# Network connectome analysis of multi omics data identifies molecular markers of recurrence and grade progression in meningioma

## TL;DR

This study uses multi-omics data to identify molecular markers that predict meningioma recurrence and grade progression, improving risk assessment beyond traditional methods.

## Contribution

A novel network connectome framework integrating DNA methylation, RNA-seq, and proteomic data to identify molecular patterns linked to meningioma aggressiveness.

## Key findings

- Distinct network clusters differentiate recurrent and higher-grade meningiomas from indolent ones.
- 29 methylation, 32 gene, and 33 protein features were significantly related to recurrence.
- LINC01397 emerged as a potential unified biomarker across omic layers.

## Abstract

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.

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.

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.

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.