RESEARCH ARTICLE
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A pan-cancer analysis unveiling the function of NR4A family genes in tumor immune microenvironment, prognosis, and drug response
Seong-Woo Park1D . Mi-Ryung Han1,2[D
Received: 1 May 2024 / Accepted: 22 June 2024 / Published online: 8 July 2024 @ The Author(s) under exclusive licence to The Genetics Society of Korea 2024
Abstract
Background NR4A family genes play crucial roles in cancers. However, the role of NR4A family genes in cancers remains paradoxical as they promote or suppress tumorigenesis.
Objective We aimed to conduct comprehensive analyses of the association between the expression of NR4A family genes and tumor microenvironment (TME) based on bioinformatics methods.
Methods We collected RNA-seq data from 33 cancer types and 20 normal tissue sites from the TCGA and GTEx databases. Expression patterns of NR4A family genes and their associations with DNA methylation, miRNA, overall survival, drug responses, and tumor microenvironment were investigated.
Results Significant downregulation of all NR4A family genes was observed in 15 cancer types. DNA promoter methylation and expression of NR4A family genes were negatively correlated in five cancers. The expression of 10 miRNAs targeting NR4A family genes was negatively correlated with the expression of NR4A family genes. High expression of all NR4A fam- ily genes was associated with poor prognosis in stomach adenocarcinoma and increased expressions of NR4A2 and NR4A3 were associated with poor prognosis in adrenocortical carcinoma. In addition, we found an elevated expression of NR4A2, which enhances the response to various chemotherapeutic drugs, whereas NR4A3 decreases drug sensitivity. Interestingly, in breast cancer, NR4A3 was significantly associated with C2 (IFN-y dominant), C3 (inflammatory), and C6 (TGF-ß dominant) immune subtypes and infiltrated immune cell types, implying both oncogenic and tumor-suppressive functions of NR4A3 in breast cancer.
Conclusion The NR4A family genes have the potential to serve as a diagnostic, prognostic, and immunological marker of human cancers.
Keywords NR4A family genes . Pan-cancer . Bioinformatics . Immune subtype . Tumor microenvironment (TME)
Introduction
Cancer is the leading cause of early mortality in several countries (CP et al. 2020). The incidence and mortality rates of cancer are rapidly increasing worldwide (Sung et al. 2021). There were 10.3 million cancer deaths and
19.3 million new cancer cases in 2020. Thus, it is important to enhance our comprehensive understanding of tumor sup- pressors and oncogenes.
In previous studies, nuclear receptor subfamily 4 group A (NR4A) family genes were found to be significantly dys- regulated in various types of cancers (Mohan et al. 2012). NR4A is a subfamily of transcription factors consisting of three members: NR4A1, NR4A2, and NR4A3 (Beard et al. 2015). They have similar domain structures, including N- and C-terminal domains containing activation function domains, and ligand-binding domain (LBD), which sur- round a DNA-binding domain, and a hinge region (Safe and Karki 2021). These transcription factors respond to various signals and have various biological functions in humans by regulating the pathways involved in homeostasis, prolifera- tion, cell migration, apoptosis, metabolism, DNA repair, and
☒ Mi-Ryung Han genetic0309@inu.ac.kr
1 Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon 22012, Korea
2 Institute for New Drug Development, College of Life Science and Bioengineering, Incheon National University, Incheon 22012, Korea
glucose utilization (Mohan et al. 2012). NR4A family genes are known to promote or suppress tumorigenesis based on these regulations (Mohan et al. 2012).
The role of these genes has mainly been investigated in blood-derived and solid tumors (Yousefi et al. 2022). Stud- ies on blood-derived cancers have shown that NR4A family genes act as tumor suppressors, suggesting that repression of NR4A1 and NR4A3 contributes to the progression of leu- kemia and lymphoma (Wenzl et al.). In solid tumors, includ- ing breast and colon cancers, the upregulation of NR4A family genes has been shown to be pro-oncogenic and is often associated with poor prognosis and inhibition of apop- tosis (Safe et al. 2016).
However, the role of NR4A family genes in solid tumors remains controversial. The induced expression of NR4A2 promotes apoptosis while inhibiting the growth of bladder cancer cell lines (Inamoto et al. 2008). Some in-vitro stud- ies have shown that NR4A3 is downregulated in breast and lung cancer cell lines (Ohkubo et al. 2000; Fedorova et al. 2019). In silico study using breast, lung, prostate, colorectal, uterine, and ovarian cancer tissues showed that NR4A1 was downregulated in metastatic cancer compared to that in pri- mary tumors (Ramaswamy et al. 2003). Another study using The Cancer Genome Atlas (TCGA) and Molecular Taxon- omy of Breast Cancer International Consortium (META- BRIC) breast cancer data showed that the expression levels of NR4A family genes were significantly lower in tumor samples than in normal samples (Yousefi et al. 2022). Although several studies have demonstrated the importance of the NR4A family genes in cancer, the role of these genes remains paradoxical, and an association between NR4A family genes and the tumor microenvironment (TME) has not been reported. Therefore, it is necessary to conduct a comprehensive pan-cancer analysis of NR4A family genes to assess the role of potential therapeutic targets in clinical research.
