DEGs screened by ImmuneScore and StromalScore in the TIME
375 GC samples were divided into high-score and low-score groups based on the median of ImmuneScore and StromalScore. To explore the changes of the immune and stromal components in the TIME, we performed a series of comparative analyses of GC samples with high and low scores. | logFC|> 1 was used as the screening criteria, and a p-value of < 0.05 indicated statistical significance. In the StromalScore group, 800 DEGs were obtained, of which 716 were upregulated genes and 84 were downregulated genes. 609 DEGs were identified in the ImmuneScore group, of which 415 were upregulated genes and 194 were downregulated genes. Finally, 1409 DEGs were obtained, of which 1131 were downregulated genes and 278 were upregulated genes. The expression of differential genes was visualized using heatmaps (Fig. 1A,B). The Venn diagram showed that 146 up-regulated genes (Fig. 1C) and 39 down-regulated genes (Fig. 1D) were shared by the ImmuneScore and StromalScore groups. Finally, 185 DEGs were finally obtained through the analysis of Venn diagram.
GO and KEGG enrichment analyses results of DEGs
In order to understand the molecular mechanism the above 185 DEGs affecting the TIME of GC patients, GO and KEGG functional analyses were conducted. The GO results indicated that these DEGs primarily participated in the following biological processes: leukocyte proliferation and regulation, lymphocyte proliferation and regulation, monocyte proliferation and regulation, regulation of immune effect process, and leukocyte mediated immune regulation. In terms of the GO molecular function, DEGs mainly focused on regulating the immune receptor activity, cytokine receptor activity, chemokine receptor activity, chemokine binding, etc. (Fig. 2A,B). KEGG results indicated that these DEGs primarily participated in the cytokine-cytokine receptor interactions, chemokine signaling pathways, viral protein-cytokine and cytokine receptor interactions, and B-cell receptor signaling pathways (Fig. 2C). These DEGs might be correlated with the regulation of immune response and TIME in patients with GC.
Interactive analysis results of PPI network and univariate Cox regression analysis
To further determine the interaction among the 185 DEGs, a PPI network was drawn (Fig. 3A) and the top 37 genes were screened according to the number of nodes (Fig. 3B). To explore the impact of 185 DEGs on the GC patients’ survival, univariate Cox regression analysis was conducted to screen out the 15 genes with the most significant impact (Fig. 3C). Finally, results of the intersection analysis between the leading nodes of the PPI network and 15 genes screened by univariate Cox regression revealed that only two genes, ELANE and CNTN2, overlapped (Fig. 3D). In this study, we selected ELANE for subsequent analyses.
Correlation between ELANE expression level and the clinical parameters of GC patients
The mRNA expression of ELANE was analyzed based on the TCGA database. The expression of ELANE was low in GC samples and high in normal samples (p < 0.001, Fig. 4A). When the expression level of ELANE in tumor and normal tissues of the same GC patient was compared, the expression of ELANE in normal tissues was also obviously higher than that in tumor (p < 0.001, Fig. 4B). As shown in Fig. 4C, ELANE was mainly co-expressed with Myeloperoxidase (MPO), Cathepsin G (CTSG), Multimerin-1 (MMRN1), Collectin-12 (COLEC12) and Phosphodiesterase 1A (PDE1A). However, using IHC staining for 86 pairs of tumor and normal tissues, there was no difference in ELANE protein expression in GC tissues compared to normal tissues (p > 0.05, Fig. 4D,E). Meanwhile, immunohistochemistry results showed that the ELANE protein was localized in the cytoplasm (Fig. 4D). Analysis of the ELANE expression level and clinical parameters of GC patients indicated that the ELANE expression was related to age (p = 0.0021, Fig. 4F). The expression of ELANE in T1 stage was obviously different from T2, T3, and T4 stages (p < 0.05, all), and the expression level of T1 stage was lower than T2, T3, and T4 stages (Fig. 4H). However, no difference was found in the ELANE expression levels among T2, T3, and T4 stage patients (p > 0.05) (Fig. 4H). Similarly, the ELANE expression in stage I was significantly different from stage II (p < 0.05), while the ELANE expression level of stage I was lower than stage II, III, and IV; however, no difference was found in the ELANE expression level between stage II, III, and IV (p > 0.05, Fig. 4K). In addition, no correlation was observed between ELANE expression levels and tumor grade, N stage, or M stage (p > 0.05, Fig. 4G–J). The above results revealed that the expression of ELANE showed an upregulated trend with the progression of GC, which indicated that the high expression of ELANE might be closely linked to tumor progression in GC patients.
