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. 2021 Oct 15;13(10):11048–11064.

TNFSF13B and PPARGC1A expression is associated with tumor-infiltrating immune cell abundance and prognosis in clear cell renal cell carcinoma

Tianming Ma 1,2, Lingfeng Meng 1,2, Xiaonan Wang 2,3, Zijian Tian 1,2, Jiawen Wang 1, Xiaodong Liu 1,2, Wei Zhang 1, Yaoguang Zhang 1,2
PMCID: PMC8581857  PMID: 34786042

Abstract

Growing evidence suggests that the tumor microenvironment (TME) plays crucial roles in tumor progression and treatment efficacy in clear cell renal cell carcinoma (ccRCC), which typically has a poor prognosis due to high relapse and metastasis rates. We comprehensively analyzed ccRCC RNA-sequencing data from The Cancer Genome Atlas (TCGA) database to identify candidate prognostic TME-related genes involved in ccRCC. We used the ESTIMATE and CIBERSORT algorithms to estimate the proportions of immune cells, stromal cells, and tumor-infiltrating immune cells (TICs) in the TME in ccRCC samples from 539 patients. By examining the intersection of the differentially expressed genes (DEGs) obtained by Cox regression analysis and protein-protein interaction network, we identified five overlapping DEGs (IGLL5, MZB1, HSD11B1, TNFSF13B, and PPARGC1A). Further analysis revealed that TNFSF13B expression was elevated in ccRCC tumor tissues and negatively associated with overall survival. PPARGC1A expression exhibited the opposite patterns. Immunohistochemical analysis of 35 paired ccRCC and adjacent normal tissues confirmed the in-silico results. Gene set enrichment analysis revealed that genes in the groups with high TNFSF13B and PPARGC1A expression were enriched mainly in immune-related activities. In the group with low PPARGC1A expression, genes were enriched in metabolic pathways. CIBERSORT analysis of TIC proportions revealed that Tregs and CD8 T-cell abundance correlated positively with TNFSF13B expression, but negatively with PPARGC1A expression. These findings demonstrate that TNFSF13B and PPARGC1A are prognostic predictors and possible therapeutic targets in ccRCC.

Keywords: TNFSF13B, PPARGC1A, tumor microenvironment, tumor-infiltrating immune cell, clear cell renal cell carcinoma

Introduction

Kidney cancer is a common type of cancer with a high mortality rate and a steadily increasing morbidity rate worldwide [1]. In 2020, there were ca. 73,750 new cases of kidney cancer and ca. 14,830 deaths due to kidney cancer in the USA [2]. Renal cell carcinoma (RCC) is the major histological type, accounting for approximately 90% of all kidney cancers. Clear cell renal cell cancer (ccRCC) is the most common subtype. Although surgery is effective for early stage ccRCC, early diagnosis is difficult. Relapse or metastasis occur in 30% of cases after curative treatment. Unfortunately, the prognosis of patients with metastasis is generally poor because ccRCC is insensitive to conventional radiotherapy and chemotherapy [3]. It is imperative to elucidate the mechanisms underlying the development of ccRCC.

Recent studies have reported the significance of the tumor microenvironment (TME) in cancer development and progression [4,5]. Resident stromal cells and recruited immune cells play important roles in the TME and contribute to tumor progression. The precise mechanisms remain unclear [6]. The roles of tumor-infiltrating immune cells (TICs) in the TME in tumorigenesis and immunotherapy have been studied [7,8]. TIC abundance is known to be significantly correlated with outcomes in ccRCC [9,10].

The ESTIMATE and CIBERSORT algorithms have been widely used to estimate the contributions of immune and stromal cells to TICs in the TMEs of various cancers, including ccRCC [11-13]. We used these tools to examine the proportions of TICs, immune cells, and stromal cells in ccRCC tissues. We investigated the expression of TME-related genes and their associations with clinical prognosis and TICs using data from The Cancer Genome Atlas (TCGA) database and our hospital.

