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

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 c...

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Detalles Bibliográficos
Autores principales: Niu, Xiaodong, Pan, Qi, Zhang, Qianwen, Wang, Xiang, Liu, Yanhui, Li, Yu, Zhang, Yuekang, Yang, Yuan, Mao, Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10028094/
https://www.ncbi.nlm.nih.gov/pubmed/36305248
http://dx.doi.org/10.1002/cam4.5368
Descripción
Sumario: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.

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