Multi-omics data integration for subtype identification of Chinese lower-grade gliomas: A joint similarity network fusion approach
Year: 2022
Type: article
Abstract: Lower-grade gliomas (LGG), characterized by heterogeneity and invasiveness, originate from the central nervous system. Although studies focusing on molecular subtyping and molecular characteristics have provided novel insights into improving the diagnosis and therapy of LGG, there is an urgent need to identify new molecular subtypes and biomarkers that are promising to improve patient survival outcomes. Here, we proposed a joint similarity network fusion (Joint-SNF) method to integrate different omics data types to construct a fused network using the Joint and Individual Variation Explained (JIVE) technique under the SNF framework. Focusing on the joint network structure, a spectral clustering method was employed to obtain subtypes of patients. Simulation studies show that the proposed Joint-SNF method outperforms the original SNF approach under various simulation scenarios. We further applied the method to a Chinese LGG data set including mRNA expression, DNA methylation and microRNA (miRNA). Three molecular subtypes were identified and showed statistically significant differences in patient survival outcomes. The five-year mortality rates of the three subtypes are 80.8%, 32.1%, and 34.4%, respectively. After adjusting for clinically relevant covariates, the death risk of patients in Cluster 1 was 5.06 times higher than patients in other clusters. The fused network attained by the proposed Joint-SNF method enhances strong similarities, thus greatly improves subtyping performance compared to the original SNF method. The findings in the real application may provide important clues for improving patient survival outcomes and for precision treatment for Chinese LGG patients. An R package to implement the method can be accessed in Github at https://github.com/Sameerer/Joint-SNF.
Authors: Lingmei Li, Yifang Wei, Guojing Shi, Haitao Yang, Zhi Li, Ruiling Fang, Hongyan Cao, Yuehua Cui
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,800
Funders: Michigan State University, National Natural Science Foundation of China, Department of Education of Hebei Province, Natural Science Foundation of Hebei Province, Shanxi Medical University, Applied Basic Research Project of Shanxi Province, China, College of Engineering, Michigan State University
OpenAlex ID: W4283784350