Immune profile of the tumor microenvironment and the identification of a four-gene signature for lung adenocarcinoma

Results

Tumor microenvironment score is associated with prognosis, age, gender, and TNM stage of LUAD patients

In order to identify the immune characteristics of the tumor microenvironment of LUAD and screen prognostic-related genes, an analysis process was established to show the screening process (Supplementary Figure 1). After analyzing the FPKM data from TCGA, we divided the immune and stromal cells in each sample into high-score and low-score groups. LUAD patients with a high overall score or immune score presented a longer survival time than did those with a low score (p<0.05) (Figure 1A, 1B). However, the survival time of patients with high stromal scores was not significantly different from that of patients with low stromal scores (p > 0.05) (Figure 1C). The overall score in tumor samples differed between ages < 65 and > 65 and varied by gender (Figure 1D). The overall score in T1 patients was different from that in obtained in T2, T3, and T4 patients, and the overall score in M0 patients was different from that in M1 patients (Figure 1D). Except for the significant difference in overall score observed between stage I and stage IV, there were no significant differences among other clinical stages (Figure 1D). The immune score in tumor samples was significantly different between ages < 65 and > 65 and varied by gender (Figure 1E). Immune scores in T1 patients were different from those in T2, T3, and T4 patients (Figure 1E). Except for the significant difference in immune score between stage I and stage III, there were no significant differences among other clinical stages (Figure 1E). The stromal score in tumor samples differed significantly between males and females and varied across different M stages (Figure 1F). Although the stromal score of stage IV patients was different from that of stage I and stage II patients, there were no significant differences among the other clinical stages (Figure 1F).

Figure 1. Correlation of tumor score with different clinical features. Survival analysis of patients with LUAD based on overall score (A), immune score (B), and stromal score (C). Effect of age, gender or tumor TNM stage on overall score (D), immune score (E), and stromal score (F).

Obtaining DEGs and performing enrichment analysis

The samples were divided into a high immune (or stromal) score group and a low immune (or stromal) score group. A total of 623 upregulated genes and 142 downregulated genes were screened from the high immune score group, and 673 upregulated genes and 112 downregulated genes were screened from high stromal score group (Figure 2A, 2B). We obtained 318 upregulated genes and 56 downregulated genes by taking the intersection of the DEGs in the two groups (Figure 2C, 2D). All 374 DEGs were further employed to conduct GO (Figure 2E, 2F) and KEGG (Figure 2G, 2H) enrichment analyses to elucidate their functions in tumorigenesis and progression. T cell activation, leukocyte proliferation and lymphocyte proliferation were the most enriched pathways, and most genes were related to immune- or stromal- cell activation and proliferation. These TME-related genes and cells are the primary causes of tumor growth and deterioration.

Figure 2. DEGs of high immune score (stromal score) and low score groups and functional enrichment analysis. Heatmap of significantly differentially expressed genes based on immune (A) and stromal (B) scores for LUAD. Venn diagram analysis of high (C) and low (D) expressed genes based on immune and stromal scores. (E) GO analysis of aberrantly expressed genes at the intersection of two groups. (F) CircleMap showing the functional interactions between pathways and genes as extracted from GO. (G) KEGG analysis of aberrantly expressed genes at the intersection of two groups. (H) CircleMap showing the functional interactions between pathways and genes as extracted from KEGG.

Screening of the most important DEGs

We first conducted PPI network analysis based on 374 DEGs using STRING (Figure 3A) and Cytoscape software (Figure 3B). A total of 371 nodes and 732 edges were identified from PPI network analysis of 374 immune-related DEGs (minimum required interaction score > 0.9). The top 30 proteins that had the maximum number of nodes in the PPI network are displayed in Figure 3C. We further conducted Univariate Cox regression analysis on 374 immune-related DEGs, and 98 prognostic genes were identified as risk factors for LUAD (Figure 3D). The four core target genes (CCR2, CCR4, P2RY12, and P2RY13) were screened out by taking the intersection of the top 30 genes and 98 candidate prognostic genes (Figure 3E).

