Finally, immune and stromal scorings were derived using the ESTIMATE method. The percentage of TICs was computed through CIBERSOR. The MMP9 as a TME-related gene was identified by utilizing the univariate Cox regression analysis and protein-protein interaction (PPI) networks.
Correlation between ESTIMATE score and prognosis
The immune and stromal score both have negative correlation with the survival rate of BLCA patients (Figure 2 (a, b)), but the estimate score has no correlation with the survival rate of BLCA (Figure 2 (c)). Next, we compare the clinicopathological staging and gender of the two high and low score groups. Shown as Figure 3, stromal score and estimate score is correlated with stage II and III, stage II and IV in tumor stage respectively (Figure 3 (a, b)), and immune score has no significant correlation with tumor stage (Figure 3 (c)); however, all the score was uncorrelated with the clinical stage (Figure 3 (d-f)), indicating that two types of scores are relevant to the progression of BLCA. Moreover, all scores are also related to gender. Our result shows that the prevalence of men is higher than that of women, which is consistent with previous conclusions.
Filter DEGs by score
There are 461 up-regulated DEGs and 35 down-regulated DEGs screened totally in immune score analysis (Figure 4 (a, c, d)). 496 up-regulated DEGs and 54 down-regulated DEGs screened in stromal score analysis (Figure 4 (b, c, d)). At the same time, we acted GO and KEGG pathway analysis of 280 co-expressed genes. The results of GO analysis show that the co-expressed genes are mainly enriched in the regulation of immune effect process, immune cell proliferation and cytokine activation, etc. and immune response (Figure 4 (e)). KEGG analysis results show that DEGs are mainly enriched in cytokines, the formation of extracellular bactericidal network of neutrophils and phagosomes (Figure 4 (f)), which are all related to immunity. Summing up the two above enrichment analysis results, we conclude that the function of differential genes may be involved in immune reaction, showing that immune reaction is essential to the TME of BLCA.
PPI networks and COX regression analysis of co-expressed genes
We employed the STRING online tool to build a PPI network in order to analyze the relationship between DEGs, and then visualized it via Cytoscape version 3.9.0 (Figure 5 (a)). And then We the top 30 genes were selected in the number of protein interaction network nodes (Figure 5 (b)). What’s over, we completed univariate COX regression analysis to find the relationship between the above genes and BLCA, and finally got 4 genes (Figure 5 (c)). Then, we used the Venn diagram to take the intersection that obtained from the top 30 genes in the number of protein interaction network nodes and the genes gotten via the univariate COX regression analysis (Figure 5 (d)). Finally, we screened out the MMP9.
Expression difference of MMP9 and clinical correlation analysis
After obtaining the gene, we analyzed the expression differences of MMP9 in the tissue of normal and tumor and studied its relationship with BLCA. MMP9 is significantly differently expressed in the tumor tissue and normal tissue, it expressed highly in the tumor tissue (P=0.003) (Figure 6 (a)). The results of the survival analysis showed that the survival rate of patients about MMP9 gene high expression is lower than that with low expression in BLCA (P=0.021)（Figure 6 (b). Besides, MMP9 has significant differences in expression with tumor stage, including stage II and stage III and stage IV (P<0.001) (Figure 6 (c, d)). The higher the staging, the higher the expression. In summary, it may be related to the prognosis of BLCA about the expression of MMP9.
Enrichment analysis of MMP9
Then we make enrichment analysis via GSEA version 4.1.0. The co-expressed genes are mostly enriched in chemokine signaling pathways, cytokine, and cell adhesion molecules (CAMs), all of which are related to immune response in the high expression group of MMP9(Figure 7). Interestingly, the enrichment analysis of MMP9 low expression is not significant. The enrichment analysis results show that there is a very close relationship between MMP9 and TME for bladder cancer.
Correlation between MMP9 and TICs
We analyzed the proportion of 22 types of TICs determined in BLCA case by the way of CIBERSORT algorithm in R tool (Figure 8(a-b)). Then we made a difference and correlation analysis between the high expression groups and low expression groups of MMP9 and TICs to further confirm the role of MMP9 in TME. Difference analysis showed that 7 types of TICs were related to the expression of MMP9 (Figure 8(c)). Correlation analysis externalized that the 10 types of TICs were correlated with the expression level of MMP9 (Figure 8(d)). Then we obtained 6 types of TICs related to the expression of MMP9 when taking the intersection of the two analysis results (Figure 8(e)). Among them, macrophages M0 and eosinophils are positively correlated with the expression of MMP9. On the other hand, activated dendritic cells, mast cells, CD8 T cells, and monocytes are negatively correlated with the expression of MMP9. The above results further prove that MMP9 may have great importance in the immune response of bladder cancer TME.
Correlation analysis of immune checkpoint genes
We performed the correlation analysis of immune checkpoint genes through the R tool, suggesting that it is positively correlated with the expression levels of MMP9 and 36 immune check sites (Figure 9).