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

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.

Identification and Validation of the Prognostic Panel in Clear Cell Renal Cell Carcinoma Based on Resting Mast Cells for Prediction of Distant Metastasis and Immunotherapy Response.

Clear cell renal cell carcinoma (ccRCC) has a high metastatic rate, and its incidence and mortality are still rising. The aim of this study was to identify the key tumor-infiltrating immune cells (TIICs) affecting the distant metastasis and prognosis of patients with ccRCC and to construct a relevant prognostic panel to predict immunotherapy response. Based on ccRCC bulk RNA sequencing data, resting mast cells (RMCs) were screened and verified using the CIBERSORT algorithm, survival analysis, and expression analysis. Distant metastasis-associated genes were identified using single-cell RNA sequencing data. Subsequently, a three-gene (CFB, PPP1R18, and TOM1L1) panel with superior distant metastatic and prognostic performance was established and validated, which stratified patients into high- and low-risk groups. The high-risk group exhibited lower infiltration of RMCs, higher tumor mutation burden (TMB), and worse prognosis. Therapeutically, the high-risk group was more sensitive to anti-PD-1 and anti-CTLA-4 immunotherapy, whereas the low-risk group displayed a better response to anti-PD-L1 immunotherapy. Furthermore, two immune clusters revealing distinct immune, clinical, and prognosis heterogeneity were distinguished. Immunohistochemistry of ccRCC samples verified the expression patterns of the three key genes. Collectively, the prognostic panel based on RMCs is able to predict distant metastasis and immunotherapy response in patients with ccRCC, providing new insight for the treatment of advanced ccRCC.

A mast cell-related prognostic model for non-small cell lung cancer.

Background: The immune microenvironment of non-small cell lung cancer (NSCLC) plays a critical role in its treatment. Mast cells (MCs) appear to play a key role in the tumor microenvironment, and studies are needed to further elucidate the diagnosis and treatment of NSCLC. Methods: Data was collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses constructed a resting mast cell related genes (RMCRGs) risk model. Differences in the immune infiltration levels of diverse immune infiltrating cells between the high- and low-risk groups were identified by CIBERSORT. We analyzed the enrichment terms in the entire TCGA cohort using Gene Set Enrichment Analysis (GSEA) software version 4.1.1. We used Pearson correlation analysis to identify the relationships between risk scores, immune checkpoint inhibitors (ICIs), and tumor mutation burden (TMB). Finally, the half-maximal inhibitory concentration (IC50) values for chemotherapy in the high- and low-risk populations were evaluated via the R "oncoPredict" package. Results: We found 21 RMCRGs that were significantly associated with resting MCs. Gene ontology (GO) analysis showed that the 21 RMCRGs were enriched in regulating angiotensin blood levels and angiotensin maturation. An initial univariate Cox regression analysis was performed on the 21 RMCRGs, four of which were identified as significantly related to prognostic risk in NSCLC. Then, LASSO regression was carried out to construct a prognostic model. We found a positive correlation between the expression of the four RMCRGs with resting mast cell infiltration in NSCLC; the higher the risk score, the less resting mast cell infiltration and immune checkpoint inhibitor (ICI) expression. A drug sensitivity analysis showed a difference in drug sensitivity between the high- and low-risk groups. Conclusions: We constructed a predictive prognostic risk model for NSCLC containing four RMCRGs. We hope this risk model will provide a theoretical basis for future investigations on NSCLC mechanisms, diagnosis, treatment, and prognosis.