3.1. Siglec10 expression in glioma patients with bioinformatics datasets
The expression of siglec10 was discovered in different subtypes and grades of gliomas. As shown in Fig. 1 and Fig. 2, the siglec10 expression was higher in tumor tissue than normal tissue in LGG and GBM. Siglec10 expression in was higher in high grade gliomas (grade III, IV) than low grade gliomas (grade II). In addition, the expression of siglec10 was lower in IDH mutated gliomas than IDH wide-type gliomas. As for the different subtypes of gliomas, siglec10 expression was higher in mesenchymal subtype than classical, neural, and proneural subtypes.
In addition, bioinformatics datasets TCGA and CGGA were used to analyze the survival prognosis of glioma patients. High siglec10 expression patients had shorter survival prognosis than low siglec10 expression patients in TCGA dataset (P = 0.00058) and CGGA dataset (P ༜ 0.0001) (Fig. 1E, 1F).
3.2. Higher siglec10 expression predicts worse survival in glioma patients
To comfirm the results above, 162 samples of glioma patients from Sanbo Brain Hospital Capital Medical University were analyzed by immunohistochemical staining method. The results showed that high siglec10 expression patients had shorter survival prognosis than low siglec10 expression patients (P = 0.00044, Fig. 3A). Figure 3B displayed the clinical forest of siglec10 expression in glioma patients. High siglec10 expression had shorter survival prognosis than low siglec10 expression in patients with grade IV (P = 0.003), GBM (P = 0.003), ATRX loss (P ༜ 0.001), no radiotherapy (P = 0.022), or no chemotherapy (P = 0.016).
3.3. Siglec10 was significant correlated with tumorigenic inflammatory cells, immune checkpoints and immune related genes
It has been reported that siglec10 was an innate immune checkpoint in macrophage and may as a potential target for immunetherapy in ovarian and breast cancer (13). Therefore, we mainly focused on its immune mechanism in glioma. Firstly, the correlation between siglec10 and inflammatory cells in tumor microenvironment was revealed. Inflammatory cells were clustered into three clusters with hclust method. In TCGA dataset, siglec10 was classfied into tumorigenic inflammatory cells including regulatory T cells, myeloid-derived suppressor cells (MDSCs), macrophages, and immature dendritic cells (Cell cluster-A, Fig. 4A). In CGGA dataset, siglec10 was classfied into tumorigenic inflammatory cells including regulatory T cells, myeloid-derived suppressor cells (MDSCs), macrophages, plasmacytoid dendritic cells, and neutrophils (Cell cluster-A, Fig. 4B).
Secondly, the correlation between siglec10 and immune related genes was discovered by ssGSEA, either. Siglec10 was positively correlated with immune related genes including cytokines, chemokines, interferons, BCR signaling pathway, antigen processing and presentation, etc (Fig. 4C and 4D).
Furthermore, the relationship between siglec10 and other immune checkpoints was discovered. In TCGA dataset, siglec10 was correlated with programmed cell death 1 (PDCD1), cytotoxic T-lymphocyte-associated protein 4 (CTLA4), lymphocyte activating 3 (LAG3), T cell immunoreceptor with Ig and ITIM domains (TIGIT) (Fig. 4E). In CGGA dataset, siglec10 was correlated with PDCD1, CTLA4, LAG3, TIGIT, either (Fig. 4F).
3.4 Siglec10 was associated with multiple immune-related signaling pathways
We used GSEA analysis to investigate the related inflammatory pathways of siglec10 (Fig. 5). In TCGA dataset, siglec10 was related with IL6-JAK-STAT3 signaling pathway, reactive oxygen species, TGFβ signaling pathway. In CGGA dataset, siglec10 was related with IL6-JAK-STAT3 signaling pathway, reactive oxygen species, TGFβ signaling pathway, either.
3.5. GO and KEGG analysis
In this study, we used GO and KEGG analysis to conduct functional enrichment analysis. The data obtained from TCGA dataset were performed GO and KEGG analysis (Fig. 6A and 6B). GO analysis indicated the correlated genes of siglec10 were enriched in neutrophil activation, neutrophil degranulation, neutrophil medicated immunity, T cell activation, regulation of lymphocyte activation, etc. KEGG signaling pathway analysis showed that the correlated genes of siglec10 were related with tuberculosis, osteoclast differentiation, phagosome, staphylococcus aureus infection, cytokine-cytokine receptor interaction, etc.
Furthermore, the data obtain from CGGA dataset were performed GO and KEGG analysis (Fig. 6C and 6D). GO analysis indicated the correlated genes of siglec10 were enriched in neutrophil activation, neutrophil degranulation, neutrophil medicated immunity, T cell activation, immune response-regulating cell surface receptor signaling pathway, etc. KEGG signaling pathway analysis showed that the correlated genes of siglec10 were related with osteoclast differentiation, cytokine-cytokine receptor interaction, phagosome, tuberculosis, staphylococcus aureus infection, B cell receptor signaling pathway, etc.