Figure 3. Screening of differentially expressed genes based on protein-protein interaction (PPI) and Univariate Cox regression analysis. (A) PPI network of the aberrantly expressed genes based on STRING (interaction confidence value > 0.95). (B) Visualized PPI analysis of differentially expressed genes using Cytoscape. (C) Top 30 genes with maximum adjacent nodes. (D) Univariate Cox regression analysis for the aberrantly expressed genes. Genes with a p value less than 0.05 are shown in the forest plot. (E) Venn diagram of key genes in PPI and Cox regression analysis. Four TIIC-related genes (CCR2, CCR4, P2RY12, and P2RY13) were finally screened as prognostic factors of LUAD.

Expression levels and survival curve of the four target genes in LUAD

To verify the effect of these four genes on LUAD, we measured the expression levels of the four genes in tumor tissues and normal tissues. The expression levels of CCR2, P2RY12, and P2RY13 in tumor tissues were significantly lower than those observed in normal tissues, and the expression level of CCR4 did not differ between tumor tissues and normal tissues (Figure 4A). We further compared the expression levels of these four genes in paired tumor and adjacent normal tissues, which showed that the expression levels of CCR4, P2RY12, and P2RY13 in tumor tissues were significantly lower than those in adjacent normal tissues, and the expression level of CCR2 did not differ between paired tumor and adjacent normal tissues (Figure 4B). To further study the impact of these four genes on survival time, LUAD patients were divided into a high-expression group and a low-expression group based on the expression levels of the four genes. The results indicated that LUAD patients with high expression levels of the four genes had a better prognosis and higher survival time (Figure 4C). We also investigated the distribution of the expression of these four genes across different ages, genders and TNM stages. The CCR2 expression level in female LUAD patients or patients aged > 65 was higher than that in male LUAD patients or patients aged ≤ 65(Figure 4D) (p < 0.05). The CCR2 expression level in T1 patients was higher than that in T2 and T3 patients (Figure 4D) (p < 0.05). The CCR2 expression level in N0 patients was higher than that observed in N2 patients (Figure 4D) (p < 0.05). Except for the finding that the CCR2 expression levels in LUAD patients with stage I disease were higher than those in LUAD patients with stage III disease, no difference appeared between patients with other clinical stages of disease (Figure 4D) (p < 0.05). The CCR4 expression level in female LUAD patients or patients aged > 65 was higher than that in male LUAD patients or patients aged ≤ 65(Figure 4E) (p < 0.05). The CCR4 expression level in T1 patients was different from that in T2, T3, and T4 patients, and the CCR4 expression level in T2 patients was different from that in T3 patients (Figure 4E) (p < 0.05). The CCR4 expression level in LUAD patients with stage I disease was higher than that in patients with stage II, stage III, and stage IV disease (Figure 4E) (p < 0.05). The P2RY12 expression level in female LUAD patients or patients aged > 65 was higher than that in male LUAD patients and patients aged < 65(Figure 4F) (p < 0.05). Except for the finding that P2RY12 expression levels in T1 patients were higher than those in T2 and T3 patients, no difference was observed between patients with other clinical TNM stages (Figure 4F) (p < 0.05). The P2RY13 expression level in female LUAD patients or patients aged > 65 was higher than that in male LUAD patients or patients aged < 65(Figure 4G) (p < 0.05). P2RY13 expression levels in T1 patients were different from those in T2 and T3 patients (Figure 4G) (p < 0.05). Except for the finding that P2RY13 expression levels in LUAD patients with stage I disease were higher than in those with stage III disease, no differences between other clinical stages were observed in patients (Figure 4G) (p < 0.05).

