Abstract

One of the most aggressive primary brain tumors is glioblastoma multiforme (GBM), which has poor prognosis, and with limited therapeutic options. Identifying reliable molecular markers will be crucial to enhancing the prognostic value for GBM and to elucidating the biological mechanisms of GBM progression. While co-expression network analysis has been used extensively for biomarker discovery, traditional centrality measures in graph theory assign only topological measures to genes, and exclude any biological information based on patient survival. In this study, we suggest a biologically informed node-weighted centrality approach for incorporating survival-related biological information with the conventional network topology to enhance biomarker prioritization. This dataset contains DNA methylation profiles from the The Cancer Genome Atlas (TCGA) GBM cohort which was preprocessed to bring out informative probes followed by univariate Cox proportional hazards analysis to extract the genes associated with survival. To incorporate biological weights that are derived from survival, node-weighted versions of degree, closeness, betweenness and eigenvector centrality were formulated on the biological network. The proposed framework was then used to prioritize genes within the co-expression network that have a biological relevance. A total of 156 genes were identified in the analysis that are associated with survival, and ten candidate prognostic biomarkers (SEMA3B, MARK2, APS, PITPNM2, SFRP1, SLC44A2, PRLH, KRTAP4.2, CTSZ and DIP2C) were selected. These biomarkers were uniformly associated with overall survival and are biologically meaningful molecular signatures with survival data and network topology. This study introduces a biologically informed node-weighted centrality framework which integrates the biologically derived information from the survival analysis into the graph-theoretic network analysis for biomarker prioritization. The proposed methodology is an extension of our previously reported biomarker discovery pipeline, where the node weighting is introduced using a survival-guided approach, while the previously validated biological dataset and the biomarker set remain. This framework offers a computational approach to biomarker prioritization based on networks of biological relevance which can be extended to GBM and other complex diseases.

Keywords

Gene Co-Expression Network, Centrality Measures, Class-weights, survival analysis, Node Weighted Centrality,

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References

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