2.1 Analysis Process of This Study
To estimate the number of immune and matrix components and the proportion of TICs in AML samples, clinical data of 200 cases and 151 transcriptomic data were downloaded from the TCGA database, and then ImmuneScore and StromalScore were obtained by ESTIMATE algorithm. DEGs were obtained by differential analysis. The network core genes were found using PPI network. The prognosis-related genes obtained using univariate COX regression analysis, and both were cross-tabulated to finally find a single gene, ITGB2. ITGB2 was used to perform a series of subsequent analyses regarding AML survival, GSEA, TICs correlation, etc.
2.2 Correlation between Immune/Stromal/ESTIMATE Score and AML survival
To determine the potential association of overall survival with immune scores and stromal scores, we classified the 200 AML cases into high and low subgroups based on the median score of ImmuneScore or StromalScore. Higher scores estimated in the ImmuneScore or StromalScore indicate more immune or stromal components in the TME. ESTIMATEScore was the sum of the ImmuneScore and StromalScore indicating the combined proportion of both components in the TME. As shown in Fig. 1, the results revealed that the proportion of immune components and ESTIMATEScore had negative correlation with the overall survival rate, with no statistically significant differences, while StromalScore was not correlated with the survival rate. These results implied that the immune components of TME may have implications for the prognosis of AML patients.
2.3 Identification of DEGs based on immune scores and stromal scores in AML
The AML patient's immune and stromal scores were divided into two groups with high and low scores, and the scores were compared between the two groups, setting the cut-off criteria as p < 0.05 and |fold change| > 1 to find the genes with differences. The higher the patient's immune cell content, the higher the gene expression level. Comparing the gene expression levels of the high and low scoring subgroups, 897 DEGs were obtained, of which, 655 genes were upregulated and 242 genes were downregulated (Figs. 2B,C,D). Similarly, 785 DEGs were obtained from StromalScore, consisting of 567 up-regulated genes and 218 down-regulated genes (Figs. 2A,C,D). The DEGs of immune cells and stromal cells were taken to intersect to obtain common DEGs, which were represented by differential Venn diagrams. A total of 502 genes were upregulated and 122 genes were downregulated. These 624 DEGs may be determinants of the tumor microenvironment in AML patients.
Results from gene ontology (GO) enrichment analysis indicated that the DEGs almost mapped to the immune-related and inflammation-related GO terms, such as neutrophil activation and positive regulation of cytokine production (Fig. 2E). The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis also displayed the enrichment of cytokine–cytokine receptor interaction, and osteoclast differentiation, phagosome, staphylococcus aureus infection, tuberculosis (Fig. 2F). Thus, the overall functions of DEGs are associated with the immune and inflammatory response, which implied that the involvement of immune factors was a predominant feature of TME in AML.
2.4 Intersection Analysis of PPI Network and Univariate COX Regression
To investigate whether there are protein interactions between these DEGs, we constructed a PPI network based on the STRING database and Cytoscape software. The interconnections between the 624 genes can be seen in Fig. 3A. Among them, the top 30 genes ranked by the number of gene-linked nodes were shown in the bar chart (Fig. 3B). Univariate COX regression analysis was performed for survival of AML patients, and the top 20 genes were obtained in order of P-value (Fig. 3C). Then, a cross-tabulation analysis was performed between the 30 genes of the leading nodes in the PPI network and the top 20 genes of the COX regression analysis. Only one factors, ITGB2, was overlapping from the above analyses (Fig. 3D).
2.5 The Correlation of ITGB2 Expression with the Survival in AML Patients
The gene product of ITGB2 is one of the βintegrins, which is reported to be expressed mainly on immune cells. It has been suggested that it can be involved in leukocyte extravasation, binding and clearance of complement fragments, phagocytosis and killing of intracellular pathogenic microbes [17, 18]. ITGB2 has only been reported in CLL in hematologic tumors. However, it has never been reported in acute leukemia. In this study, all AML samples were divided into high and low groups by median of ITGB2 expression. The survival analysis showed that AML patients with ITGB2 low expression had longer survival than that of ITGB2 high expression (p = 0.007) (Fig. 4A). Given the levels of ITGB2 were negatively correlated with the survival of AML patients, ITGB2 expression-associated signal pathways were investigated by GSEA enrichment analysis. ITGB2 is an immune-related factor, and the higher the gene expression, the more active some signaling pathways are, such as B cell receptor singnaling pathway, chemokine signaling pathway, Toll like receptor singnaling pathway (Fig. 4B). These results suggested that ITGB2 might be a potential indicator of the tumor microenvironment in AML patients.
2.6 Correlation between ITGB2 expression and TICs
To further confirm the correlation between ITGB2 gene and TICs, we analyzed the proportion of tumor-infiltrating immune subgroups using the CIBERSORT algorithm. We used both immune cell differential analysis and correlation tests to find 22 immune cells in AML samples (Fig. 5). In the difference analysis of immune cells, there were 12 kinds of immune cells in the two groups with high and low expression of ITGB2 have statistical difference (Fig. 6A). The results from the correlation analysis showed that a total of 10 kinds of TICs were correlated with the expression of ITGB2 (Fig. 6B). Among them, only one kind of TICs, monocytes was positively correlated with ITGB2 expression; nine kinds of TICs were negatively correlated with ITGB2 expression, including native B cell, CD8 + T cells, resting NK cells, and resting Mast cells, activated NK cells, activated mast cells, resting CD4 memory T cells, Plasma cells and follicular helper T cells. These results further proved that the levels of ITGB2 expression was closely correlated with immune cells in the TME.