Prognostic value and immune infiltration of novel signatures in clear cell renal cell carcinoma microenvironment

Abstract

Growing evidence has highlighted the immune response as an important feature of carcinogenesis and therapeutic efficacy in clear cell renal cell carcinoma (ccRCC). This study categorized ccRCC cases into high and low score groups based on their immune/stromal scores generated by the ESTIMATE algorithm, and identified an association between these scores and prognosis. Differentially expressed tumor environment (TME)-related genes extracted from common upregulated components in immune and stromal scores were described using functional annotations and protein-protein interaction (PPI) networks. Most PPIs were selected for further prognostic investigation. Many additional previously neglected signatures, including AGPAT9, AQP7, HMGCS2, KLF15, MLXIPL, PPARGC1A, exhibited significant prognostic potential. In addition, multivariate Cox analysis indicated that MIXIPL and PPARGC1A were the most significant prognostic signatures, and were closely related to immune infiltration in TCGA cohort. External prognostic validation of MIXIPL and PPARGC1A was undertaken in 380 ccRCC cases from a real-world cohort. These findings indicate the relevance of monitoring and manipulation of the microenvironment for ccRCC prognosis and precision immunotherapy.

Keywords: ESTIMATE algorithm; clear cell renal cell carcinoma; immune signature; prognosis; tumor microenvironment.

Figure 1

Association between immune/stromal/Estimate score and prognosis in TCGA after ESTIMATE algorithm processed. (A–B) Estimate score was significantly associated with higher ISUP grade and AJCC stage (p<0.001). The highest Estimate score was found in the most progressive clinicopathological stage, G4 and stage IV. (C) Survival curves indicated that elevated immune score significantly correlated with poor overall survival in 533 ccRCC patients (p=0.001, 1165 vs. 1217 days). (D) Increased stromal score significantly associated with shorter OS (p=0.002, 1117.5 vs. 1230 days). (E) Significant Estimate score also predict significant OS for ccRCC patients (p=0.003, 1172.5 vs. 1223.5 days).

Figure 2

Differential expressed genes with immune and stromal score and related functional annotations in ccRCC. (A) Based on immune score comparison, 162 genes were up-regulated and 747 genes down-regulated in the high score than the low score group after propensity analysis using limma package algorithm. (B) Similarly, for high stromal score compared with low score, 261 up-regulated genes and 1198 down-regulated genes were obtained. (C–D) A total of 77 DEGs were commonly upregulated in the high scores groups, and 787 genes were synchronously downregulated using Venn algorithm. (E) functional enrichment analysis including GO: BP, GO: CC, GO: MF and KEGG pathways, was performed in 864 commonly DEGs. (F) Cluster analysis and heat map including 77 up-regulated DEGs suggested distinct mRNA expression profiles of DEGs in 533 ccRCC samples.

Figure 3

Significant modular analysis and function enrichment analysis based on PPI network. (A) PPI network was constructed using a total of 77 commonly up-regulated DEGs. MCODE, plug-in of Cytoscape, was used to detect most significant co-regulated modular. The most significant modular including AGPAT9, AQP7, HMGCS2, KLF15, MLXIPL and PPARGC1A, was marked in yellow. (B) functional annotations indicated that 77 DEGs were mostly involved in carbohydrate digestion and absorption, fatty acid transmembrane transport activity, PPAP signaling pathway, response to methionine, insulin resistance, water channel activity, enamel mineralization, negative regulation of mitochondrial fission, etc.

Figure 4

Survival analysis of significant DEGs in 533 ccRCC from TCGA database. Among 6 significant hub genes, significantly decreased AGPAT9, AQP7, HMGCS2, KLF15, PPARGC1A mRNA expressions were found in ccRCC tissues compared with adjacent normal tissues, while MLXIPL mRNA expression was significantly elevated in tumor samples compared with normal samples. Kaplan-Meier method indicated that decreased AGPAT9, AQP7, HMGCS2, KLF15, PPARGC1A mRNA expression significantly correlated with poor OS (p<0.001), and elevated MLXIPL mRNA expression was significantly associated with shorter OS for ccRCC patients (p=0.012).

Figure 5

Prognostic validation of MLXIPL and PPARGC1A in FUSCC cohort. (A–B) To validate AQP9 mRNA expression profile in ccRCC tissues, we performed RT-qPCR using 380 paired tumor and normal samples with available clinical follow-up data from a real-world cohort. It revealed dramatically increased MLXIPL and decreased PPARGC1A mRNA expression in ccRCC samples than normal tissues. (C–F) Survival curves suggested that patients with elevated MLXIPL and decreased PPARGC1A mRNA levels significantly correlated with poorer PFS and OS (p<0.001).

Figure 6

ROC curves were generated to validate the ability of the logistic model to predict prognosis. After integrating all the significant clinicopathological parameters and gene expression profiles in the multivariate Cox regression models of FUSCC cohort, we generated the formulas for MLXIPL and PPARGC1A to predict prognosis in FUSCC cohort, and validated prognostic ability in TCGA cohort.

Figure 7

Immune infiltration of MLXIPL and PPARGC1A. After identifying prognostic value of MLXIPL and PPARGC1A, we performed correlation analysis between MLXIPL and PPARGC1A and immune infiltration level for ccRCC. Scatter plots were generated with partial Spearman's correlation and statistical significance. MLXIPL and PPARGC1A expression were significantly associated purity (correlation=0.207 and 0.287, respectively). In addition, elevated MLXIPL and PPARGC1A significantly correlated with B cell, CD8+ T cell, macrophage, neotrophil, and dendritic cell infiltration (p<0.05), prompting a general decline in immune infiltration level.