ArticleCancer medicine2023
Weighted correlation network analysis identifies multiple susceptibility loci for low-grade glioma.
Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis.BMC genomics · 2023Article
- Weighted correlation network analysis identifies multiple susceptibility loci for low-grade glioma.Cancer medicine · 2023Article
- Analysis of comorbid characteristics, molecular mechanisms between mild cognitive impairment and Alzheimer's disease with interventions in a community-based population in Shanghai.Frontiers in neurologyArticle
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsIn 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.
resultsSequencing 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.
conclusionThis 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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