In this study, we analyzed the expression profiles of NR4A family genes in 33 cancer types using TCGA pan- cancer data and a Genotype-Tissue Expression (GTEx) dataset. Associations between the expression of NR4A fam- ily genes and DNA methylation, miRNAs, immune sub- type, overall survival, drug sensitivity, and TME were also evaluated. Our results suggest the possibility for the clinical application of NR4A family genes as prognostic and thera- peutic targets in patients with cancer.
Materials and methods
Pan-cancer data collection
RNA-seq data in the fragments per kilobase of transcript per million reads (FPKM) format of TCGA and GTEx, which were processed and integrated uniformly by the Toil process, were downloaded from UCSC Xena (https://xenabrowser. net/datapages/, accessed on November 8 2023) (Vivian et al. 2017). Mature miRNA expression data, Illumina 450k DNA methylation beta-value data, clinical data, survival data, and immune subtype data were downloaded from UCSC Xena. A total of 9,807 tumor samples and 5,973 normal samples were used in this study. Detailed information on the samples is shown in Supplementary Table 1.
Gene expression analysis of NR4A family genes in pan-cancer
The difference in the overall expression levels of NR4A fam- ily genes across TCGA 33 cancers was analyzed using the Kruskal-Wallis test, and Spearman’s correlations between NR4A family genes were investigated. We then performed differential expression analysis between tumor and adja- cent normal samples using the Wilcoxon signed-rank test. A heatmap of the log2 (fold change) values was plotted.
Correlation between the expression of NR4A family genes and DNA methylation
Using the 450k array methylation beta-value data in TCGA database, the mean beta values of CpG sites in the promoter regions (TSS200, TSS1500, 5’ UTR) were calculated. Asso- ciations between the mean beta values and the expression of NR4A family genes were calculated using Spearman’s correlation.
Correlation between the expression of NR4A family genes and miRNA
We downloaded miRNA regulation data of NR4A family genes from miRWalk, an open-source platform that incor- porates TargetScan, miRDB, and miRTarBase datasets and provides predicted and validated miRNA target data of known genes (Sticht et al. 2018). Spearman’s correlation between mature miRNA expression and the mRNA expres- sion of NR4A family genes was calculated. Only miRNAs predicted to target the NR4A family genes from at least two databases were used.
Survival analysis
Patients were divided into high- and low-expression groups based on the median expression levels of the NR4A fam- ily genes. Overall survival was assessed using the Kaplan- Meier method. Intergroup differences were evaluated using a log-rank test, and statistical significance was set at p<0.05. We then performed Univariate Cox proportional hazard regression to identify the association between the expres- sion levels of NR4A family genes and pan-cancer progno- sis. The hazard ratios (HR) were plotted as forest plots.
Correlation between the expression of NR4A family genes and drug sensitivity
The National Cancer Institute (NCI)-60 dataset was down- loaded from the CellMiner version 2.9 database (https:// discover.nci.nih.gov/cellminer/), which included mRNA expression levels of NR4A family genes and z-scores of drug sensitivity in 60 different tumor cell lines from nine different cancer types (Shankavaram et al. 2009). Spear- man’s correlation coefficient was used to test the associa- tion between the expression of NR4A family genes and drug sensitivity. The sensitivities of 248 FDA-approved drugs and 519 drugs in clinical trials were selected and used in the correlation analysis.
Expression of NR4A family genes by different immune subtypes
Six immune subtypes, including wound healing (C1), IFN-y dominant (C2), inflammatory (C3), lymphocyte depleted (C4), immunologically quiet (C5), and TGF-ß dominant (C6), were defined regarding five representative immuno- logical signatures (Thorsson et al. 2018). As each immune subtype has different biological and clinical features that determine the effectiveness of anticancer therapy, we ana- lyzed the association between the expression levels of NR4A family genes using the Kruskal-Wallis test. The distribution of immune subtypes is shown in Supplementary Table 2.
Correlation between the expression of NR4A family genes and TME
ESTIMATE is an algorithm that produces stromal, immune, and ESTIMATE scores that infer the stromal cell infiltra- tion level, immune cell infiltration level, and tumor purity, respectively (Yoshihara et al. 2013). Spearman’s correlation was used to measure the association between the expression of NR4A family genes and the three scores.
Immune deconvolution analysis for breast cancer
Each fraction of immune cell infiltration in BRCA was esti- mated using CIBERSORT, a linear support vector regres- sion-based algorithm for quantifying the infiltration of 22 immune cells from gene expression profiles (Newman et al. 2015). Patients were divided into high- and low-expression groups based on the median expression levels of NR4A3. We investigated the differentially infiltrated immune cells between the high- and low-expression groups using the Wil- coxon signed-rank test.
