Identification of key genes and pathways associated with resting mast cells in meningioma

Abstract

Background To identify candidate key genes and pathways related to resting mast cells in meningioma and the underlying molecular mechanisms of meningioma. Methods Gene 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. Results A 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. Conclusion The 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.

Synthesizing Molecular and Immune Characteristics to Move Beyond WHO Grade in Meningiomas: A Focused Review

“…developed a risk score by analyzing differentially expressed genes related to resting mast cells, immune cell abundance, miRNA-mRNA co-expression network, and drug-gene interaction prediction. Importantly, the 9 key genes identified were important in the signaling of TNF-alpha, IL-17, and other cytokines, supporting the importance of further elucidating the interplay between molecular and immunologic signatures ( 90 ).…”

“…Similarly, CCL2, a monocyte chemoattractant, was found to be highly expressed in meningioma tissue ( 89 ). Cytokine-cytokine receptor interaction networks have already been developed to inform prognostic scoring of meningioma samples ( 72 , 90 ). A consistent finding of these studies is that the cytokine milieu is extraordinarily heterogenous between tumors, and some have posited that ligand/receptor imbalance may impact tumor progression ( 86 ).…”

Noncoding RNA landscape and their emerging roles as biomarkers and therapeutic targets in meningioma

“… 52 Interestingly, downregulation of miR-145-5p has also been associated with resting mast cells involved in meningioma development. 53 …”

Two novel predictive biomarkers for osteosarcoma and glycolysis pathways: A profiling study on HS2ST1 and SDC3

“…Immune cells have been shown to be dysregulated in a variety of tumors and it may be an integral factor in cancer progression. [16,17] Hence, in order to gain more insight into the immune cell typing and deeply dissect the composition of immune cells in OS, we performed an analysis using the CIBERSORT [18] software.…”