2.1 Pan-cancer NAT10 mRNA expression levels
The mRNA expression levels of NAT10 were analyzed in Oncomine over a cancer-wide range. NAT10 expression was higher in cancer groups compared with the respective normal groups, including bladder, breast, colorectal, esophageal, gastric, liver, lung, kidney, and prostate cancers, as well as leukemia and myeloma. Interestingly, lower expression of NAT10 was found in one leukemia dataset (Fig. 1A). The NAT10 expression data for multiple cancers from Oncomine are summarized in Supplementary Table 1.
The GTEx database was also used to examine different tissues from healthy individuals; the mRNA expression levels of NAT10 were similar across all tissues (Fig. 1D), with the notable exceptions of bone marrow and blood. More significantly, in different cancer cell lines from the CCLE database, not only were NAT10 expression levels widely increased, but there was a narrower range of expression compared with that in normal tissues. (Fig. 1E). Furthermore, the pan-cancer expression of NAT10 was examined based on RNA sequencing data from TCGA using TIMER. Details of expression in tumor tissues and adjacent normal tissues are shown in Fig. 1B. NAT10 expression was significantly increased in 17 of 23 cancer types. Based on combined data from TCGA and GTEx, the expression of NAT10 was significantly elevated in 26 of 27 cancers (Fig. 1C).
2.2 Analysis of the pan-cancer link between NAT10 expression and multifaceted prognostic value
We assessed the correlation between the respective expression levels of NAT10 and OS, PFS, DFS, and DSS in different cancer types using a single-variate Cox regression analysis based on TCGA. The results are summarized in Fig. 2A-D. Nine of the 33 cancer types showed significant relationships between NAT10 expression levels and OS, seven showed significant relationships with PFS, five with DFS, and seven with DSS. Overall, the HRs for NAT10 were significant for LIHC, HNSC, ACC, KIRP, and PCPG with respect to OS, PFS, DFS, and DSS. In addition, survival curves comparing high and low expression of NAT10 in different types of cancer in the TCGA database were shown in supplementary figure 1.
Using Kaplan-Meier Plotter and GEPIA, high expression of NAT10 in HNSC, KIRP, LIHC and PCPG had worse outcomes from Kaplan-Meier Plotter in OS and RFS (Fig. 3A-H). For ACC, HNSC, KIRP and LIHC, NAT10 significantly decreased the OS in GEPIA (Fig. 3I-M). In addition, compared with low expression levels, high expression levels of NAT10 were correlated with poorer DFS in ACC, KIRP and LIHC in GEPIA (Fig. 3N-P). Using PrognoScan, we analyzed the role of NAT10 in each cancer type (number of cancer types = 12) and the relationships between NAT10 expression and prognosis in different cancers. The results are shown in Supplementary Table 2. Therefore, these results suggest that NAT10 expression is an independent risk factor for poor prognosis in these cancers.
2.3 High NAT10 expression affects the prognosis of LIHC with different clinicopathological features
In order to determine the relevance and underlying mechanisms of NAT10 expression in LIHC, we first analyzed NAT10 expression at different stages of LIHC, ACC, KIRP, and HNSC using TIMER. The expression of NAT10 at stage III showed a significant increase compared with stage I (Fig. 4A-D). The relationships between NAT10 expression and clinicopathological features were investigated by combining clinical and pathological data in Kaplan-Meier Plotter. With respect to OS and PFS, almost all characteristics showed a detrimental role of NAT10 in patients with LIHC, except for grade 2 (N =174, HR = 1.92, 95% CI = 0.97 to 3.97, P = 0.0564), AJCC_T 1 (N = 180, HR = 1.6, 95% CI = 0.89 to 2.89, P =0.1146), and micro-vascular invasion (N = 90, HR =2.02, 95% CI =0.9 to 4.57, P =0.0833) for OS; and stage 2 (N = 84, HR = 1.87, 95% CI = 0.99 to 3.54, P = 0.0501), grade 2 (N = 175, HR =1.51, 95% CI = 0.98 to 2.35, P = 0.0619), non-vascular invasion (N = 204, HR = 1.54, 95% CI = 0.96 to 2.49, P = 0.0721), and micro-vascular invasion (N = 91, HR = 1.76, 95% CI = 0.97 to 3.19, P = 0.0583) for PFS (Supplementary Table 3). Therefore, the expression of NAT10 seems to be an independent risk factor in prognosis of LIHC.