Materials and methods

Data sources

We collected transcriptome RNA-sequencing data from tumor tissues and corresponding clinical data from 539 ccRCC patients, along with data from 72 adjacent non-tumor samples, from TCGA. The ESTIMATE algorithm was used to calculate immune, stromal, and ESTIMATE scores for each sample. These scores represent the ratios of immune-stromal components and their total proportions in the TME. Xie et al. collected gene expression profiling data and clinical follow-up information from 629 ccRCC cases from the Gene Expression Omnibus database (datasets GSE29609, GSE22541, and GSE3) and TCGA to develop the web tool OSkirc (http://bioinfo.henu.edu.cn/KIRC/KIRCList.jsp), which we used to validate our results [14]. Further, 35 patients with ccRCC who underwent surgical treatment in Beijing Hospital between January 2013 and December 2020 were enrolled in this study for immunohistochemical analysis of their tissue samples. This research was approved by the Research Ethics Committee of Beijing Hospital (approval number: 2021BJYYEC-161-01). Informed consent was acquired from all patients involved.

Analysis of the associations between ESTIMATE scores, overall survival (OS), and clinicopathology

The tumor samples from TCGA were divided into high- and low-immune-, stromal-, and ESTIMATE-score groups based on the median scores. Kaplan-Meier curves were generated to compare OS of ccRCC patients between the groups. The Wilcoxon rank-sum test or Kruskal-Wallis rank sum test was used to evaluate the relationship between the scores and clinicopathological parameters.

Differentially expressed genes (DEGs) identification

We used the Wilcoxon rank-sum test to identify DEGs between the high- and low-score groups based on a |log foldchange| >1 and a false discovery rate (FDR) <0.05. Heatmaps of the DEGs were created using the R package pheatmap.

DEGs enrichment analysis

The functions of the DEGs identified were elucidated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses using the R packages clusterProfiler, enrichplot, and ggplot2. Terms with P<0.05 and q<0.05 were considered significantly enriched.

Protein-protein interaction (PPI) network and Cox regression analyses

A PPI network of the DEGs was established using the STRING database, set to 0.4, and was reconstructed using Cytoscape v. 3.6.1. Univariate Cox regression analysis was used to identify prognostic genes among the DEGs. We integrated the results of these two analyses to determine hub genes.

Analysis of the associations between hub gene expression, OS, and clinicopathology

The R package limma was used to visualize the differential expression of the hub genes in the normal and ccRCC tissue samples from TCGA. The samples were classified into low- or high-expression groups for each hub gene based on the median expression. Survival analysis was used to compare OS between the two groups. The Wilcoxon rank-sum test was used to determine the relationship between hub gene expression and clinicopathological parameters.

Gene set enrichment analysis (GSEA) of the hub genes

We conducted GSEA with the C2 KEGG v. 7.1 and C7 v. 6.2 gene sets using GSEA v. 4.0.3. A nominal P and FDR q<0.05 indicated significance.

Analysis of the correlations between hub gene expression and the proportion of TICs

We used the CIBERSORT algorithm (downloaded from https://cibersortx.stanford.edu/) to estimate the proportion of TICs. Only those tumor samples with P<0.05 in terms of quality filtering were retained to evaluate the correlations between TICs and hub genes.

Statistical analysis

All analyses were performed using R v.4.0.3 (http://www.R-project.org). The Wilcoxon rank-sum test was used to compare gene expression between tumor and normal tissues and subgroups based on clinicopathological parameters. The Kruskal-Wallis test was used for comparisons of more than two groups. Kaplan-Meier analysis and the log rank test were used to assess OS in the different groups. Univariate Cox regression analysis was used to screen out prognostic genes among the DEGs. The Wilcoxon rank-sum test was used to compare the difference of TICs between high- and low- expression groups of hub genes. Spearman correlation analysis was used to investigate the correlations between TICs and hub genes. Statistical significance was set at P<0.05.

Results

The ESTIMATE immune score is negatively correlated with OS

Tumor samples were classified into high- and low-score groups based on the median ESTIMATE scores. According to Kaplan-Meier curves, patients with a high immune score had a more damaged OS than those with a low immune score (P=0.033, Figure 1A). OS did not significantly differ between the low- and high-score groups for the stromal or ESTIMATE scores (Figure 1B, 1C). This indicates that the immune component of the TME is a better indicator of ccRCC prognosis than the stromal component.

