3.1 An abnormal mRNA expression, protein level and the prognostic value of AIMP1 in LUAD tissues
To determine the distribution of AIMP1 protein in tumor tissues of all types of cancers and their genomics, we used the TCGA database and the TIMER database. Thus, we found that the expression of AIMP1 was significantly upregulated in most types of tumor tissues, including LUAD samples (Figure 1A). Likewise, the expression of AIMP1 was also significantly upregulated in GEO data set (n = 561) (Figure 1B). Next, we used the Kaplan-Meier database to examine the connection between the clinical outcomes of AIMP1 expression in LUAD tissues. The results indicate that the prognosis of LUAD patients was better when the mRNA expression of AIMP1 was high (Figure 1C–D). Therefore, we investigated the prognostic ability of AIMP1 in LUAD tissues, and we verified the findings with GEO data. The results indicate that prolonged survival was possible in LUAD patients manifesting a high mRNA expression of AIMP1 (p < 0.001, Figure 1E). By performing univariate Cox analysis, we found the overall survival (OS) of LUAD patients was associated with the following parameters: age (HR= 1.008, p = 0.252), tumor size (HR= 0.984, p = 0.894), lymph node status (HR=0.834, p = 0.208), and AIMP1 expression (HR= 0.663, p < 0.001). The results of multivariate analysis indicate that AIMP1 expression (HR = 0.656, p < 0.001) had an independent prognostic value (Table 1). This finding was further illustrated in the forest map (Figure 1F-G). We measured the protein level of AIMP1 in four human LUAD tissues and its adjacent tissues by western blot analysis (Figure 2A). The quantitative results indicated that the protein level of AIMP1 was significantly greater in LUAD tissues (Figure 2B, p < 0.05).
3.2 The relationship between AIMP1 expression and clinicopathological parameters
The expression of AIMP1 gene is high in LUAD tissues. Therefore, we need to elucidate the relationship between the expression of AIMP1 and the clinicopathological parameters of LUAD patients. The mRNA level of AIMP1 was found to be associated with the size of tumor (p = 0.025) (Figure 3A). A high expression of AIMP1 indicated lymph node metastasis in LUAD patients (p = 0.046) (Figure 3B). Unfortunately, there was no significant association of AIMP1 expression with age and gender in LUAD patients (Figure 3C, D). The results of logistic regression analysis (Table 2) showed that AIMP1 expression was associated with age ≤ 65 (p = 0.049), and tumor stage (T3vsT1) (p = 0.046); however, no significant correlation was found between the expression of AIMP1 and other clinical parameters, such as gender and lymph node metastasis. A significant correlation was not observed between NTU Cohort and AIMP1 expression. The clinical parameters (age, gender, smoking history, clinical stage, T, N, and M classification) are displayed in the Supplementary Table 1.
3.3 An analysis of AIMP1 expression in tissue microarray and its prognostic value
To establish the correlation between the expression of AIMP1 and the clinicopathological parameters of LUAD patients, we performed IHC analysis on tissue chips, each of which contained 165 LUAD samples. Figure 4A and C illustrates that AIMP1 is expressed in the cell membrane and cytoplasm of cells. Moreover, the expression of AIMP1 was extremely elevated in tumor tissues than in adjacent normal tissues. Based on the staining index, we divided the samples into two groups: i) the group with a positive expression of AIMP1 and the group with a negative expression of AIMP1 (Figure 4C). Table 3 illustrates the correlation between the expression of AIMP1 and following clinicopathological parameters: disease stage (p = 0.028) and tumor differentiation (p = 0.003); however, there is no correlation of AIMP1 expression and age, sex, smoking history, and T or N classification. By performing continuous-variable (survival analysis), we conclusively proved that AIMP1 expression was associated with the OS of LUAD patients (p < 0.001) (Figure 4B). Univariate Cox analysis established that the OS of LUAD patients was correlated with the following clinical parameters: disease stage (HR= 1.257, p = 0.048), tumor size (HR= 1.441, p = 0.004), lymph node status (HR=1.328, p = 0.028), and AIMP1 expression (HR= 0.926, p = 0.019). As shown in forest map, tumor size (HR = 1.484, p = 0.037), and AIMP1 expression (HR = 0.922, p = 0.011) act as independent prognostic values. This finding was confirmed by the results of multivariate analysis (Figure 4D, E).
