Immune Cell Infiltration and Identifying Genes of Prognostic Value in the Papillary Renal Cell Carcinoma Microenvironment by Bioinformatics Analysis

Ting Liu, Man Zhang, Deming Sun, Ting Liu, Man Zhang, Deming Sun

Abstract

Papillary renal cell carcinoma (PRCC) is one of the most common histological subtypes of renal cell carcinoma. Type 1 and type 2 PRCC are reported to be clinically and biologically distinct. However, little is known about immune infiltration and the expression patterns of immune-related genes in these two histologic subtypes, thereby limiting the development of immunotherapy for PRCC. Thus, we analyzed the expression of 22 immune cells in type 1 and type 2 PRCC tissues by combining The Cancer Genome Atlas (TCGA) database with the ESTIMATE and CIBERSORT algorithms. Subsequently, we extracted a list of differentially expressed genes associated with the immune microenvironment. Multichip mRNA microarray data sets for PRCC were downloaded from the Gene Expression Omnibus (GEO) to further validate our findings. We found that the immune scores and stromal scores were associated with overall survival in patients with type 2 PRCC rather than type 1 PRCC. Tumor-infiltrating M1 and M2 macrophages could predict the clinical outcome by reflecting the host's immune capacity against type 2 PRCC. Furthermore, CCL19/CCR7, CXCL12/CXCR4, and CCL20/CCR6 were shown to be potential new targets for tumor gene therapy in type 2 PRCC. Our findings provide valuable resources for improving immunotherapy for PRCC.

Conflict of interest statement

The authors declare that there is no conflict of interest regarding the publication of this paper.

Copyright © 2020 Ting Liu et al.

Figures

Figure 1
Figure 1
Relationships between stromal/immune scores and prognosis in PRCC patients. (a) Effects of stromal scores and immune scores on overall survival rate in PRCC patients. Effects of stromal/immune scores on overall survival rate in (b) type 1 and (c) type 2 PRCC patients.
Figure 2
Figure 2
Relationship between stromal/immune scores and clinical characteristics. Associations between stromal/immune scores and distant metastasis, lymph nodes, clinical stage, and topography in (a) type 1 PRCC patients and (b) type 2 PRCC patients. M: distant metastasis; N: lymph node; T: topography.
Figure 3
Figure 3
Profiles of infiltrating immune cells in normal and PRCC tissues. (a) Heat map based on proportions of 22 immune cell types in normal, type 1, and type 2 PRCC tissues. (b) Violin plot showing differences in the expression of 22 immune cell types in normal (blue), type 1 (red), and type 2 (green) PRCC groups. (c) PCA analyses indicating group-biased clustering of immune cells from 76 GEO cases and 95 TCGA cases. (d) Correlation analysis based on 22 immune cell subpopulations. ∗P < 0.05, ∗∗P < 0.01, and ∗∗∗P < 0.001.
Figure 4
Figure 4
Kaplan–Meier survival curves obtained to investigate the impacts of key immune cell types on overall survival in type 1 and type 2 PRCC patients. A P value < 0.05 was considered to indicate a significant difference based on the log-rank test.
Figure 5
Figure 5
Relationships between survival-related immune cells and clinicopathological characteristics in patients with type 2 PRCC. (a) Associations between M1 macrophages and clinical stage, topography, lymph nodes, and distant metastasis. (b) Associations between M2 macrophages and clinicopathological characteristics. T: topography; N: lymph node; M: distant metastasis.
Figure 6
Figure 6
Functional enrichment analysis and selection of key modules for DEGs in type 2 PRCC tissues. (a) Overlapping genes identified in TCGA and GEO databases. Significant (b) GO terms and (c) KEGG pathways for the upregulated genes identified in the high immune score group of type 2 PRCC. (d) Three meaningful modules in the PPI network. (e) Kaplan–Meier curves were prepared according to the high and low expression levels of core genes in type 2 PRCC.

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