Figure 1.

Figure 1

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Correlation between ESTIMATE scores and the OS of ccRCC patients. Kaplan-Meier survival analysis of ccRCC patients grouped by (A) immune score, (B) stromal score, and (C) ESTIMATE score.

The ESTIMATE immune score correlates with clinicopathology

ccRCC patients aged ≤65 years had higher stromal scores than older ccRCC patients (P=0.0064, Wilcoxon rank-sum test) (Figure 2G). The immune and ESTIMATE scores did not significantly differ between older and younger patients (Figure 2A, 2M). The immune and ESTIMATE scores increased with pathologic grade (G1 vs. G4, G2 vs. G3, G2 vs. G4, G3 vs. G4, all P<0.05; Figure 2B, 2N). Similar results were obtained for clinical stage (stage I vs. stage III, stage I vs. stage IV, P<0.05; Figure 2C, 2O). The immune and ESTIMATE scores were both higher in T3 than in T1 (P<0.05; Figure 2D, 2P) and in M1 than in M0 (P<0.05; Figure 2F, 2R). None of the three scores were significantly correlated with N stage (Figure 2E, 2K, 2Q). These findings provide evidence that the immune and stromal components affect the progress of ccRCC.

Figure 2.

Figure 2

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Relationship between scores and clinicopathological characteristics of ccRCC patients. (A-F) Immune scores according to age, pathological grade, clinical stage, and TNM stage. (G-L) Stromal and (M-R) ESTIMATE scores according to clinicopathology.

Detection and enrichment of DEGs

The Wilcoxon rank-sum test was used to assess the significance of differential gene expression between the immune and stromal score groups. Notably, 656 DEGs were obtained from immune score (samples with high score vs. low score). Among them, 510 genes were upregulated, and 146 genes were downregulated (Figure 3A, 3C, 3D). There were 411 DEGs in the high-stromal-score vs. low-stromal-score group (259 upregulated and 152 downregulated, respectively) (Figure 3B-D). A Venn diagram revealed 93 overlapping DEGs between the stromal and immune groups (44 upregulated and 49 downregulated), which may play important roles in the TME. GO and KEGG enrichment analyses were used to elucidate the roles of these 93 DEGs. Most were associated with immune-related activities, including leukocyte proliferation and cytokine-cytokine receptor interactions (Figure 3E, 3F).

Figure 3.

Figure 3

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Analysis of DEGs. Heatmaps for DEGs generated by comparison of the high score group vs. the low score group in (A) immune and (B) stromal scores. (C, D) Venn diagrams showing common upregulated and downregulated DEGs associated with immune and stromal scores. (E, F) GO and KEGG enrichment analysis of 93 DEGs.

PPI network and prognostic genes

To elucidate the relationship among the DEGs, we established a PPI network, which comprised 52 DEGs and 93 edges (Figure 4A). Figure 4B shows the top 30 hub genes, ranked according to their connectedness in the PPI network (i.e., the number of nodes they connect to). Cox regression analysis identified 23 prognostic DEGs among the 93 overlapping DEGs (Figure 4C). By integrating the results of these two analyses, we found five genes related to prognosis: IGLL5, MZB1, HSD11B1, TNFSF13B, and PPARGC1A (Figure 4D).

Figure 4.

Figure 4

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PPI network and determination of hub genes. A. PPI network, the confidence threshold was set to 0.4. B. Bargraph showing the top 30 hub genes, ranked according to their connectedness in the PPI network (i.e., the number of nodes they are connected with). C. Forest plot of the 23 prognostic genes generated by univariate Cox analysis with P<0.05. D. Venn diagram showing the common hub genes shared by leading 30 nodes in PPI and significant factors in univariate Cox.