2.4 NAT10 expression is correlated with pan-cancer immune infiltration levels
Previous studies have proved that tumor-infiltrating lymphocytes can affect patient survival (31), and the above results demonstrate a powerful pan-cancer effect of NAT10 on prognosis. Thus, we explored the relationships between inflammatory infiltration and NAT10 expression. Using TIMER datasets, we calculated the coefficients of NAT10 expression and immune infiltration levels in 40 cancer types. The results show that NAT10 expression has significant positive correlations with tumor purity in 15 types of cancer. In addition, NAT10 expression had significant correlations with infiltrating levels of B cells in 15 types of cancer, CD8+ T cells in 17 types of cancer, CD4+ T cells in 20 types of cancer, macrophages in 13 types of cancer, neutrophils in 23 types of cancer, and DCs in 19 types of cancer (Supplementary Table 4).
To investigate the distinct types of cancers in which NAT10 was associated with prognosis and immune infiltration, and considering that tumor purity influences the analysis of immune infiltration, we first assessed the relationships between NAT10 expression and tumor purity in the above five types of cancer. Two types (ACC and HNSC) of the five showed significant positive correlations with tumor purity in TIMER. In addition, consistent positive correlations with different types of infiltrating immune cells were seen in LIHC: neutrophils (R = 0.162, P = 0.009) and DCs (R = 0.129, P = 0.039) in KIRP; B cells (R = 0.243, P = 0.002) and macrophages (R = 0.221, P = 0.004) in PCPG; B cells, CD4+ T cells, neutrophils, and DCs in ACC; and CD8+ T cells, neutrophils and DCs in HNSC showed positive correlations with NAT10 expression (Fig. 5A-E). These findings strongly suggest that NAT10 affects patient survival via interactions with immune cell infiltration in cancers including LIHC.
2.5 Relationships between NAT10 expression and immune markers
To further investigate the correlations between NAT10 and different types of infiltrating immune cells, we analyzed the relationships between NAT10 and immune cell markers using TIMER and GEPIA. In TIMER, after adjustments for tumor purity, NAT10 expression was significantly associated with 42 of 45 immune cell markers in LIHC; however, it was significantly correlated with only 22 gene markers in KIRP, eight gene markers in ACC, 28 gene markers in HNSC and 21 gene markers in PCPG (Table 1).
As shown in Fig. 5, B cells, CD4+ T cells, and macrophages were the three immune cell types most strongly correlated with NAT10 expression in LIHC. However, these correlations were not found in KIRP. The relationships between NAT10 expression and B cells, CD4+ T cells, and macrophage markers also showed differences between LIHC and KIRP. First, as for B cells and macrophage markers, we analyzed the correlations of NAT10 expression in tumor and normal tissues for LIHC and KIRP based on the GEPIA database. Notably, the correlations between NAT10 and TAMs were similar to those found using TIMER, suggesting that NAT10 is correlated with TAM infiltration in LIHC. Second, NAT10 expression in LIHC and KIRP showed partial difference in its relationships with CD8+ T cells, Tfh cells, Th2 cells, Th9 cells, Th17 cells, Th22 cells, neutrophils, and NK cells. In addition, NAT10 in LIHC had significant correlations with T cell exhaustion markers including PD-1 and CTLA4, and monocyte markers including CD14 and CD16, whereas NAT10 in KIRP showed no such relationships. We also used MCPcounter datasets to analyze the correlations between NAT10 expression and other immune cells; the results, shown in Supplementary Fig. 2, revealed strong positive correlations of endothelial cells and fibroblasts with NAT10 expression in KIRP and LIHC. Therefore, these results further confirm the findings that NAT10 is specifically correlated with immune infiltrating cells in LIHC, demonstrating that NAT10 has a vital role in immune escape in LIHC.
