Análise transcriptômica em câncer do colo do útero - Mine, Karina Lumi [UNIFESP] (2009)
Cervical cancer is the second most frequent cancer in women worldwide. Approximately 80% of cases occur in developing countries and the majority of cases are diagnosed in advanced stages. The goals of this study were: (i) to identify a gene expression profile to predict the treatment response, since about 35% of patients with locally advanced cervical cancer will not respond to treatment; (ii) to identify genes and gene groups, pathways and gene networks, with different expression between cervical tumor and normal adjacent tissue, in order to identify genes related to cervical cancer pathogenesis and to better understand the molecular mechanisms involved in this cancer. Methods: To perform the genome-wide gene expression analysis we used microarrays containing oligonucleotides corresponding to ~14,000 genes. We used 23 slides hybridized with tumor samples for the treatment response analysis and for the comparison analysis between tumor and normal tissues we used 34 slides with tumor samples and 20 with normal samples. For data analysis we used the following programs: BRB Array Tools to search the molecular predictor and to obtain gene list; DAVID, Babelomics e Ingenuity Pathway Analysis to identify the over-represented pathways in the gene list; MILANO to verify how our findings relate to the published literature; DAVID, CFinder e Cytoscape for gene networks analysis. Results: (i) we did not identify a gene expression profile that could predict the treatment response; (ii) we found 810 differentially expressed genes between tumor and normal tissues, 341 were up-regulated and 469 were down-regulated in tumor samples. We identify 13 overrepresented pathways and among them, we found previously known (e.g. ‘Cell Cycle’ and ‘p53 signaling’) and unknown molecular pathways (‘Oxidative Phosphorylation’ and ‘Ribosome’) in cervical cancer. Several pathway genes, even in the previously known pathways related to cervical cancer, have not been studied in this cancer. In the gene network analysis, we found 23 subnetworks. Among them we highlighted the subnetwork that contains genes from the kallikrein family. Our results also suggest that our gene profile can also be applied for cervical cancer cases from the other studies and for esophagus cancer. Conclusions: Using genome-wide transcriptome analysis we identified genes and gene groups, pathways and gene networks, involved in cervical cancer. These results might help to understand some molecular mechanisms involved in the cervical cancer pathogenesis and the genes might be interesting candidates to further susceptibility and biomarker studies in cervical cancer.