoplasm.

Genome wide expression analysis in HPV16 Cervical Cancer: identification of altered metabolic pathways

BackgroundCervical carcinoma (CC) is a leading cause of death among women worldwide. Human papilloma virus (HPV) is a major etiological factor in CC and HPV 16 is the more frequent viral type present. Our aim was to characterize metabolic pathways altered in HPV 16 tumor samples by means of transcriptome wide analysis and bioinformatics tools for visualizing expression data in the context of KEGG biological pathways.ResultsWe found 2,067 genes significantly up or down-modulated (at least 2-fold) in tumor clinical samples compared to normal tissues, representing ~3.7% of analyzed genes. Cervical carcinoma was associated with an important up-regulation of Wnt signaling pathway, which was validated by in situ hybridization in clinical samples. Other up-regulated pathways were those of calcium signaling and MAPK signaling, as well as cell cycle-related genes. There was down-regulation of focal adhesion, TGF-β signaling, among other metabolic pathways.ConclusionThis analysis of HPV 16 tumors transcriptome could be useful for the identification of genes and molecular pathways involved in the pathogenesis of cervical carcinoma. Understanding the possible role of these proteins in the pathogenesis of CC deserves further studies.

Characterization of the global profile of genes expressed in cervical epithelium by Serial Analysis of Gene Expression (SAGE)

BackgroundSerial Analysis of Gene Expression (SAGE) is a new technique that allows a detailed and profound quantitative and qualitative knowledge of gene expression profile, without previous knowledge of sequence of analyzed genes. We carried out a modification of SAGE methodology (microSAGE), useful for the analysis of limited quantities of tissue samples, on normal human cervical tissue obtained from a donor without histopathological lesions. Cervical epithelium is constituted mainly by cervical keratinocytes which are the targets of human papilloma virus (HPV), where persistent HPV infection of cervical epithelium is associated with an increase risk for developing cervical carcinomas (CC).ResultsWe report here a transcriptome analysis of cervical tissue by SAGE, derived from 30,418 sequenced tags that provide a wealth of information about the gene products involved in normal cervical epithelium physiology, as well as genes not previously found in uterine cervix tissue involved in the process of epidermal differentiation.ConclusionThis first comprehensive and profound analysis of uterine cervix transcriptome, should be useful for the identification of genes involved in normal cervix uterine function, and candidate genes associated with cervical carcinoma.

Differentially expressed genes between high-risk human papillomavirus types in human cervical cancer cells.

Cervical carcinoma (CC) is one of the most common cancers among women worldwide and the first cause of death among the Mexican female population. Human papillomavirus (HPV) infection is the most important etiologic factor for CC. Of the oncogenic types, HPV16 and HPV18 are found in 60–70% of invasive CCs worldwide. HPV18 appears to be associated with a more aggressive form of cervical neoplasia than HPV16 infection. At present, there are no studies on differentially expressed cellular genes between transformed cells harboring HPV16 and HPV18 sequences. Based on previous complementaryDNA microarray data from our group, 13 genes were found to be differentially overexpressed between HPV16- and HPV18-transformed cells. These genes were as follows: E6BP, UBE4A, C20orf14, ATF7, ABCC8, SLC6A12, WASF3, SUV39H1, SPAG8, CCNC, E2FFE, BIRC5, and DEDD. Differential expression of six selected genes was confirmed by real-time reverse transcription–polymerase chain reaction (RT-PCR). All real-time RT-PCRs confirmed differential expression between HPV18 and HPV− samples. The present work identifies genes from signaling pathways triggered by HPV transformation that could be differentially deregulated between HPV16+ and HPV18+ samples.

An oligoarray for the detection of human papillomavirus type 16 variants

On the basis of human papillomavirus (HPV) E6 gene mutations, there are more than five variants of HPV 16. We applied a sensitive and specific stacking hybridization assay using an oligoarray for the detection of Asian–American (AA) and European (E) (E350G) HPV 16 variants. A simple glass slide was coated with capture probes consisting of short oligonucleotide DNA sequences (7–9 mers) specific for AA and E variants. Two different regions of the E6 HPV 16 gene were amplified with a set of two primers, which were used as target DNA. These targets were preannealed with auxiliary labeled oligonucleotides and hybridized to the oligoarray in the presence of specific and complementary capture probes. Our designed array based on shorter capture probes successfully discriminated between HPV 16 AA and E variants. The present DNA oligoarray system could be useful as a reliable technique for HPV 16 detection and does not require specialized equipment; nevertheless, further intra- and interlaboratory studies are needed.

Toward a Non-Empirical Treatment for Rheumatoid Arthritis Based on Its Molecular Pathogenesis

Rheumatoid arthritis (RA) is a chronic, disabling disease that affects individuals during the productive years of their lives. Modern treatment for RA includes the so-called “biologic” therapy, which is based on recombinant proteins that modify the biologic processes. These agents have potent therapeutic effects and different mechanisms of action. Nevertheless, therapeutic failure still prevails. Treatment that prevents disability in RA must be started in an early manner, before the development of complications and, ideally, with a minimum possibility of therapeutic failure. As yet, there are no clinical or laboratory criteria to identify those patients with a higher probability of responding to particular types of therapy, delaying control of RA ad affecting the prevention of incapacity. Research into gene diversity through single-nucleotide polymorphisms (SNPs) by means of microarray systems, allows the detailed analysis of gene factors associated to a given disease. SNPs have been recently applied to the study of RA, where the major polymorphisms associated to RA occur primarily in genes that code for proteins related to the initiation of an immune response and/or the control of cellular activity in the immune system, in addition to genes related to tissue repair. The specific meaning of these findings is in its initial stages of research. On the other hand, proteomics relate to the analysis of protein expression profiles at multiple levels. Both types of studies will contribute to the knowledge of patterns of gene expression in RA compared to the general population, and will allow an understanding of the pathogenesis of RA. Moreover, proteomic and genomic profiles can be employed to designs probes that identify individuals with the risk of developing RA, individually predict the response to different therapeutic modalities (pharmacogenomics) and for the follow-up of the biologic response to therapy.