ArticleNeuro-oncology2025
High peritumoral network connectedness in glioblastoma reveals a distinct epigenetic signature and is associated with decreased overall survival.
Article in Neuro-oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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15 authors.
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Abstract
backgroundGlioblastomas are functionally integrated into their peritumoral neural environment, and the dynamic functional interaction can be analyzed using network theory, providing insights into the tumor-brain interface. We investigated the peritumoral network connectedness of glioblastomas, revealing its association with distinct epigenetic signatures, its influence on survival, and its susceptibility to modification through surgical treatment.
methodsResting-state fMRI was performed on 48 glioblastoma patients. Tumor lesions were segmented, and networks were constructed at 10 mm and 40 mm distances from the tumor margin. These networks were mirrored to the healthy hemisphere to compare lesional and contralesional networks. The difference between lesional and contralesional mean degree centrality was calculated to assess the peritumoral network connectedness. Its correlation with epigenetic signatures and effect on overall survival were analyzed. Surgery-induced changes in the peritumoral network connectedness were evaluated in 7 patients with follow-up data.
resultsMean degree centrality was significantly higher in the lesional compared to the contralesional network (P = .032), indicating a tumor-induced effect on its local environment and reflecting high peritumoral network connectedness. Glioblastomas with a neural high epigenetic signature exhibited increased peritumoral network connectedness (P = .010), which was associated with decreased survival (P = .036). Postoperative peritumoral network connectedness tended to decrease, suggesting that surgical resection disrupts the functional communication between the tumor and its peritumoral environment.
conclusionsThe role of network features in predicting patient survival suggests their clinical relevance as imaging biomarkers for assessing personalized treatment strategies, which may include targeting crucial nodes for disconnection or even neuromodulation of neural circuits.
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