TNFSF13B and PPARGC1A expression correlates with prognosis

The expression of the five hub genes differed significantly between normal and ccRCC tissues. The ccRCC samples were classified into high- and low-expression groups based on the median expression of the five hub genes. Among these genes, TNFSF13B and PPARGC1A showed the largest expression differences between the normal and ccRCC samples and the strongest association with OS. Elevated PPARGC1A expression was associated with improved OS in ccRCC patients (Figure 5). These two genes were selected for further comprehensive analysis.

Figure 5.

Figure 5

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Expression of TME-related candidate prognostic genes in ccRCC tissues and associations between gene expression and OS in ccRCC patients. A-E. Differential expression of IGLL5, MZB1, HSD11B1, TNFSF13B, and PPARGC1A between normal and tumor samples. F-J. Paired differentiation analysis of hub genes. K-O. OS of ccRCC patients according to high or low hub gene expression.

TNFSF13B and PPARGC1A protein expression in ccRCC was evaluated by immunohistochemistry using 35 paired ccRCC and adjacent normal tissues from patients treated in our center. The protein expression data were consistent with the mRNA expression data from TCGA (i.e., high TNFSF13B expression in ccRCC tissues, but low TNFSF13B expression in adjacent normal tissues, and high PPARGC1A expression in adjacent normal tissues, but low PPARGC1A expression in ccRCC tissues) (Figure 6). The Kaplan-Meier analysis in OSkirc showed that elevated TNFSF13B expression was closely related to worsened OS in ccRCC patients (P<0.001, Figure 7A). Elevated PPARGC1A expression was associated with improved OS in ccRCC patients (P<0.001, Figure 7B). These findings were consistent with our results.

Figure 6.

Figure 6

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Immunohistochemical staining of TNFSF13B and PPARGC1A in ccRCC tumor and adjacent normal tissues. A. High TNFSF13B expression in ccRCC tissues; B. Low TNFSF13B expression in ccRCC tissues; C. Low TNFSF13B expression in adjacent normal tissues; D. High PPARGC1A expression in adjacent normal tissues; E. Low PPARGC1A expression in adjacent normal tissues; F. Low PPARGC1A expression in ccRCC tissues. Scale bar: 100 μm. Magnification: 200×.

Figure 7.

Figure 7

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Kaplan-Meier analysis of patients with high and low expression of TNFSF13B (A) and PPARGC1A (B) in the OSkirc web tool.

Correlations between hub gene expression and clinicopathological parameters of ccRCC patients

We found that TNFSF13B expression increased with increasing grade, clinical stage, and TNM stage (Figure 8A-E). PPARGC1A expression decreased with increasing grade, clinical stage, and T and M stages, but not N stage (Figure 8F-J). These findings suggest that TNFSF13B and PPARGC1A may influence disease progression and patient survival in ccRCC.

Figure 8.

Figure 8

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Clinicopathologic significance of TNFSF13B and PPARGC1A expression in ccRCC tissues. Influence of TNFSF13B expression on (A) pathological grade, (B) clinical stage, and (C-E) TNM stages. Influence of PPARGC1A expression on (F) pathological grade, (G) clinical stage, and (H-J) TNM stages.

TNFSF13B and PPARGC1A functional enrichment analyses

To determine the functions of TNFSF13B and PPARGC1A in ccRCC, GSEA was applied to the high- and low-expression groups compared with the median level of these two genes expression, respectively. Samples with high expression of these two genes were enriched in genes related to cell adhesion- and immune-related activities, including extracellular matrix-receptor interaction, focal adhesion, and B-cell and T-cell receptor signaling pathways, among the C2 KEGG and C7 immune gene sets (Figure 9A, 9C, 9D, 9F). Samples with low PPARGC1A expression were enriched in genes involved in metabolic pathways, including PPAR signaling and leucine and isoleucine degradation (Figure 9E). For C7 immune gene sets, based on the criterion FDR q<0.5, there were no gene sets enriched in the low expression group of two hub genes.

Figure 9.

Figure 9

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GSEA of TNFSF13B and PPARGC1A. Multi-GSEA enrichment curves for the C2 KEGG collection for (A, D) high TNFSF13B and PPARGC1A expression and (B, E) low TNFSF13B and PPARGC1A expression. (C, F) Multi-GSEA enrichment curves for the C7 collection for high TNFSF13B and PPARGC1A expression.

