Font Size:
Understanding omics links behind glioma heterogeneity: a network and clustering approach
Last modified: 2024-05-15
Abstract
Gliomas are primary malignant brain tumors known for their generally poor prognoses, largely due to the molecular heterogeneity observed across different tumor types. In this study, we propose a comprehensive strategy involving network discovery and clustering to explore the transcriptomics landscape of gliomas and support targeted therapeutic research on the potential biomarkers identified. In a first stage of the methodology proposed, the graphical lasso [1] algorithm is applied to disclose interactions among genes in each glioma type through the estimation of sparse transcriptomics networks. Centrality measures and modularity detection [2] are then used to aid in the identification of key genes in glioma types. In the second stage, spectral clustering [3] of patient similarity networks is applied to evaluate the suitability of the genes identified in grouping patients into the glioma types. The results obtained underscore the potential of the proposed approach in uncovering relevant genes associated with glioma heterogeneity. Further research efforts may involve the biological validation of the disclosed network insights on glioma heterogeneity.
Keywords
Glioma, omics, network, clustering
References
[1] Friedman, J., Hastie, T., Tibshirani, R.: Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3), 432--441 (2008)
[2] Newman, M.E.J.: Modularity and community structure in networks. PNAS 103(23), 8577–8582 (2006)
[3] John, C.R., Watson, D., Barnes, M.R., Pitzalis, C., Lewis, M.J.:
Spectrum: fast density-aware spectral clustering for single and multi-omic data. Bioinformatics 36(4), 1159--1166 (2020)