Dataset Information
Weighted correlation network analysis identifies multiple susceptibility loci for low-grade glioma.
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ABSTRACT: Background
The current molecular classifications cannot completely explain the polarized malignant biological behavior of low-grade gliomas (LGGs), especially for tumor recurrence. Therefore, we tried to identify suspicious hub genes related to tumor recurrence in LGGs.Methods
In this study, we constructed a gene-miRNA-lncRNA co-expression network for LGGs by a weighted gene co-expression network analysis (WGCNA). GDCRNATools and the WGCNA R package were mainly used in data analysis.Results
Sequencing data from 502 LGG patients were analyzed in this study. Compared with recurrent glioma tissues, we identified 774 differentially expressed (DE) mRNAs, 49 DE miRNAs, and 129 DE lncRNAs in primary LGGs and ultimately determined that the expression of MKLN1 was related to tumor recurrence in LGG.Conclusion
This study identified the potential biomarkers for the pathogenesis and recurrence of LGGs and proposed that MKLN1 could be a potential therapeutic target.
SUBMITTER: Niu X
PROVIDER: S-EPMC10028094 | biostudies-literature | 2023 Mar
REPOSITORIES: biostudies-literature
Publications
Weighted correlation network analysis identifies multiple susceptibility loci for low-grade glioma.
Cancer medicine 20221028 5
<h4>Background</h4>The current molecular classifications cannot completely explain the polarized malignant biological behavior of low-grade gliomas (LGGs), especially for tumor recurrence. Therefore, we tried to identify suspicious hub genes related to tumor recurrence in LGGs.<h4>Methods</h4>In this study, we constructed a gene-miRNA-lncRNA co-expression network for LGGs by a weighted gene co-expression network analysis (WGCNA). GDCRNATools and the WGCNA R package were mainly used in data analy ...[more]