TNFSF13B and PPARGC1A expression regulates TME immune activity

We used the CIBERSORT algorithm to estimate the number of TIC subsets among the ccRCC samples. There were 22 distinct TIC profiles (Figure 10). By combining difference and correlation analyses, we found that 11 types of TICs were correlated with TNFSF13B expression: naive B cells, activated dendritic cells, eosinophils, resting mast cells, resting NK cells, activated memory CD4 T cells, resting memory CD4 T cells, CD8 T cells, follicular helper T cells, gamma delta T cells, and regulatory T cells (Tregs) (Figure 11B-E, 11H, 11J-P); most of the associations were positive. Similarly, 10 types of TICs were correlated with PPARGC1A expression (Figure 12C, 12E-I, 12K-O). Of these, Tregs showed the most significant correlation. These findings suggested that both TNFSF13B and PPARGC1A have immunomodulatory roles in the TME.

Figure 10.

Figure 10

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Distribution of TICs in ccRCC tumor samples. A. Proportions of 22 types of TICs in ccRCC samples. B. The heatmap displays the correlation among 22 types of TICs.

Figure 11.

Figure 11

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Correlations between TNFSF13B expression and TICs. A. Proportions of 22 TIC types in ccRCC samples with differential expression of TNFSF13B. B-O. Associations between the proportions of 14 TIC types and TNFSF13B expression. P. Venn diagram illustrating 11 types of common TICs correlated with TNFSF13B expression codetermined by difference and correlation tests.

Figure 12.

Figure 12

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Correlation between PPARGC1A expression and TICs. A. Proportions of 22 TIC types in ccRCC samples with differential PPARGC1A expression. B-N. Associations between the proportions of 13 TIC types and PPARGC1A expression. O. Venn diagram illustrating 10 common types of TICs correlated with PPARGC1A expression codetermined by difference and correlation tests.

Discussion

RCC tumors are complex and heterogeneous tumors with high recurrence and metastasis rates. The prognosis of RCC patients is usually dismal [15]. For a number of years, tyrosine kinase inhibitors, which inhibit the vascular endothelial growth factor receptor, have been used to treat metastatic RCC [16]. Unfortunately, because of drug resistance, only a small proportion of patients benefit from tyrosine kinase inhibitors [17]. TICs have been suggested to impact resistance to tyrosine kinase inhibitors in the TME [10,18].

There is increasing evidence of the clinical significance of the TME in predicting tumorigenesis, progression, prognosis, and therapeutic efficacy, in which TICs in the TME play important roles [4-10]. Recently, immune checkpoint inhibitors have been recommended as a first-line treatment for metastatic RCC [19,20]. The TME is actively involved in the response to immune checkpoint inhibitors [21]. By conducting a comprehensive ccRCC RNA-sequencing data analysis using TCGA data, we identified five TME-related genes (IGLL5, MZB1, HSD11B1, TNFSF13B, and PPARGC1A) that may have prognostic value in ccRCC. We elucidated the prognostic significance of TNFSF13B and PPARGC1A expression and screened immune cells associated with the expression of these two genes. The findings suggested TNFSF13B and PPARGC1A be prognostic markers and novel therapeutic targets in ccRCC.

TNFSF13B, also known as BAFF, encodes tumor necrosis factor ligand superfamily member 13B, which regulates B-cell differentiation and survival, and T-cell function [22,23]. Extensive evidence suggesteds it plays a prominent role in immune system diseases [24,25]. Its function in glioma and breast tumors has been examined [26,27]. In ccRCC, TNFSF13B expression is positively correlated with tumor stage [28]. Consistent with findings in previous studies [12,28], the current study revealed that elevated TNFSF13B expression in ccRCC was significantly positively associated with histological grade, clinical stage, TNM stage, and with poor prognosis. We attempted to further elucidate its role in ccRCC using GSEA, which revealed that TNFSF13B upregulation was associated with immune-related signaling pathways, such as the B-cell and macrophage receptor signaling pathways. Stelmach et al. identified seven alternative TNFSF13B transcripts in T-cell and monocyte transcriptomes and found that TNFSF13B is regulated through a post-transcriptional mechanism [23]. It is worth examining the effect of TNFSF13B on TICs further.

