nSEA: n-Node Subnetwork Enumeration Algorithm Identifies Lower Grade Glioma Subtypes with Altered Subnetworks and Distinct Prognostics

Advances in molecular characterization have reshaped our understanding of low-grade glioma (LGG) subtypes, emphasizing the need for comprehensive classification beyond histology. Lever-aging this, we present a novel approach, network-based Subnetwork Enumeration, and Analysis (nSEA), to identify dis...

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Veröffentlicht in:Biocomputing 2024 Jg. 29; S. 521 - 533
Hauptverfasser: Zhang, Zhihan, Wang, Christiana, Zhao, Ziyin, Yi, Ziyue, Durmaz, Arda, Yu, Jennifer S., Bebek, Gurkan
Format: Buchkapitel Journal Article
Sprache:Englisch
Veröffentlicht: United States WORLD SCIENTIFIC 01.01.2024
Schlagworte:
Algorithms
Brain Neoplasms - genetics
Brain Neoplasms - pathology
Computational Biology
Glioma - genetics
Glioma - pathology
Humans
PRECISION MEDICINE: INNOVATIVE METHODS FOR ADVANCED UNDERSTANDING OF MOLECULAR UNDERPINNINGS OF DISEASE
Prognosis
Protein Interaction Maps
Network Analysis
Protein-protein Interaction Networks
Cancer Systems Biology
ISBN:9789811286421, 9811286426, 9811286434, 9811286418, 9789811286414, 9789811286438
ISSN:2335-6936, 2335-6936
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Zusammenfassung:Advances in molecular characterization have reshaped our understanding of low-grade glioma (LGG) subtypes, emphasizing the need for comprehensive classification beyond histology. Lever-aging this, we present a novel approach, network-based Subnetwork Enumeration, and Analysis (nSEA), to identify distinct LGG patient groups based on dysregulated molecular pathways. Using gene expression profiles from 516 patients and a protein-protein interaction network we generated 25 million sub-networks. Through our unsupervised bottom-up approach, we selected 92 subnetworks that categorized LGG patients into five groups. Notably, a new LGG patient group with a lack of mutations in EGFR, NF1, and PTEN emerged as a previously unidentified patient subgroup with unique clinical features and subnetwork states. Validation of the patient groups on an independent dataset demonstrated the robustness of our approach and revealed consistent survival traits across different patient populations. This study offers a comprehensive molecular classification of LGG, providing insights beyond traditional genetic markers. By integrating network analysis with patient clustering, we unveil a previously overlooked patient subgroup with potential implications for prognosis and treatment strategies. Our approach sheds light on the synergistic nature of driver genes and highlights the biological relevance of the identified subnetworks. With broad implications for glioma research, our findings pave the way for further investigations into the mechanistic underpinnings of LGG subtypes and their clinical relevance. Availability: Source code and supplementary data are available at https://github.com/bebeklab/nSEA
Bibliographie:ObjectType-Article-1
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ISBN:9789811286421
9811286426
9811286434
9811286418
9789811286414
9789811286438
ISSN:2335-6936
2335-6936
DOI:10.1142/9789811286421_0040