3.1. Expression levels analysis and high NAPSB inferred a better prognosis for HCC
NAPSB transcription levels in different human tumors were showed in Fig. 1A. Compared with adjacent normal tissues, expression of NAPSB in BLCA (bladder urothelial carcinoma), COAD (colon adenocarcinoma), LIHC (liver hepatocellular carcinoma), LUAD (lung adenocarcinoma), LUSC (lung squamous cell carcinoma) and READ (rectal adenocarcinoma) was significantly decreased. For TCGA cohort, we analyzed paired samples by paired Student’s t-test to verify the above results in HCC (Fig. 1B). To fully demonstrate this expression difference, we validated it with multiple datasets, including ICGC, GSE55092, GSE54236, and GSE121248, finding that NAPSB was indeed significantly decreased in HCC tissues (Fig. 1C). Moreover, NAPSB expression was examined in 13 paired HCC and adjacent normal tissues of Zhongnan cohort by RT-PCR and we obtained consistent results. (Fig. 1D).
The correlation between NAPSB and clinicopathologic characteristics for TCGA and ICGC cohorts were presented in Supplementary Tables 5 and 6. In addition, K-M survival analysis showed that high expression of NAPSB was linked to better overall survival than its low expression (Fig. 1E) and more significantly associated with better disease-free survival (Fig. 1F) and progression-free survival (Fig. 1G). Its overall survival value was also verified in the ICGC cohort (Fig. 1H). Univariate Cox regression analysis showed that NAPSB expression were significantly associated with better DFI and PFI outcomes (Supplementary Fig. 1A) and multivariate Cox regression analysis further validated it (Supplementary Fig. 1B). Therefore, NAPSB expression was beneficial to overall survival and could serve as an independent predictor of disease-free survival and progression-free survival in HCC.
3.2. Enrichment analyses inferred NAPSB was related to immune activation
Correlation between NAPSB and other genes was analyzed using TCGA-HCC data and there were 930 genes significantly associated with NAPSB (p-value < 0.01, |Spearman`s correlation| ≥ 0.45; Supplementary Table 7). The correlation of NAPSB with the top 50 co-expressed genes was showed in Fig. 2A, which contained some immune-related molecules like CD48, CD37, IL6, HLA-DQA1. Meanwhile, DEGs analysis between NAPSB subgroups showed that there were 993 upregulated in the NAPSB-high group compared with the NAPSB-low group (adjusted p-value < 0.05 and | log2 (fold change) | value ≥ 1.3; Supplementary Table 8). The top 10 upregulated genes also contained immune-related molecules, such as CD48, CD37, CCR5 (Fig. 2B), suggesting that NAPSB may be involved in immunity.
Thereafter, the intersection of co-expressed genes and upregulated DEGs including 476 common genes were obtained as the most closely related genes to NAPSB (Fig. 2C; Supplementary Table 9). The GO analysis for these common genes demonstrated they were enriched in processes such as T cell activation, regulation of T cell activation, and regulation of immune effector process (Fig. 2D; Supplementary Table 10). The KEGG analysis showed they were associated with chemokine signaling pathway, Th17 cell differentiation and T cell receptor signaling pathway (Fig. 2E; Supplementary Table 11). Most biological functions and signaling pathways were immune-related, strongly implying that NAPSB may mediate the TME in HCC.
Even further, we conducted GSEA and GSVA between NAPSB subgroups and also identified many significant pathways related to immunity (Fig. 2F, G; Supplementary Table 12, 13). These findings paralleled the above results.
3.3. NAPSB shaped an immuno-hot and inflamed TME in HCC
The immunological role of NAPSB was comprehensively explored subsequently using TCGA and ICGC cohorts. NAPSB was found to upregulated the expression of critical immunomodulators (including MHC, immunostimulator, chemokine, and receptor) (Fig. 3A), which may upregulate the activities of the cancer–immunity cycle subsequently. Then ESTIMATE algorithm was applied to calculate the immune score, stromal score, estimated score and tumor purity. We found these scores were significantly increased NAPSB-high group (Fig. 3B) while the tumor purity was negatively correlated with the expression of NAPSB (Fig. 3C). As for TME immune cell infiltration, almost all immune cells were significantly enriched in NAPSB-high group (Fig. 3D). Consistent with these, the infiltration levels of CD8 + T cells, CD4 + T cells, NK cells, B cells, DCs and macrophages were almost positively correlated with NAPSB in six different algorithms (Fig. 3E). In line with these, NAPSB was positively correlated with the marker genes of these six major types of immune cells (Fig. 3F). These results suggested NAPSB was associated with an inflamed TME. Even further, we observed the NAPSB expression positively correlated with the T cell inflamed score (TIS) and all of genes within this signature (Fig. 3G, H), further confirming its roles in shaping a hot inflamed TME. These findings were all verified in ICGC cohort and obtained consistent results (Supplementary Fig. 2).