Figure 4. Expression levels of the four genes (CCR2, CCR4, P2RY12, and P2RY13) and their prognostic value in LUAD patients. (A) The expression levels of the four genes in LUAD and normal tissues. (B) The levels of these four genes in paired tumor and adjacent normal tissues. (C) Survival curves of the expression of these four genes in the high-expression (red line) and low-expression (blue line) groups. The expression levels of CCR2 (D), CCR4 (E), P2RY12 (F), and P2RY12 (G) in patients with LUAD of different ages, genders and tumor TNM stages.

GSEA for the four genes

To research signaling pathways related to these four genes (CCR2, CCR4, P2RY12, and P2RY13), gene set enrichment analysis (GSEA) was performed to select significantly enriched signaling pathways according to NES, nominal p-value, FDR q-value and FWER p-value. The 10 most important signaling pathways enriched in highly expressed phenotypes of CCR2 were autoimmune disease, B cell receptor signaling pathway, cell adhesion molecules, chemokine signaling pathway, cytokine- cytokine interaction, hematopoietic cell lineage, Leishmania infection, NK cell-mediated cytotoxicity, T cell signaling pathway, and Toll-like receptor signaling pathway (Figure 5A). The 10 most important signaling pathways enriched in highly expressed phenotypes of CCR4 were the B cell receptor signaling pathway, cell adhesion molecules, cytokine- cytokine interaction, Fc epsilon RI signaling pathway, hematopoietic cell lineage, JAK-STAT signaling pathway, Leishmania infection, NK cell-mediated cytotoxicity, and T cell receptor signaling pathway (Figure 5B). The 10 most important signaling pathways enriched in highly expressed phenotypes of P2RY12 were autoimmune disease, cell adhesion molecules, chemokine signaling pathway, cytokine- cytokine interaction, hematopoietic cell lineage, intestinal immune network for IgA production, Leishmania infection, NK cell-mediated cytotoxicity, systemic lupus erythematosus, and viral myocarditis (Figure 5C). The 10 most important signaling pathways enriched in highly expressed phenotypes of P2RY13 were cell adhesion molecules, chemokine signaling pathway, cytokine- cytokine interaction, FcγR -mediated phagocytosis, hematopoietic cell lineage, JAK-STAT signaling pathway, Leishmania infection, NK cell-mediated cytotoxicity, T cell receptor signaling, and toll like receptor signaling pathway (Figure 5D).

Figure 5. Select GSEA plots of signatures for the four genes. (A) Enriched gene sets in KEGG collection by high expression of CCR2 (A), CCR4 (B), P2RY12 (C), and P2RY13 (D). Each line with a unique color represents one particular gene set. The upregulated genes are located on the left of the x-axis, and the downregulated genes are on the right. Only the gene sets with FDR q < 0.05, NOM p < 0.05, and FWER p < 0.05 are displayed. The top 10 leading gene sets are presented in the plot.

Distribution characteristics of immune cells in the TME of LUAD

To characterize the role played by immune cells in the progression of LUAD, the CIBERSORT algorithm was employed to estimate the differences in immune infiltration of 22 immune cell types in the TME. Figure 6A shows the landscape of the TME immune infiltration model, and every bar plot represents the proportion of 22 immune cells in each sample. Furthermore, the correlation matrix reflected the correlation of different TIICs, ranging from weak to strong, in LUAD (Figure 6B). As shown in the above analysis, CD4 memory resting T cells and CD8 T cells had a strong negative correlation (Cor = -0.44). CD8 T cells exhibited a positive correlation with CD4 memory activated T cells (Cor = 0.48).

Figure 6. CIBERSORT for estimating TIIC components in the LUAD microenvironment. (A) Stacked bar chart representing the component of TIICs in LUAD samples. (B) Correlation matrix of the different tumor-infiltrating immune cell proportions in LUAD.

