1. There Was an Association in Survival and Score in glioma Patients
To ascertain the association between immune and stromal estimates ratio and survival rate, survival analysis was demonstrated for ImmuneScore, StromalScor, as well as ESTIMATEScore, using the Kaplan–Meier survival analysis. The count of immune or stromal components of TME were expressed as an estimate in ImmuneScore or StromalScore. In comparison with the median, glioma patients were segmented into high and low groups. The results in Figure 1 showed, the scores of immune cells and stromal cells content were markedly connected with the survival in glioma patients. In other words, these revealed that the immune, stromal and estimate components of TME were more appropriate indicators of prognosis in patients with glioma(p<0.01).
2. The Score Was Correlated with Clinicopathologic Stage in glioma Patients
Clinical information on glioma patients in TCGA database was obtained to ascertain the association in the ratio of immune and stromal components with clinicopathological trait. The figure2 showed that immune score and stromal score were markedly positively association in tumor age and grade (Figure2A, Figure2C, Figure2D, and Figure2F, p< 0.001). From the results, we can see that the quantity of immune and stromal components was relevant to glioma evolution, such as age and grade, but there was little correlation with gender.
3. The DEG Shared by The ImmuneScore and StromalScore Were Principally Enriched in Immune-related Genes
We performed comparative analyses of high-score and low-score samples to ensure the definite variation in the gene profiles of immune and stromal components of TME. Contrasted to the median, we got 1655 DEGs in the high-score and low-score samples of Immunescore. There has 1026 up-regulated genes and 629 down-regulated genes in all genes (Figure3A, Figure3C, and Figure3D). Analogously, 1813 differential genes were acquired in StromalScore, with 1204 up-regulated genes and 609 down-regulated genes (Figure3B, Figure3C, and Figure3D). The intersecting point analysis of the Venn plot displayed that 950 up-regulated genes overlapped in the aggregate in ImmuneScore and StromalScore analysis, with 488 down-regulated genes overlapped in ImmuneScore and StromalScore. These genes (total 1438 genes) may be influencing the status of TME. At the GO enrichment analysis, we can conclude the DEGs was majority consistent with GO term in relation to immunity. For instance, neutrophil activation, neutrophil mediated immunity and leukocyte migproportionn (Figure 3E). Similarly, Neuroactive ligand-receptor interaction, Cytokine-cytokine receptor interaction and Tuberculosis enrichment were shown at the Kyoto Encyclopedia of KEGG enrichment analysis in the same way (Figure 3F). Hence, this entire function in DEGs seemingly to be connected with immune-related activities, which manifests that the exist of immune factors has an influence for certain in TME of glioma.
4. Intersection Analysis of PPI Network and Univariate COX Regression
According to STRING database, the PPI network was built by using Cytoscape software (3.7.2). Figure 4A displayed the interaction between genes (confidence 0.95), and the bar chart shown the previous 30 genes permutated by the count of nodes (Figure 4B). We intersected the 30 genes in PPI and the genes with a pvalue of <0.05 by the univariate cox regression, and screened out thirty genes (Figure 4C). To determine the value of risk among the 30 factors, we performed univariate Cox regression analysis on glioma patients. Figure 4D shows that only a few genes have low risk values
5.Correlation Between Survival Analysis and SYK Expression in Patients with glioma
An interesting gene, SYK, is thought to regulate the growth of epithelial cells in human breast cancer. Research found out that SKY has a great relationship with the occurrence and development of tumors[12, 15]. We analyzed the data obtained from TCGA database that compared with normal tissues, SKY expression in the tumor group was significantly increased (p<0.001) (Figure 5A), and survival analysis of SYK displayed that the survival rate of glioma patients with low SYK expression was observably higher compared to the glioma patients with high SYK expression, the entire survival of tumor patients was observably reduced (Figure 5B). Multivariate Cox survival analysis confirmed that high SYK expression level was an autocephalous predictor of undesirable prognosis in glioma patients (p <0.001). The results show in figure 5C that SYK is a high-risk gene in glioma. These results indicated that SYK significant high expression in glioma samples than in normal samples, and the expression of SYK was an autocephalous predictor of undesirable prognosis in glioma patients.
6. SYK May Be an Indicator of TME regulation
Since SYK levels were inversely associated with the survival in glioma patients, we divided the data of glioma patients into high group of expression and low group of expression and severally contrasted them to the median level of SYK expression in GSEA. As we can see from Figure 6A, SYK high expression group principally enriched immune-related activities. For example, B cell receptor signaling pathway, hematopoietic cell lineage as well as autoimmune thyroid disease. In contrast, there was almost no gene sets enrichment in the low expression group of SYK. The afore-mentioned consequences revealed that SYK can be a potential indicator possibly of TME status.
7. Correlation Between SYK Expression and the Ratio of TICs
To further verify the association in the expression of SYK and the immune microenvironment, we analyzed the ratio of tumor infiltrating immune subsets by utilizing CIBERSORT algorithm and established a spectrum of 22 immune cells in glioma samples (Figure 7AB). Immune cells difference analysis results show that there are 6 different immune cells were related to the expression of SYK (Figure 7C), Immune cell correlation analysis results show that there are 9 different immune cells were related to the expression of SYK (Figure 7D). The difference analysis and correlation analysis show that there are 4 different immune cells that were associated with the SYK expression. Among them, two kinds of immune cells were positively correlation to the SYK expression, comprising CD4 memory resting T cell and Monocytes;and two different types of immune cells were inversely associated with the SYK expression, including T cells follicular helper and Macrophages M0. All of these results were reflected ulteriorly that the part of SYK in modulating immune response and playing a key part in TME.