Open AccessWeighted correlation network analysis identifies multiple susceptibility loci for low‐grade glioma
Author(s) -
Niu Xiaodong,
Pan Qi,
Zhang Qianwen,
Wang Xiang,
Liu Yanhui,
Li Yu,
Zhang Yuekang,
Yang Yuan,
Mao Qing
Publication year - 2023
Publication title -
cancer medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 53
ISSN - 2045-7634
DOI - 10.1002/cam4.5368
Subject(s) - glioma , microrna , biology , gene , gene expression , pathogenesis , correlation , computational biology , oncology , genetics , cancer research , bioinformatics , medicine , immunology , geometry , mathematics
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.