State of the art in silico tools for the study of signaling pathways in cancer

In the last several years, researchers have exhibited an intense interest in the evolutionarily conserved signaling pathways that have crucial roles during embryonic development. Interestingly, the malfunctioning of these signaling pathways leads to several human diseases, including cancer. The chemical and biophysical events that occur during cellular signaling, as well as the number of interactions within a signaling pathway, make these systems complex to study. In silico resources are tools used to aid the understanding of cellular signaling pathways. Systems approaches have provided a deeper knowledge of diverse biochemical processes, including individual metabolic pathways, signaling networks and genome-scale metabolic networks. In the future, these tools will be enormously valuable, if they continue to be developed in parallel with growing biological knowledge. In this study, an overview of the bioinformatics resources that are currently available for the analysis of biological networks is provided

Relevant networks involving the p53 signalling pathway in renal cell carcinoma

[Abstract] Introduction: Renal cell carcinoma is the most common type of kidney cancer. A better understanding of the critical pathways and interactions associated with alterations in renal function and renal tumour properties is required. Our final goal is to combine the knowledge provided by a regulatory network with experimental observations provided by the dataset. Methods: In this study, a systems biology approach was used, integrating immunohistochemistry protein expression profiles and protein interaction information with the STRING and MeV bioinformatics tools. A group consisting of 80 patients with renal cell carcinoma was studied. The expression of selected markers was assessed using tissue microarray technology on immunohistochemically stained slides. The immunohistochemical data of the molecular factors studied were analysed using a parametric statistical test, Pearson’s correlation coefficient test. Results: Bioinformatics analysis of tumour samples resulted in 2 protein networks. The first network consists of proteins involved in the angiogenesis pathway and the apoptosis suppressor, BCL2, and includes both positive and negative correlations. The second network shows a negative interaction between the p53 tumour suppressor protein and the glucose transporter type 4. Conclusion: The comprehensive pathway network will help us to realise the cooperative behaviours among pathways. Regulation of metabolic pathways is an important role of p53. The pathway involving the tumour suppressor gene p53 could regulate tumour angiogenesis. Further investigation of the proteins that interact with this pathway in this type of tumour may provide new strategies for cancer therapies to specifically inhibit the molecules that play crucial roles in tumour progression

MicroARN circulantes en sangre de pacientes con cáncer de próstata

Artículo original[Resumen] Introducción. Los microARN (miARN) son ARN reguladores de pequeño tamaño que no codifican para proteínas. La detección de células tumorales circulantes (CTC) proporcionaría información diagnóstica y pronóstica en los tumores de próstata (TP). En este sentido los miARN podrían constituir una nueva y prometedora clase de biomarcadores para la detección de CTC. Objetivos. Analizar miARN circulantes en sangre total como marcadores no invasivos en pacientes con cáncer de próstata localizado e individuos sanos. Material y métodos. Estudio preliminar con una N poblacional de 40 pacientes con una media de edad de 71 años y un PSA medio de 18, 9 ng/ml (rango). Respecto al grupo de riesgo (GR): un 33,3% bajo riesgo, un 30% riesgo intermedio y un 36,7% alto riesgo. Se realizó un estudio previo in silico que identificó 92 miARN candidatos seguido de otro in vivo para verificar los hallazgos del primero mediante tecnología de arrays de PCR a tiempo real. Resultados. El análisis estadístico de los resultados reveló 10 miARN candidatos con una expresión diferencial estadísticamente significativa entre los distintos grupos de riesgo y los controles sanos: hsa-miR-337-3p, hsa-miR-330-3p, hsa-miR-339-3p, hsa-miR-124, hsa-miR-218, hsa-miR-128, hsa-miR-10a, hsa-miR-199b-5p, hsa-miR-200b y hsa-miR-15b Conclusiones. Nuestros datos sugieren que los miARN circulantes pueden servir como biomarcadores para identificar grupos de riesgo en CaP.[Abstract] Introduction. MicroRNAs (miRNAs) are small regulatory RNAs that do not code for proteins. Detection of circulating tumor cells (CTC) would provide diagnostic and prognostic information in prostate tumors (PT). Thus, miRNAs could constitute a promising new class of biomarkers for CTC detection. Objectives. To analyze circulating microRNAs in whole blood as non-invasive markers in patients with localized prostate cancer and healthy individuals. Material and methods. A preliminary study including a population of 40 patients with mean age of 71 years and mean PSA of 18, 9ng/ml (range). Regarding the risk group (RG): 33.3% had low risk, 30% intermediate risk and 36.7% high risk. A previous in silico study identified 92 candidates and was followed by another in vivo to verify the findings of the former using array technology by real-time PCR. Results. Statistical analysis of the results revealed 10 microRNAs candidates with statistically significant differential expression between the different risk groups and healthy controls: hsa-miR-337-3p, hsa-miR-330-3p, hsa-miR-339-3p, hsa-miR-124, hsa-miR-218, hsa-miR-128, hsa-miR-10a, hsa-miR-199b-5p, hsa-miR-200b and hsa-miR-15b. Conclusions. Our data suggest that circulating microRNAs can act as biomarkers to identify risk groups in CaP