ArticleBMC cancer2021
Identification of key genes and pathways associated with resting mast cells in meningioma.
Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 14 citations in OpenAlex.
- Establishment of the H8T-MG Meningioma Cell Line and Integrated Transcriptomics Reveal a Metabolic-Immune Signature in Diploid Transitional WHO Grade 1 Tumours.Biomolecules · 2026Article
- Article
- Identification and experimental validation of biomarkers associated with paraptosis in meningioma.Medicine · 2025Article
- Multi-omics and Mendelian randomization study explores potential therapeutic targets for meningiomas.Discover oncology · 2025Article
- Role of mast cell-derived exosomes in exacerbating neuronal injury of experimental cerebral malaria.Parasites & vectors · 2025Article
- Mast cell-mediated microRNA functioning in immune regulation and disease pathophysiology.Clinical and experimental medicine · 2025Review
- RNA sequencing and multiplexed immunohistochemistry reveal the factors for postoperative recurrence of stage IB-IIA cervical squamous cell carcinoma.Discover oncology · 2024Article
- Noncoding RNA landscape and their emerging roles as biomarkers and therapeutic targets in meningioma.Molecular therapy. Oncology · 2024Review
- Article
- Two novel predictive biomarkers for osteosarcoma and glycolysis pathways: A profiling study on HS2ST1 and SDC3.Medicine · 2022Article
- Synthesizing Molecular and Immune Characteristics to Move Beyond WHO Grade in Meningiomas: A Focused Review.Frontiers in oncology · 2022Review
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo identify candidate key genes and pathways related to resting mast cells in meningioma and the underlying molecular mechanisms of meningioma.
methodsGene expression profiles of the used microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. GO and KEGG pathway enrichments of DEGs were analyzed using the ClusterProfiler package in R. The protein-protein interaction network (PPI), and TF-miRNA- mRNA co-expression networks were constructed. Further, the difference in immune infiltration was investigated using the CIBERSORT algorithm.
resultsA total of 1499 DEGs were identified between tumor and normal controls. The analysis of the immune cell infiltration landscape showed that the probability of distribution of memory B cells, regulatory T cells (Tregs), and resting mast cells in tumor samples were significantly higher than those in the controls. Moreover, through WGCNA analysis, the module related to resting mast cells contained 158 DEGs, and KEGG pathway analysis revealed that the DEGs were dominant in the TNF signaling pathway, cytokine-cytokine receptor interaction, and IL-17 signaling pathway. Survival analysis of hub genes related to resting mast cells showed that the risk model was constructed based on 9 key genes. The TF-miRNA- mRNA co-regulation network, including MYC-miR-145-5p, TNFAIP3-miR-29c-3p, and TNFAIP3-hsa-miR-335-3p, were obtained. Further, 36 nodes and 197 interactions in the PPI network were identified.
conclusionThe results of this study revealed candidate key genes, miRNAs, and pathways related to resting mast cells involved in meningioma development, providing potential therapeutic targets for meningioma treatment.
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