2.6 Pan-cancer correlation of NAT10 expression with expression of immune checkpoint genes
Tumor immunotherapy is a novel treatment that involves restarting and maintaining the tumor-immune cycle to restore the body’s normal anti-tumor immune response. Immune checkpoint genes are the main direction for monoclonal antibody inhibitors, cancer vaccines, cell therapies, and small-molecule inhibitors (32). Thus, we analyzed the relationships between NAT10 expression and 47 immune checkpoint genes in the above five types of cancer. Figure 6 shows the most significant positive correlations in KIRP (15 of 47) and LIHC (31 of 47); no such strong relationships were found in HNSC (three of 47), ACC (three of 47), or PCPG (seven of 47), but there were positive correlations. Therefore, these results further suggest that NAT10 expression has a vital role related to immune checkpoint genes in KIRP and LIHC (Fig. 6A).
2.7 Relationships between NAT10 expression and immune neoantigens, TMB and MSI
Neoantigens are new unnatural proteins encoded by mutated genes in tumor cells, which can be used to synthesize new antigen vaccines to activate immunity and achieve a therapeutic effect (33). Hence, we counted the number of new antigens in the above five types of cancer and analyzed the relationships between NAT10 expression and these antigens. The results are shown in Fig. 6B. Surprisingly, there was no relationship between NAT10 expression and antigens.
Tumor mutation load (or TMB) (34), a quantifiable biomarker used to reflect the number of mutations contained in tumor cells, and MSI (35), the emergence of a new microsatellite allele in the tumor, are valid prognostic biomarkers and indicators of immune therapy response in many tumor types. Therefore, we analyzed the correlations of NAT10 expression with TMB and MSI in the above five types of cancer, using Person correlation. As shown in Fig. 6C, NAT10 expression was positively correlated with low TMB in KIRP (P = 0.0061). In addition, the coefficient values for MSI indicated that NAT10 expression is positively correlated with high MSI in LIHC (P = 0.0092, Fig. 6D). Overall, these results show that the relationships of NAT10 expression with TMB and MSI are diverse among these five types of cancer.
2.8 Interactions and correlations of predicted proteins with NAT10 in LIHC
NAT10, the only confirmed regulator of mRNA acetyltransferase, shows remarkable correlation with LIHC. However, as in the case of m6A RNA methylation regulators which change the levels of m6A, the details of the compounds involved in the acetylation of mRNA are not clear. To further explore the mechanism of NAT10 in liver cancer, STRING tools were used to predict the proteins interacting with NAT10 (Fig. 7A); these included NOL10 (nucleolar protein 10, NOL10), HEATR1 (HEAT repeat-containing protein 1), BMS1 (ribosome biogenesis protein), TBL3 (transducin beta-like protein 3), WDR46 (WD repeat-containing protein 46), NOL6 (nucleolar protein 6), IMP4 (U3 small nucleolar ribonucleoprotein protein IMP4), UTP20 (small subunit processome component 20 homolog), NOP14 (nucleolar protein 14), and UTP18 (U3 small nucleolar RNA-associated protein 18 homolog). The interaction network was further supported by the correlation analysis in LIHC. Interestingly, NAT10 expression was strongly positively correlated with the 10 predicted genes (Fig. 7B). Furthermore, relationships between prognosis and expression of the 10 genes were investigated in LIHC with respect to OS and RFS. As shown in Supplementary Table 5, in almost all cases, higher expression of the 10 genes was associated with poor prognosis in LIHC patients, similar to NAT10. Therefore, it is reasonable to conclude that the interactions between NAT10 and these 10 genes influence the prognosis of LIHC patients.