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Weighted correlation network analysis identifies multiple susceptibility loci for low‐grade glioma

Authors :
Xiaodong Niu
Qi Pan
Qianwen Zhang
Xiang Wang
Yanhui Liu
Yu Li
Yuekang Zhang
Yuan Yang
Qing Mao
Source :
Cancer Medicine, Vol 12, Iss 5, Pp 6379-6387 (2023)
Publication Year :
2023
Publisher :
Wiley, 2023.

Abstract

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.

Details

Language :
English
ISSN :
20457634
Volume :
12
Issue :
5
Database :
Directory of Open Access Journals
Journal :
Cancer Medicine
Publication Type :
Academic Journal
Accession number :
edsdoj.2a9c3577cfd34994ac938d0bb05ea25e
Document Type :
article
Full Text :
https://doi.org/10.1002/cam4.5368