Finally, we evaluated the correlation between NAPSB and seven steps of cancer-immunity cycle, which conceptualized the anti-cancer immune response . Overall, In the NAPSB-high group, the activities associated with the majority of the steps in the cycle were notably upregulated (Fig. 3i), including the release of cancer cell antigens (Step 1), priming and activation (Step 3), trafficking of immune cells to tumors (Step 4) and infiltration of immune cells into tumors (Step 5). In summary, these data consistently indicated that high expression of NAPSB was to transform a non-inflamed TME into an immuno-hot and inflamed microenvironment, consequently triggering anti-cancer immune response.
3.4. NAPSB highly expressed in hot tumors and may enhance immunotherapy response
Unsupervised clustering was conducted to classified HCC samples into hot tumors and cold tumors based on the hot tumor signature genes (Supplementary Table 14; Fig. 4A-D) . The expression of NAPSB was compared between hot and cold tumors and we found that it was overexpressed in hot tumors (Fig. 4E), suggesting that NAPSB could play a role in distinct hot/cold tumor states and be associated with therapeutic response to immunotherapy. The same methods were used to validate above results in the ICGC cohort (Supplementary Fig. 3A-E).
In addition, NAPSB expression was found to be positively correlated with BTLA, CTLA-4, IDO1, LAG-3, PD-1, PD-L1, TIGIT, and TIM-3 expression (Fig. 4F), which were well-known predictors of response to immunotherapy. Also, the enrichment scores of therapeutic signatures, predicting clinical response, were compared in NAPSB subgroups. As exhibited in Fig. 4G, I, NAPSB was negatively correlated with the enrichment scores of PPARG network, β-catenin signaling pathway, VEGFA and IDH1, which were all immunosuppressive gene signatures [39–42]. However, in the NAPSB-high group, immunotherapy-positive pathways such as IFN-γ-signature, APM-signal, EGFR-ligands, hypoxia and KDM6B were activated (Fig. 4H) [43–47], indicating immune-activated state and beneficial to immunotherapy response. These observations were also validated using ICGC samples (Supplementary Fig. 3F-H).
The last but important, the role of the NAPSB in predicting the immune checkpoint blockade (ICB) response was explored in tow immunotherapy-related melanoma cohorts. In GSE91061, we found the ICB response rates were obviously higher in the NAPSB-high group than in the NAPSB-low group (Fig. 4J) and the expression of NAPSB was significantly high in response group (Fig. 4K). Similar results were observed in the GSE78220 cohort (Supplementary Fig. 3I). These evidences reconfirmed that NAPSB may be a valuable predictor of immunotherapy response across cancers.
3.5. NAPSB was associated with increased sensitivity to chemotherapy
Using data from GDSC and CTRP, the role of NAPSB in chemotherapy sensitivity was analyzed. Intriguingly, NAPSB expression was negatively associated with IC50 of most agents in GDSC and CTRP (Fig. 5A and Supplementary Fig. 4; Supplementary Table 15), supporting that NAPSB can enhance the therapeutic response to chemotherapy. Two heat maps showed that the IC50 of some commonly used drugs for HCC was lower in the NAPSB-high group in GDSC and CTRP databases, respectively (Fig. 5B, C). Results above speculated that high expression of NAPSB is beneficial to the sensitive response of chemotherapy.
Thereafter, by analyzing GSE104580, a HCC cohort of TACE, we found the expression of NAPSB was significantly higher in TACE response group (Fig. 5D) and the response rates were obviously higher in the NAPSB-high group than in the NAPSB-low group (Fig. 5E). This data further illustrated that high expression of NAPSB may be beneficial to chemotherapy response.
3.6. Association of NAPSB with cell death of tumor cells
Given that cell death had been reported in recent years to play a significant role in tumor therapy , we investigated the association between NAPSB and various forms of cell death, including pyroptosis, necroptosis, apoptosis, autophagy and ferroptosis. As showed in Fig. 6A-C, and E, NAPSB expression was markedly correlated with pyroptosis, apoptosis and necroptosis, but negatively correlated with ferroptosis. Autophagy had no correlation with NAPSB expression (Fig. 6D). We also verified these discoveries with ICGC cohort. In line with these findings, the correlations between NAPSB and several cell death forms were consistent with that in the ICGC cohort (Fig. 6F, G). Among the above results, the correlation between NAPSB and pyroptosis was the most significant. Results above inferred that NAPSB may have a beneficial effect on immunotherapy and chemotherapy responses by promoting PANoptosis in tumor therapy.