Impact of the expression of the four genes on TIICs in LUAD

To further clarify the mechanism underlying the functions of the four previously discovered key genes (CCR2, CCR4, P2RY12, and P2RY13) in the tumor microenvironment, we divided LUAD patients into high-expression groups and low-expression groups according to the four gene expression levels. Our results indicated that memory B cells (p = 0.015), CD8 T cells (p = 0.021), CD4 memory resting T cells (p = 0.008), CD4 memory activated T cells (p < 0.001), monocytes (p < 0.001), M1 macrophages (p = 0.018), resting dendritic cells (p < 0.001), and resting mast cells (p = 0.013) were present in higher proportions in the high CCR2-expression group than in the others (Figure 7A). The relative proportions of follicular helper T cells (p = 0.038), gamma delta T cells (p = 0.002), M0 macrophages (p < 0.001), and activated mast cells (p = 0.002) were significantly upregulated in the low CCR2 group (Figure 7A). Naive B cells (p = 0.024), memory B cells (p < 0.001), CD8 T cells (p < 0.001), CD4 memory resting T cells (p < 0.001), CD4 memory activated T cells (p < 0.001), and M1 macrophages (p = 0.004) were present in higher proportions in the high CCR4-expression group than in the low CCR4-expression group, and gamma delta T cells (p = 0.015), activated NK cells (p < 0.001), M0 macrophages (p < 0.001), and M2 macrophages (p = 0.01) were significantly upregulated in the low CCR4 group (Figure 7B). Memory B cells (p < 0.001), CD4 memory resting T cells (p < 0.001), monocytes (p < 0.001), M2 macrophages (p < 0.001), resting dendritic cells (p < 0.001), resting mast cells (p < 0.001), and eosinophils (p = 0.009) were present in higher proportions in the high P2RY12-expression group than in the others (Figure 7C). The relative proportions of plasma cells (p < 0.001), follicular helper T cells (p = 0.029), gamma delta T cells (p = 0.019), activated NK cells (p = 0.023), M0 macrophages (p < 0.001), and activated mast cells (p < 0.001) were significantly upregulated in the low P2RY12 group (Figure 7C). Memory B cells (p<0.001), CD8 T cells (p=0.017), CD4 memory resting T cells (p = 0.03), CD4 memory activated T cells (p < 0.001), monocytes (p < 0.001), M1 macrophages (p = 0.003), resting dendritic cells (p < 0.001), resting mast cells (p = 0.005), and activated mast cells (p = 0.002) were present in higher proportions in the high P2RY13-expression group than in the low P2RY13-expression groups, and plasma cells (p < 0.001) and M0 macrophages (p < 0.001) were significantly upregulated in the low P2RY13 group (Figure 7D).

Figure 7. Effect of the four genes on TIIC levels in patients with LUAD. Violin plot indicating the ratio differentiation of 21 types of TIICs in high/low CCR2 (A), CCR4 (B), P2RY12 (C), and P2RY13 (D) expression relative to the median expression level.

Correlation analysis of the expression of the four genes with TIICs in LUAD

To further study the relationships among the four genes (CCR2, CCR4, P2RY12, and P2RY13) and immune cell infiltration, we investigated the correlations of the expression of these four genes with TIICs. The TIICs correlated with these four genes are presented in Figure 8. The results indicated that CCR2 was positively correlated with the infiltration of memory B cells, resting dendritic cells, eosinophils, M1 macrophages, monocytes, neutrophils, CD4 memory activated T cells, CD4 memory resting T cells, CD8 T cells, and gamma delta T cells but negatively correlated with the infiltration of activated dendritic cells, M0 macrophages, activated mast cells, activated NK cells, follicular helper T cells, and regulatory T cells (Figure 8A). CCR4 was positively correlated with the infiltration of memory B cells, naive B cells, resting dendritic cells, resting mast cells, CD4 memory activated T cells, CD4 memory resting T cells, and CD8 T cells but was negatively correlated with the infiltration of M0 macrophages, M2 macrophages, activated mast cells, activated NK cells, and follicular helper T cells (Figure 8B). P2RY12 was positively correlated with the infiltration of memory B cells, resting dendritic cells, eosinophils, M2 macrophages, resting mast cells, monocytes, CD4 memory resting T cells, and gamma delta T cells but was negatively correlated with the infiltration of M0 macrophages, activated mast cells, activated NK cells, resting NK cells, plasma cells, and regulatory T cells (Figure 8C). P2RY13 was positively correlated with the infiltration of memory B cells, resting dendritic cells, eosinophils, M1 macrophages, resting mast cells, monocytes, neutrophils, CD4 memory activated T cells, CD4 memory resting T cells, CD8 T cells, and gamma delta T cells but was negatively correlated with the infiltration of activated dendritic cells, M0 macrophages, activated mast cells, activated NK cells, and plasma cells (Figure 8D).