The predictive value of ELANE in survival and prognosis of patients with GC
In this study, all samples were divided into ELANE high- and low-expression groups, and K–M survival curve was conducted to determine the OS of all patients. In TCGA cohort, the OS of GC patients with low ELANE expression level was longer than those with high ELANE expression level (p = 0.015, Fig. 5A), and the p-value was less than 0.05. In validation cohort, we observed identical results (p = 0.002, Fig. 5C). In order to test the predictive value of ELANE expression level on the survival cycle of GC patients, a ROC was established in this study. In TCGA cohort, ROC indicated that the 1-year, 3-year, and 5-year AUC values for GC patients were 0.869, 0.981, and 0.995, respectively (Fig. 5B). In validation cohort, ROC indicated that the 5-year AUC value for GC patients was 0.749 (Fig. 5D). Therefore, ELANE was considered as a good predictor of survival. Meanwhile, Cox regression analysis was also carried out. The univariate Cox regression analysis results showed that age (p = 0.003), T (p = 0.026), N (p = 0.006), M (p = 0.003), high StromalScore (p = 0.046), and high expression level of ELANE (p = 0.009) were risk factors for the prognosis of GC patients (Table 1). However, multivariate Cox regression analysis showed that age (p < 0.001), N (p = 0.03), M (p < 0.001), and high ELANE expression level (p = 0.019) were independent risk factors for the prognosis of GC (Table 1). Therefore, the expression of ELANE was negatively related to the GC patients’ survival cycle; moreover, ELANE is a potential biomarker that can predict the GC patients’ survival and prognosis.
Relationship between ELANE and TIME in patients with GC and results of GSEA
This research investigated the correlation between the expression level of ELANE and TIME in patients with GC and found that the expression of ELANE was significantly correlated with the ImmuneScore, StromalScore, and ESTIMATEScore in the TIME (Fig. 6A). There was a negative correlation between the expression of ELANE and TMB (R = − 0.38, p < 0.001; Fig. 6B). To explore the relevant mechanisms of ELANE and TIME, GSEA was conducted. The GSEA results indicated that the gene sets in the ELANE high-expression group were primarily involved in the signaling pathways correlated with the regulation of immune response, such as the chemokine signaling pathway, leukocyte transendothelial migration, cytokine-cytokine receptor interaction, infection, cell adhesion molecules, and autoimmune diseases (Fig. 7A–H and Table 2). The gene sets in the ELANE low-expression group were mainly involved in the signaling pathways related to metabolism, such as oxidative phosphorylation, RNA degradation, proteasomes, protein export, mismatch repair, and nucleotide excision repair (Fig. 7I–P and Table 2). The above results suggest that ELANE might be an influencing factor for the TIME to maintain the immune status.
Relationship between ELANE expression levels and TIICs and ICs
To explore and determine the relationship and mechanism between ELANE expression level and TIME in patients with GC, CIBERSORT method was applied to estimate the proportion of 22 immune cell components and quantitatively analyze the immune infiltration. The differentially expressed immune cells were screened based on the level of ELANE expression. Eight differentially expressed immune cells were screened out, including resting CD4 memory T cells, T follicular helper cells, activated CD4 memory T cells, Mono cells, M0 macrophages, M1 macrophages, resting master cells, and resting dendritic cells (Fig. 8A,B). In addition, there was a positive correlation between ELANE expression level and T-cell regulation (Tregs) (R = 0.148, p = 0.024), B-cell memory (R = 0.135, p = 0.040), resting mast cells (R = 0.355, p < 0.001), monocytes (R = 0.287, p < 0.001), and resting dendritic cells (R = 0.196, p = 0.003) (Fig. 8C). There was a negative correlation between ELANE expression level and the activated CD4 memory (R = − 0.182, p = 0.005), resting NK cells (R = − 0.203, p = 0.002), follicular helper T cells (R = − 0.205, p = 0.002), M0 macrophages (R = − 0.209, p = 0.001), and M1 macrophages (R = − 0.230, p < 0.001) (Fig. 8C). The above results suggested that ELANE may participate in the regulation of TIICs in the TIME, and affect the TIME. To explore the clinical value of ELANE expression in immunotherapy of GC patients, we investigated the correlation between ELANE expression level and ICs gene expression level in GC. This results indicated that the expression level of ELANE was positively related to 15 kinds of IC genes (all: p < 0.05). The expression level of ELANE was negatively related to TNFRSF (p = 0.044) and HHLA2 (p = 0.024) (Fig. 9A,B). This finding suggests that ELANE may be a new target for immunotherapy of GC in the future.