The renal clear cell carcinoma immune landscape

Abstract

A comprehensive evaluation of the clear cell renal cell carcinoma (ccRCC) immune landscape was found using 584 RNA-sequencing datasets from The Cancer Genome Atlas (TCGA), we identified 17 key dysregulated immune-associated genes in ccRCC based on association with clinical variables and important immune pathways. Of the numerous findings from our analyses, we found that several of the 17 key dysregulated genes are heavily involved in interleukin and NF-kB signaling and that somatic copy number alteration (SCNA) hotspots may be causally associated with gene dysregulation. More importantly, we also found that key immune-associated genes and pathways are strongly upregulated in ccRCC. Our study may lend novel insights into the clinical implications of immune dysregulation in ccRCC and suggests potential immunotherapeutic targets for further evaluation.

Keywords: Renal clear cell carcinoma; TCGA.

Fig. 1

Summary of study workflow and differential expression results. (A) Schematic of data processing procedures and algorithms utilized. (B) Heatmap of all IA genes differentially expressed when comparing ccRCC samples to normal samples. All genes correlate with patient survival (see Figure 2). In order from low to high expression, the heatmap displays dark blue, light blue, light red, and dark red. Asterisks next to gene names denote downregulated IA genes in ccRCC. (C) Volcano plot of differential expression significance vs. fold change in ccRCC samples vs. normal samples. All dots on the volcano plot represent survival-associated IA genes with significant fold change (|FC|<2) and corrected p-value (p<0.05). Selected 17 IA genes of interest in this study are labeled in red. (D) Schematic highlighting the interactions between selected IA genes of interest and key immune cells, processes, and pathways.

Fig. 2

Correlations of immune-associated gene expression with clinical variables and survival in ccRCC patients. Boxplots of IA gene expression vs. (A) cancer presence after treatment, (B) pathologic M stage, (C) pathologic N stage, (D) pathologic T stage, and (E) histologic grade (Kruskal-Wallis, p<0.05). (F) Kaplan-Meier survival plots for 17 selected IA genes (Cox regression, p<0.05).

Fig. 3

Genomic alterations most associated with dysregulation of each IA gene. REVEALER plots of amplifications, deletions, or mutations that have the highest correlation with gene expression for (A) IA genes upregulated in ccRCC and (B) IA genes downregulated in ccRCC, with the significance threshold at |CIC| > 0.3. (C) Bar graphs of Spearman correlation coefficient of IA gene expression with expression of genes within SCNA regions seen in panels (A) and (B). Only correlations with correlation coefficient > 0.4 or <-0.4 are presented. (D) Circos plot visualizing chromosomal locations of correlations presented in panels (A) and (B). IA gene names are listed on the green circle, while SCNA regions are listed on the yellow circle. Blue lines connecting IA gene names to SCNA region names indicate that deletion of the regions correlate with dysregulation of the IA gene, while red lines indicate that amplification of the regions correlate with IA gene dysregulation. (E) Scatter plots of most significant direct (positive) correlations from panel (C). (F) Scatter plots of most significant inverse (negative) correlations from panel (C).

Fig. 4

Correlations between IA gene expression and regulatory miRNA expressions. (A) Plot of interactions between dysregulated IA genes and targeting miRNAs. (B) A Circos plot depicts the potential interactions between dysregulated miRNAs and dysregulated IA genes with their relative positions in the genome. Potential interactions are defined as interactions forming the leading edge subsets of each GSEA plot. (C) Sample gene set enrichment analysis plots indicate negative enrichment of miRNA expression in relation to expression of IA genes IFNG, IRF6, and OSM. All lines connecting to a single IA gene are of the same color in panels (A) and (B).

Fig. 5

Gene set-scale analysis of IA dysregulation. GSEA was used to associate the expression of (A) JAK3, (B) TNFSF14, (C) IFNG, and (D) OSM to expressions of genes in immunologic signatures (p<0.05). Bar graphs were plotted of GSEA enrichment result using fold change of gene expression in ccRCC vs. normal samples, against (E) cancer-related signatures and (F) canonical pathways as gene sets (FDR<0.1). Red bars indicate correlation with negative fold change for signatures of downregulated genes after knockdown of cancer-related genes in panel (E), while blue bars indicate correlation with negative fold change for signatures of downregulated genes after upregulation of cancer-related genes. Red bars indicate negative enrichment against fold change, and blue bars indicate positive enrichment against fold change in panel (F)