ArticleDisease markers2022
Development and Validation of a Liquid-Liquid Phase Separation-Related Gene Signature as Prognostic Biomarker for Low-Grade Gliomas.
Article in Disease markers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Biomolecular condensates in lung cancer: from molecular mechanisms to therapeutic targeting.Cell death discovery · 2025Review
- Three Liquid-Liquid Phase Separation-Related Genes Associated with Prognosis in Glioma.Pharmacogenomics and personalized medicine · 2024Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aim: To explore whether the liquid-liquid phase separation- (LLPS-) related genes were potential prognostic markers that could contribute to the further classification of low-grade gliomas (LGGs). Methods: The LLPS-related genes were subjected to functional enrichment analysis. The univariable, least absolute shrinkage and selection operator, and multivariable stepwise Cox regression analyses were performed to develop an LLPS-related gene signature (GS) in the discovery data set. The biological characteristics of the high-risk LGG were explored using gene set enrichment analysis. Two independent external data sets were used to validate the LLPS-related GS. Results: LLPS-related genes are involved in multiple important cancer-related biological processes and pathways in LGG. Nine LLPS-related genes were identified to construct the LLPS-related GS, which was significantly associated with the prognosis of LGG patients. The LLPS-related GS could successfully divide patients with LGG into high- and low-risk groups, and the high-risk group showed a poorer prognosis than the low-risk group. Furthermore, the LLPS-related GS was independent of IDH and 1p19q status. Several cancer-related pathways may be more active in high-risk LGGs, such as IL6 JAK STAT3 signaling pathway. The LLPS-related GS was successfully validated with two independent external data sets. Conclusion: We developed and validated a novel LLPS-related GS for risk stratification of LGG. Our findings may provide more precise management for LGGs and a useful reference for LLPS mechanism to link LGG studies.
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