PPARGC1A, also known as PGC-1α, encodes peroxisome proliferator-activated receptor gamma coactivator-1, a key transcriptional coactivator that coordinates mitochondrial biogenesis and oxidative phosphorylation in tumor cells to induce metastasis [29]. Decreased PPARGC1A expression has been related to acute or chronic kidney injury and diabetes [30]. The oncological significance of PPARGC1A expression in tumors remain controversial. Upregulated PPARGC1A expression has been shown to promote metastasis in patients with lung cancer [31]. Similar results have been reported in patients with invasive breast cancer [29]. Consistent with our findings, in ccRCC, PPARGC1A expression reportedly was significantly lower in tumor tissues than in normal tissues. High PPARGC1A mRNA levels were associated with good OS [32]. We observed negative correlations between PPARGC1A expression and tumor grade, clinical stage, and M stage in patients with ccRCC. To examine the reasons for these conflicting findings, we used GSEA to identify the affected pathways. In the PPARGC1A high expression group, immune-related signaling and epithelial-mesenchymal transition pathways were the most enriched. In the PPARGC1A low expression, metabolic pathways, including PPAR signaling and leucine and isoleucine degradation, were highly enriched. All these pathways are associated with oncogenesis. Based on these findings, we speculate that PPARGC1A is involved in shifting the TME from immune-dominant to metabolic-dominant and therefore, PPARGC1A downregulation is associated with the dismal outcomes of ccRCC.

Considering the crucial roles of TNFSF13B and PPARGC1A in the immune response in ccRCC, we assessed immune cell infiltration in ccRCC tumor tissues and screened the TICs associated with TNFSF13B and PPARGC1A using CIBERSORT analysis. We found that TNFSF13B expression was positively correlated with the abundances of activated CD4 memory T cells, Tregs, and CD8 T cells. PPARGC1A expression was negatively correlated with abundances of Tregs and CD8 T-cells. CD4 T-cells infiltration facilitates RCC cell proliferation by mediating the TGF-β1/YBX1/HIF2α signaling pathway [33]. CD8 T cells infiltration is associated with good prognosis in most cancers, including bladder and lung cancers [34]. The role of CD8 T cells in cancer prognosis and treatment remains unclear. Consistent with our findings, Xiong et al. reported that infiltrating CD8 T cells were associated with worse RCC prognosis [10]. The authors suggested that immunosuppressive immune cells, such as Tregs, may inhibit the antitumor role of CD8 T cells [10]. The activation/inhibition status of CD8 T cells may affect the efficacy of immunotherapies in RCC [35]. Tregs, a type of CD4 T cells with immunosuppressive effects, can promote tumor development and progression by suppressing antitumor immunity [36]. An early study on RCC revealed that Tregs abundance was negatively associated with outcome [37]. Tregs express numerous immune checkpoint molecules. Treg-targeted therapies have become a research focal point [38,39]. Basic and translational research is required to establish and validate the clinical effects of such treatments. Our TIC analysis suggested that TNFSF13B and PPARGC1A may participate in the balancing and modulation of immune activity in the TME in ccRCC.

This study had several limitations. The retrospective design of our study allowed for the existence of confounding factors. The scope was limited, so we did not examine how the other three TME-related genes (IGLL5, MZB1, and HSD11B1) are associated with TICs. We did not identify the mechanisms by which all these TME-related prognostic genes participate in ccRCC.

Conclusion

In conclusion, we identified five TME-related genes with prognostic value in ccRCC using the ESTIMATE algorithm. TNFSF13B and PPARGC1A were significantly associated with changes in the TME. Their roles as indicators of treatment efficacy and potential therapeutic targets should be further examined.

Acknowledgements

This study was supported by the National Key Research and Development Program of China (grant number: 2018YFC2002202). We would like to thank Editage (www.editage.cn) for English language editing.

Disclosure of conflict of interest

None.

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