3.4 A relationship between the expression of AIMP1 and tumor-infiltrating immune cells
In this experiment, we investigated how AIMP1 gene elicits anti-tumor immunity. Furthermore, we tried to decipher the underlying mechanism through which AIMP1 leads to the pathogenesis of LUAD. Therefore, we explored the relationship between AIMP1 expression and the tumor immune microenvironment. After determining the median expression value of AIMP1 in 526 tumor samples, we divided them into two groups: group I consisted of 263 tumor samples that exhibited a high expression of AIMP1, whereas group II consisted of 263 tumor samples that exhibited a low expression of AIMP1. Using CIBERSORT software, we validated the differences between the abundance levels of immune cells in the different groups of LUAD patients. The different groups were formed on the basis of the mRNA expression patterns of AIMP1 (Figure 5A). In the group of LUAD patients with a high expression of AIMP1, the infiltration of different cells (memory B cells (p = 0.043), resting memory CD4 T cells (p = 0.001), follicular helper T cells (p =0.006), gamma delta T cells (p < 0.001), M2 macrophages (p = 0.008), activated dendritic cells (p < 0.001), and neutrophils (p < 0.001)) was significantly greater than that observed in the group of LUAD patients with a low expression of AIMP1. Compared to the group with a high expression of AIMP1, the group with a low expression of AIMP1 manifested a significantly greater level of infiltration of following cell types: naive B cells (p < 0.001), plasma cells (p < 0.001) and CD8+ T cells (p < 0.001). Thereafter, we evaluated the infiltration of immune cells in LUAD tumor samples. Then, we assumed possible correlations between 22 immune cells (Figure 5B, C). The resultant heatmap illustrated a strong correlation between the proportion of subsets of TIICs. For example, the resting mast cells showed the greatest positive correlation with the resting dendritic cells (Correlation = 0.45). Moreover, when the memory CD4+ T cells were activated, they showed a strong positive correlation with CD8 T cells (Correlation = 0.43). On the other hand, some memory CD4+ T cells were in the resting state, showing the greatest negative correlation with CD8 T cells (Correlation= -0.5). Finally, M2 macrophages were found to have a strong negative correlation with plasma cells (Correlation= –0.47).
The different types of tumors infiltrating lymphocytes (TILs) were identified in TIMER2.0 database. These cells were regulated by AIMP1 in all the types of TCGA tumors. As shown in Figure 5D, AIMP1 was positively correlated with the following cell types: CD8+ T cells, CD4+ T cells, and neutrophils. Moreover, AIMP1 was negatively correlated with the following cell types: B cells, cancer-associated fibroblasts, and regulatory T cells. When the anti-tumor immune cells were greater in number, they elicited a higher expression of AIMP1. These results indicated that the expression of AIMP1 affects the immune activity of the tumor immune microenvironment (TME).
3.5 An analysis of the differentially expressed genes (DEGs) with GO and KEGG pathway
In this study, we determined genetic variation and identified DEGs in tumors. Moreover, we classified the tumor tissues into two groups: i) group with a high mRNA expression of AIMP1 and ii) the group with a low mRNA expression of AIMP1. A volcano diagram containing 87 up-regulated genes and 300 down-regulated genes and the top 40 DEGs were shown in heatmap (Figure 6A, B). In fact, 40 genes were found to be closely related to the expression of AIMP1. Among them, the following genes were noteworthy: MYH11 HLA-DRB4, SLMO2, SPDL1, RAD51AP1, KIF23, KIF18A, ATAD2, NDUFAF7, PGGT1B and CENPE, etc (Figure 6C).
To further elucidate the relationship between the expression of AIMP1 and tumor immunity, we identified the genes that were differentially expressed. Moreover, we also identified the rich biological pathways, which were mediated by AIMP1 in the TME. Thereafter, the Cluster Profiler package in R software was used to analyze DEGs through functional and pathway enrichment analysis. The results of GO asnalysis indicate that the expression of AIMP1 was mainly associated with several immune-related biological functions, such as lymphocyte-mediated immunity, immunoglobulin and B-cell mediated immune response, humoral immune response, the regulation of complement activation, and the processing and presentation of peptide antigens via MHC Class II (Figure 6D). The results of KEGG pathway analysis indicate that the expression of AIMP1 is primarily related to the signaling pathways of autoimmune diseases, IgA-producing intestinal immune network, coronavirus disease (COVID-19), cell cycle, etc. (Figure 6E). Overall, we found that the expression of AIMP1 was closely associated with immunization. All these factors may contribute to the development of TME.
Thereafter, STRING database was used to construct a PPI network of differential genes associated with AIMP1. Figure 7A illustrates the interaction between 40 genes, which were visualized by using Cytoscape software [NATIONAL Institute of General Medical Sciences (NIGMS), Bethesda, Maryland, USA] (Figure 7B).
3.6 The differential expression of EGFR and KRAS in LUAD patients in high and low AIMP1 expression groups and their effects on OS in LUAD patients
In this experiment, we elucidated the relationship between the expression of AIMP1 and the LUAD driver genes EGFR and KRAS. Moreover, we also examined their effects on the OS of LUAD patients, which were segregated into two groups: i) group with a high expression of AIMP1 and ii) group with a low expression of AIMP1. To attain these objectives, the differential expression of EGFR and KRAS was determined in the two groups of LUAD patients with high and low expression of AIMP1, respectively. Then, we studied the effects of EGFR and KRAS genes on the OS of the two groups of LUAD patients with a high and low expression of AIMP1. The results indicate that compared to LUAD patients with a low expression of AIMP1, the expression of KRAS was significantly greater in LUAD patients with a high expression of AIMP1 (P=0.003; Figure 8A); however, the expression of EGFR was not significantly different in the two groups of LUAD patients (Figure 8B). Then, a survival curve of AIMP1 expression level was plotted against the expression levels of LUAD driver genes EGFR and KRAS. The results indicate that EGFR and KRAS showed no significant difference in the survival rate of the two groups of LUAD patients, that is, the one with a high expression of AIMP1 and the other with a low expression of AIMP1 (Figure 8C, D).