Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification
Abstract Gliomas are primary malignant brain tumors with a typically poor prognosis, exhibiting significant heterogeneity across different cancer types. Each glioma type possesses distinct molecular characteristics determining patient prognosis and therapeutic options. This study aims to explore the...
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BMC
2024-12-01
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| Series: | BioData Mining |
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| Online Access: | https://doi.org/10.1186/s13040-024-00411-y |
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| author | Nina Kastendiek Roberta Coletti Thilo Gross Marta B. Lopes |
| author_facet | Nina Kastendiek Roberta Coletti Thilo Gross Marta B. Lopes |
| author_sort | Nina Kastendiek |
| collection | DOAJ |
| description | Abstract Gliomas are primary malignant brain tumors with a typically poor prognosis, exhibiting significant heterogeneity across different cancer types. Each glioma type possesses distinct molecular characteristics determining patient prognosis and therapeutic options. This study aims to explore the molecular complexity of gliomas at the transcriptome level, employing a comprehensive approach grounded in network discovery. The graphical lasso method was used to estimate a gene co-expression network for each glioma type from a transcriptomics dataset. Causality was subsequently inferred from correlation networks by estimating the Jacobian matrix. The networks were then analyzed for gene importance using centrality measures and modularity detection, leading to the selection of genes that might play an important role in the disease. To explore the pathways and biological functions these genes are involved in, KEGG and Gene Ontology (GO) enrichment analyses on the disclosed gene sets were performed, highlighting the significance of the genes selected across several relevent pathways and GO terms. Spectral clustering based on patient similarity networks was applied to stratify patients into groups with similar molecular characteristics and to assess whether the resulting clusters align with the diagnosed glioma type. The results presented highlight the ability of the proposed methodology to uncover relevant genes associated with glioma intertumoral heterogeneity. Further investigation might encompass biological validation of the putative biomarkers disclosed. |
| format | Article |
| id | doaj-art-d6ab028c9cf24ce79a0200de015bf0f6 |
| institution | Kabale University |
| issn | 1756-0381 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj-art-d6ab028c9cf24ce79a0200de015bf0f62024-12-22T12:19:04ZengBMCBioData Mining1756-03812024-12-0117112910.1186/s13040-024-00411-yExploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratificationNina Kastendiek0Roberta Coletti1Thilo Gross2Marta B. Lopes3Institute for Chemistry and Biology of the Marine Environment, University of OldenburgCenter for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology (NOVA FCT)Institute for Chemistry and Biology of the Marine Environment, University of OldenburgCenter for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology (NOVA FCT)Abstract Gliomas are primary malignant brain tumors with a typically poor prognosis, exhibiting significant heterogeneity across different cancer types. Each glioma type possesses distinct molecular characteristics determining patient prognosis and therapeutic options. This study aims to explore the molecular complexity of gliomas at the transcriptome level, employing a comprehensive approach grounded in network discovery. The graphical lasso method was used to estimate a gene co-expression network for each glioma type from a transcriptomics dataset. Causality was subsequently inferred from correlation networks by estimating the Jacobian matrix. The networks were then analyzed for gene importance using centrality measures and modularity detection, leading to the selection of genes that might play an important role in the disease. To explore the pathways and biological functions these genes are involved in, KEGG and Gene Ontology (GO) enrichment analyses on the disclosed gene sets were performed, highlighting the significance of the genes selected across several relevent pathways and GO terms. Spectral clustering based on patient similarity networks was applied to stratify patients into groups with similar molecular characteristics and to assess whether the resulting clusters align with the diagnosed glioma type. The results presented highlight the ability of the proposed methodology to uncover relevant genes associated with glioma intertumoral heterogeneity. Further investigation might encompass biological validation of the putative biomarkers disclosed.https://doi.org/10.1186/s13040-024-00411-yCancer omics dataGraphical lassoNetwork centralityCausal networksModularity detectionSpectral clustering |
| spellingShingle | Nina Kastendiek Roberta Coletti Thilo Gross Marta B. Lopes Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification BioData Mining Cancer omics data Graphical lasso Network centrality Causal networks Modularity detection Spectral clustering |
| title | Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification |
| title_full | Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification |
| title_fullStr | Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification |
| title_full_unstemmed | Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification |
| title_short | Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification |
| title_sort | exploring glioma heterogeneity through omics networks from gene network discovery to causal insights and patient stratification |
| topic | Cancer omics data Graphical lasso Network centrality Causal networks Modularity detection Spectral clustering |
| url | https://doi.org/10.1186/s13040-024-00411-y |
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