IEEE J Biomed Health Inform - Using evolutional properties of gene networks in understanding survival prognosis of glioblastoma.

Tópicos

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Resumo

Previously, we investigated survival prognosis of glioblastoma by applying a gene regulatory approach to a human glioblastoma dataset. Here, we further extend our understanding of survival prognosis of glioblastoma by refining the network inference technique we apply to the glioblastoma dataset with the intent of uncovering further topological properties of the networks. For this study, we modify the approach by specifically looking at both positive and negative correlations separately, as opposed to absolute correlations. There is great interest in applying mathematical modeling approaches to cancer cell line datasets to generate network models of gene regulatory interactions. Analysis of these networks using graph theory metrics can identify genes of interest. The principal approach for modeling microarray datasets has been to group all the cell lines together into one overall network, and then, analyze this network as a whole. As per the previous study, we categorize a human glioblastoma cell line dataset into five categories based on survival data, and analyze each category separately using both negative and positive correlation networks constructed using a modified version of the WGCNA algorithm. Using this approach, we identified a number of genes as being important across different survival stages of the glioblastoma cell lines.

Resumo Limpo

previous investig surviv prognosi glioblastoma appli gene regulatori approach human glioblastoma dataset extend understand surviv prognosi glioblastoma refin network infer techniqu appli glioblastoma dataset intent uncov topolog properti network studi modifi approach specif look posit negat correl separ oppos absolut correl great interest appli mathemat model approach cancer cell line dataset generat network model gene regulatori interact analysi network use graph theori metric can identifi gene interest princip approach model microarray dataset group cell line togeth one overal network analyz network whole per previous studi categor human glioblastoma cell line dataset five categori base surviv data analyz categori separ use negat posit correl network construct use modifi version wgcna algorithm use approach identifi number gene import across differ surviv stage glioblastoma cell line

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