Recently, tumor immunotherapy has attracted an increasing amount of attention, and it is considered one of the most likely treatment methods to improve the survival rate of cancer patients. Immunotherapy has been successful in a variety of malignant tumors, including lung cancer, and several immunotherapy drugs have been approved by the Food and Drug Administration (FDA) [22, 23]. However, the application of immunotherapy in OS has been slow due to the complexity of the immune system and the subtle differences in the tumor-specific microenvironment as well as the fact that the tumor itself may resist immunotherapy in various ways [24]. It is necessary to determine predictive biomarkers with higher sensitivity and specificity to stratify patients to maximize the benefits for patients. In the TME, the interaction among tumor cells, immune cells and stromal cells promotes tumor proliferation and metastasis [25, 26]. Therefore, a clear understanding of the complex TME and the complex immune characteristics of tumors and immune cells may be helpful to guide clinical immunotherapy.
The ESTIMATE and CIBERSORT algorithms are currently widely used to study the infiltration of immune cells and the proportion of cell subtypes in the TME. These two algorithms can transform the information of the gene expression matrix into an immune score and immune cell infiltration ratio. For instance, Lai et al. [27] studied the TME in renal cell carcinoma and identified effective biomarkers. Moreover, other tumor-related studies, such as lung cancer [28, 29] and liver cancer [30, 31], were also reported to use these algorithms. Interestingly, Hong et al. [32] performed a study on OS and found that SIGLEC7 and SP140 were related to OS prognosis. We will describe in more detail the differences between this study and another OS-related study. In summary, based on the above two algorithms, we can better understand the immune microenvironment information of OS, which is helpful to reveal the mechanism of tumor occurrence and discover effective biomarkers.
We searched the currently available public databases for OS, and the TARGET-OS and GSE39058 datasets were used in this study. Through the ESTIMATE algorithm, TARGET-OS showed that the immune score of the immune microenvironment of OS was closely related to prognosis; that is, the higher the immune score is, the better the prognosis. Although no statistically significant differences were found in GSE39058, it can be concluded from the survival curve that there was a significant difference between the high and low immune score groups. Therefore, we speculated that this might be caused by the insufficient number of patients. Then, we obtained the DEIGs by analyzing the difference between the high and low immune score groups and determined the intersection of DEIGs in the two datasets. The GO results revealed that these DEIGs were involved in various key immune responses, such as leukocyte proliferation, regulation of leukocyte differentiation, negative regulation of leukocyte activation, regulation of innate immune response and leukocyte-mediated cytotoxicity, while KEGG pathway analysis suggested that osteoclast differentiation, NK cell-mediated cytotoxicity and adaptive immune system were the most pivotal pathways affected by these genes. These items provide a direction for us to further study the mechanism of the immune response. Using univariate and multivariate regression analyses, we found that only MAP3K15 was statistically significant in both databases. MAP3K15, also known as ASK3, was reported to respond to osmotic stress in the kidney [33], and no research has been found to relate MAP3K15 to tumors. Thus, MAP3K15 may also play a significant role in tumor initiation and progression.
Subsequently, the CIBERSORT algorithm further identified the proportion of infiltrating immune cells in the TME of OS. We found that memory B cells, naive T cells, M2 macrophages and NK cell-activated cells were the main infiltrating cells. Then, we discussed the correlations between these immune cells and MAP3K15, as well as the correlations between these immune cells and the high- and low-risk groups. Finally, we found that MAP3K15 can well predict the prognosis of patients with OS, and MAP3K15 is closely related to CD4 T cells, M0 macrophages, and M1 macrophages; these results may provide new ideas for immunotherapy in the clinic.
In addition, we noticed that Hong et al. [32] also studied the microenvironment of OS. Although we both studied the TME of OS, we think that the two studies are different. In this research, we found a common DEIG (MAP3K15) by intersecting the analysis results, while in the study of Hong, they determined that SIGLEC7 and SP140 are DEIGS. This is reasonable because different research strategies were utilized. Moreover, we performed Cox regression analysis and analyzed the correlation of MAP3K15 and immune cells. In Hong’s study, they did not further study the relationship between genes and immune cells.
Immune cell infiltration and the TME are hot research topics at present, and the development of bioinformatics methods has further improved our understanding of tumor molecular biology. However, there are still many limitations in this study. Although we validated the expression of MAP3K15 using qPCR assay and IHC and found that patients with high MAP3K15 expression had a significantly poorer prognosis, the correlation between immune infiltrated cells and MAP3K15 expression was not tested because of restrictions associated with our experimental conditions. Since we found a potential association between MAP3K15 expression and immunity, further mechanisms need to be explored.
Taken together, our study found that MAP3K15, whose expression level is closely related to immune activity in tumors, is a critical immune-related biomarker and may provide a basis for OS immunotherapy.