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Weighted correlation network analysis identifies multiple susceptibility loci for low-grade glioma
Xiaodong Niu1, Qi Pan2, Qianwen Zhang3
1Department of Neurosurgery and West China Glioma Center, West China Hospital, Sichuan University, Chengdu, China.
Cancer Medicine
|October 28, 2022
Summary
Researchers identified MKLN1 as a key gene linked to low-grade glioma (LGG) recurrence. This finding offers potential biomarkers for LGG pathogenesis and suggests MKLN1 as a therapeutic target.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Current molecular classifications of low-grade gliomas (LGGs) do not fully explain their malignant behavior and recurrence.
- There is a need to identify key genes associated with LGG tumor recurrence.
Purpose of the Study:
- To identify hub genes related to tumor recurrence in low-grade gliomas (LGGs).
- To explore potential biomarkers for LGG pathogenesis and recurrence.
Main Methods:
- Weighted gene co-expression network analysis (WGCNA) was used to construct a gene-miRNA-lncRNA co-expression network for LGGs.
- GDCRNATools and the WGCNA R package were utilized for data analysis.
Main Results:
- Analysis of sequencing data from 502 LGG patients revealed 774 differentially expressed (DE) mRNAs, 49 DE miRNAs, and 129 DE lncRNAs.
- The expression of MKLN1 was found to be significantly related to tumor recurrence in LGG tissues compared to primary LGGs.
Conclusions:
- MKLN1 was identified as a potential biomarker for LGG pathogenesis and recurrence.
- MKLN1 is proposed as a potential therapeutic target for low-grade gliomas.
