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

• Published in: BMC cancer Vol. 21; no. 1; p. 1209
• Main Authors: Xie, Hui, Yuan, Ce, Ding, Xiao-Hui, Li, Jin-Jiang, Li, Zhao-Yang, Lu, Wei-Cheng
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 12.11.2021; BioMed Central; BMC
• Subjects: Algorithms; Brain cancer; Cancer; Cellular signal transduction; Databases, Genetic; Datasets; Development and progression; Differentially expressed genes; DNA microarrays; Gene expression; Gene Expression Profiling; Genetic aspects; Genomes; Health aspects; Humans; Immunity, Cellular; Immunological memory; Immunoregulation; Infiltration; Interleukin 17; Interleukin-17 - metabolism; Lymphocytes B; Lymphocytes T; Mast cells; Mast Cells - cytology; Medical prognosis; Memory B Cells - cytology; Memory cells; Meningeal Neoplasms - genetics; Meningeal Neoplasms - immunology; Meningeal Neoplasms - pathology; Meningioma; Meningioma - genetics; Meningioma - immunology; Meningioma - pathology; Metastases; MicroRNAs - metabolism; miRNA; miRNAs; Molecular modelling; Myc protein; Oncology, Experimental; Pathways; Patients; Principal components analysis; Protein interaction; Protein Interaction Maps; Proteins; Resting mast cells; Signal Transduction; Software upgrading; Survival analysis; T-Lymphocytes, Regulatory - cytology; Therapeutic targets; Transcription factors; Tumor necrosis factor; Tumors; China; Resting mast cells; Differentially expressed genes; Pathways; miRNAs; Meningioma
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-021-08931-0

To identify candidate key genes and pathways related to resting mast cells in meningioma and the underlying molecular mechanisms of meningioma. 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. 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. 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.