ORIGINAL RESEARCH article
Front. Oncol.
Sec. Neuro-Oncology and Neurosurgical Oncology
Network connectome analysis of multi omics data identifies molecular markers of recurrence and grade progression in meningioma
Provisionally accepted
Jeong-An Gim1
Hyunjun Jo2
Woo Keun Kwon2Chang Hwa Ham2Hae Won Roh2Wonki Yoon2Jong Hyun Kim2Taek Hyun Kwon2
Joonho Byun2,3*- 1Soonchunhyang University, Asan-si, Republic of Korea
- 2Korea University Guro Hospital, Seoul, Republic of Korea
- 3Korea University, Seongbuk-gu, Republic of Korea
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Background: Meningiomas are usually benign, but some behave aggressively with early recurrence. Histopathological grading alone often fails to predict outcomes. We developed a network connectome and clustering framework that integrates DNA methylation, RNA-seq, and proteomic data to identify molecular interaction patterns linked to recurrence and grade progression. Methods: Using genome-wide methylation, transcriptomic, and proteomic profiles, we constructed multi-layer connectome networks representing inter-omic correlations. Nodes and edges were analyzed by centrality and clustering metrics to detect key molecular modules associated with clinical outcomes. Results: Distinct network clusters differentiated recurrent and higher-grade meningiomas from indolent ones. A total of 29 methylation, 32 gene, and 33 protein features were significantly related to recurrence; 70, 61, and 56 features were linked to grade progression. Recurrent tumors showed increased inter-omic connectivity and altered hub distributions. LINC01397 emerged as a recurrent hub across omic layers, suggesting its role as a potential unified biomarker. Conclusion: Our connectome-based multi-omics analysis reveals that meningioma aggressiveness is driven by coordinated molecular interactions rather than single-omic alterations. This systems-level approach provides a compact, data-driven framework for predicting recurrence and grade, supporting precision risk stratification in clinical practice.
Keywords: analysis, LINC01397, Meningioma, network connectome, Recurrence
Received: 13 Nov 2025; Accepted: 16 Feb 2026.
Copyright: © 2026 Gim, Jo, Kwon, Ham, Roh, Yoon, Kim, Kwon and Byun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Joonho Byun
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