Figure 8. Correlation of the expression of the four genes with immune cell infiltration levels in patients with LUAD. (A) Scatter plot showing that 16 kinds of TIICs were correlated with the CCR2 expression (p < 0.05). (B) Scatter plot showing that 13 kinds of TIICs were correlated with CCR4 expression (p < 0.05). (C) Scatter plot showing 14 kinds of TIICs correlated with P2RY12 expression (p < 0.05). (D) Scatter plot showing 16 kinds of TIICs correlated with P2RY13 expression (p < 0.05).

Screening of immune cells most closely related to the expression of the four genes

To screen out immune cells that were most closely related to the expression of the four genes (CCR2, CCR4, P2RY12, and P2RY13), we took the intersection of immune cells differentially infiltrating in high-/low-expression groups and immune cells correlated with the expression of the four genes. Figure 9A indicates that 11 kinds of TIICs correlated with CCR2 expression, which was codetermined by difference and correlation analysis depicted in violin and scatter plots, respectively. Figure 9B indicates that nine kinds of TIICs correlated with CCR4 expression, as co-determined by difference and correlation analysis displayed in violin and scatter plots, respectively. Figure 9C indicates that 13 kinds of TIICs correlated with P2RY12 expression, as co-determined by difference and correlation analysis displayed in violin and scatter plots, respectively. Figure 9D indicates that 11 kinds of TIICs correlated with P2RY13 expression, as co-determined by difference and correlation analysis displayed in violin and scatter plots, respectively. In addition, we took the intersection of all the above TIICs, which indicated that the abundance of memory B cells, CD4 memory resting T cells, and M0 macrophages was jointly regulated by these four genes (Figure 9E).

Figure 9. Venn diagram analysis of aberrantly TIICs based on the difference analysis method and correlation analysis method. (A) Venn diagram indicating 11 kinds of TIICs correlated with CCR2 expression co-determined by difference and correlation analysis displayed in violin and scatter plots, respectively. (B) Venn diagram indicating 9 kinds of TIICs correlated with CCR4 expression co-determined by difference and correlation analysis displayed in violin and scatter plots, respectively. (C) Venn diagram indicating 13 kinds of TIICs correlated with P2RY12 expression co-determined by difference and correlation analysis displayed in violin and scatter plots, respectively. (D) Venn diagram indicating 11 kinds of TIICs correlated with P2RY13 expression co-determined by difference and correlation analysis displayed in violin and scatter plots, respectively. (E) Venn diagram indicating 3 kinds of TIICs that were co-related by these four genes.

Author Contributions

T.F. designed the study and conducted data analysis. M.Z. and N.S. performed data collection. T.F., L.W, Y.L. H.T., and F.T. drafted the manuscript. J.H. and C.L. revised and approval the paper.

Acknowledgments

We are very grateful to TCGA databases for providing valuable data resources to enable us to perform this study.

Conflicts of Interest

We declare no conflicts of interest.

Funding

This work was supported by the National Key R&D Program of China (2018YFC1312100), the National Natural Science Foundation of China (81972196), The CAMS Innovation Fund for Medical Sciences (CIFMS) (2017-I2M-1-005, 2019-I2M-2-002), The Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2018PT32033), The Innovation team development project of Ministry of Education (IRT_17R10).

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