3.1 High prognosis value of CTLA4 in ccRCC
In TCGA dataset, CTLA4 was highly expressed in tumor tissues (N=494) compared to normal tissues (N=68, p<0.001), and the paired test also confirmed that CTLA4 was highly expressed in ccRCC (N=68, p< 0.001) (Figure 1A, B). Moreover, CTLA4 overexpression in ccRCC had also been reproduced in the GEO datasets GSE40435 and GSE46699 (Figure 1C).
The immunohistochemistry was utilized to further validate the above results, and the result showed that the density of CTLA4 expression was higher in ccRCC tissues compared with normal tissues (p < 0.001, Figure 1D). It seems that CTLA4 was steadily up-regulated in ccRCC both in data mining and in vitro experiment.
In TCGA dataset, patients were initially classified into high and low groups based on CTLA4 level. The K-M curves showed that CTLA4 shortened the OS in ccRCC, with the median survival time being 63.73 and 91.73 months in high and low group, respectively (p<0.001) (Figure 1E). In addition, 512 cases from GEPIA also confirmed that CTLA4 was a risk gene with Hazard ratio (HR) >1 (HR = 1.5, p = 0.013, Figure 1F).
We next sought to investigate the role of CTLA4 in cancer progression, and the result suggested that the overexpressed CTLA4 was related to high grade (χ2=12.465, p<0.001), advanced stage(χ2= 22.510, p<0. 001), patient with tumor state(χ2= 7.874, p=0.005) and death (χ2= 9.965, p=0.002, Table 1). We also found that CTLA4 had a positive correlation with high grade in GSE40435 cohorts (χ2=3.971, p=0.046), indicating that CTLA4 may function as an oncogene in the progress of ccRCC. Subsequently, uni- and multi-variate Cox analyses found that some variables, including CTLA4, age, tumor pathological grade and stage, were independent risk factors in ccRCC (p < 0.01) (Table 2). These results proved that CTLA4 contributed to the progression of ccRCC with a high prognosis value.
3.2 CTLA4 indicated a higher density of TILs in ccRCC tumor microenvironment, but an immunosuppressed phenotype.
To outline the corresponding function of the CTLA4 in ccRCC TME, we performed KEGG and GO analysis based on 200 CTLA4 related protein coding genes (Supplementary Table 1). 35 KEGG items were identified, including T cell mediated immune related pathway, natural killer cell mediated cytotoxicity, T helper cells differentiation, PD-L1 expression and programmed death‐1 receptor (PD-1) checkpoint pathway in cancer (Table 3), and CTLA4 was positively correlated with T cell receptor signaling pathway (cor=0.80), Natural killer cell mediated cytotoxicity (cor=0.75), Th1 and Th2 cell differentiation (cor=0.75) and Th17 cell differentiation (cor=0.78) (Figure 2A). The biological processes of CTLA4 yielded from the GO analysis were associated with the activation and differentiation of T cells (Table 3) and positively correlated with T cell activation (cor=0.79), regulation of lymphocyte activation (cor=0.79) and T cell differentiation(cor=0.80) (Figure 2B).
Previous studies have demonstrated that TILs within the TME can be regarded as a prognostic indicator in ccRCC (21). Besides, the results of GO and KEGG promoted us to continue to investigate the role of CTLA4 in TILs infiltration, which affected the ICBs’ response. Our results showed that CTLA4 was associated with a higher immune score, which was calculated by the ESTIMATE algorithm and represented the level of TILs, indicating that CTLA4 promoted the recruitment of immune cells into the TME (Figure 2C). Furthermore, there was a great difference in the composition of TILs between high and low CTLA4 groups. CTLA4 increased the infiltration of T Cells CD8+, Tregs, Macrophage M1, whereas Plasma cells, NK cells activated, Monocytes, Macrophage M2, Dendritic cells activated were less infiltrated in CTLA4 high group (Supplementary Table 2). The correlation analysis result was presented in Figure 2D, showing that CTLA4 was positively correlated with CD8+ T cells (cor=0.50, p<0.001), Tregs (cor=0.28, p<0.001) (Figure 2D). However, the immunosuppression score as well as the expression of CD8+ T cell exhaustion markers Hepatitis A virus cellular receptor 2 (HAVCR2), lymphocyte activation gene-3 (LAG3), and T cell immunoglobulin and ITIM domain (TIGIT) was higher in CTLA4 high group (Figure 2E, F). All in all, CTLA4 changed the landscape of TILs in ccRCC TME, and indicated a higher density of TILs, especially the CD8+ T cells and Tregs, but faced an immunosuppressed phenotype.
3.3 Genetic altered by CTLA4 in ccRCC
Genetic changes include non-synonymous mutations, which are mainly composed of missense mutation, synonymous mutation, insertion or deletion, and copy number gain or loss (22-25). Tumor mutation burden (TMB) can be used as a biomarker to predict the efficacy of ICBs (26). Some studies have shown that the RCC was sensitive to ICBs, although the TMB in RCC was moderate (27, 28). To identify the somatic mutations that were altered by CTLA4 in ccRCC, we performed the mutation analysis and the result showed that overexpressed CTLA4 was correlated with BRCA-associated protein 1 (BAP1) mutation (p<0.05, Figure 3). The TMB in the high CTLA4 expression group tended to be higher than the low expression group, although it was not statistically significant. Moreover, Nonsense Mutation and In Frame Ins in the high CTLA4 expression group were higher than those in the low group (Table 4). BAP1 is a deubiquitinating enzyme and considered to be a tumor suppressor, and the loss of BAP1 contributes to the metastasis and poor prognosis in various cancers (29).
3.4 CTLA4 was highly related to other immune checkpoint molecules
Recently, the combined inhibition of PD-L1 and CTLA4 has attracted much attention (30). D Planchard et al reported that combination immunotherapy of PD-L1 and CTLA4 considerably prolonged the OS in advanced refractory colorectal cancer (31). Combination immunotherapy tends to replace monotherapy, for that the combinational usage of ICBs can produce higher synergistic anti-tumor efficiency and reduce side effects (32). Therefore, we continued to explore the correlation between CTLA4 and other immune checkpoint molecules, including PDCD1 (PD-1), CD274 (PD-L1), LAG3, indoleamine-2,3-dioxygenase-1 (IDO1), and TIGIT (33) (34). The results showed that CTLA4 was highly and positively related to PD-1, PD-L1, LAG3, IDO1, and TIGIT (Figure 4).