Statistical analysis
Statistical significance of the comparison between tumor and normal samples was tested using the Wilcoxon signed- rank test. Correlation analysis among the NR4A fam- ily genes was performed using the corrplot version 0.92 R package. The Kruskal-Wallis test was used to evaluate the association between the expression of NR4A family genes and tumor immune subtype. Spearman’s correlation was used to test the association between gene expression and degree of DNA methylation, stromal score, immune score, ESTIMATE score, and drug sensitivity. All tests were selected based on normality tests in our datasets, and a Ben- jamini-Hochberg corrected p (FDR) <0.05 was considered statistically significant. All statistical analyses were per- formed using R software version 4.2.3, and visualizations were conducted using ggplot2 version 3.4.2, ggpubr version 0.6.0, pheatmap version 1.0.12, survival version 3.5.5, and survminer version 0.4.9 R packages (R Core Team 2023).
Results
Gene expression analysis of NR4A family genes in pan-cancer
To investigate the expression profiles of NR4A family genes in cancer, the expression levels of NR4A family genes across 33 types of cancers in TCGA database were examined. Our results showed that the expression levels of the NR4A fam- ily genes were significantly different, with NR4A1 showing the highest expression level (Fig. 1A). NR4A1 and NR4A3 showed the most significant positive correlation, suggest- ing that they may have common biological roles in cancer (Fig. 1B). Differential expression was analyzed using the Wilcoxon signed-rank test. For the differential expression analysis, two cancers (MESO and UVM) with no normal samples were excluded. A heatmap was drawn based on the log2(fold change) of NR4A family gene’s median expres- sion value in tumor and normal samples (Fig. 1C). Our
(A)
(C)
10.0
The expression of NR4A log2(FPKM+1)
7.5.
OV
BLCA
1
KIRP
0
LUSC
-1
5.0
THYM
LAML
-2
PAAD
-3
LIHC
2.5
KIRC
-4
PRAD
-5
ACC
0.0
GBM
HNSC
NR4A1
NR4A2
NR4A3
CHOL
(B)
KICH
NR4A1
NR4A2
NR4A3
DLBC
LGG
1
COAD
READ
0.8
NR4A1
ESCA
0.6
SKCM
STAD
0.4
PCPG
0.2
TGCT
NR4A2
0.58
UCEC
0
THCA
0.2
UCS
BRCA
0.4
CESC
NR4A3
0.66
0.48
0.6
LUAD
SARC
-0.8
NR4A1
NR4A2
NR4A3
-1
differential expression analysis showed that all members of the NR4A family genes were differentially expressed in most cancers, including BLCA, BRCA, ESCA, KIRC, KIRP, LAML, LGG, LIHC, LUAD, LUSC, OV, PAAD, PRAD, SKCM, TGCT, THCA, THYM, UCEC, and UCS (FDR <0.05; Fig. 2). NR4A1 expression was significantly lower in tumor samples than in normal samples in 24 can- cers (ACC, BLCA, BRCA, CESC, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LGG, LIHC, LUAD, LUSC, OV, PRAD, READ, SKCM, STAD, TGCT, THCA, THYM, UCEC, and UCS) and was significantly overexpressed in LAML and PAAD (Fig. 2A). Similar downregulation trends were observed for NR4A2 except for its upregulation in
family genes. (C) Heatmap showing differential expression of the NR4A family genes across each cancer
ESCA, LAML, PAAD, and SKCM (Fig. 2B). NR4A3 was significantly downregulated in 18 cancers (BLCA, BRCA, CESC, COAD, KIRC, KIRP, LGG, LIHC, LUAD, LUSC, OV, PRAD, READ, TGCT, THCA, THYM, UCEC, and UCS), and significantly upregulated in ESCA, LAML, PAAD, and SKCM (Fig. 2C). These results suggest that NR4A1, NR4A2, and NR4A3 were associated with tumor- suppressive functions in most cancers.
(A)
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The expression of NR4A1 log2(FPKM+1)
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KIRP
LAML
LAML
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LGG
LGG
LGG
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LIHC
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LUAD
LUAD
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LUSC
LUSC
LUSC
MESO
MESO
MESO
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OV
OV
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PAAD
:
PAAD
PCPG
PCPG
PCPG
PRAD
PRAD
I
PRAD
READ
READ
READ
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1
SARC
SARC
SKCM
SKCM
:
SKCM
STAD
STAD
*
STAD
TGCT
TGCT
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TGCT
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THCA
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THYM
THYM
THYM
UCEC
UCEC
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UCEC
UCS
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UCS
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UVM
UVM
tumor
normal
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tumor normal
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tumor
normal
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(C)
The expression of NR4A3 log2(FPKM+1)
***: FDR<0.001; **** : FDR <0.0001)
Fig. 2 Violin plots demonstrate differential expression of NR4A family genes between tumor and normal samples ( *: FDR<0.05; ** : FDR <0.01;
Correlation between the expression of NR4A family genes and DNA methylation
In theory, DNA methylation of the promoter region regu- lates mRNA expression by recruiting proteins involved in gene suppression or by interrupting the binding of transcrip- tion factors. To measure the effect of DNA methylation on gene expression, Spearman’s correlations between the mRNA expression data of the NR4A family genes and the mean beta values of their corresponding CpG probes were computed. We found significant correlations in five cancer
types: DLBC, KICH, PAAD, TGCT, and THYM (absolute correlation coefficient>0.3, FDR<0.05; Fig. 3, Supple- mentary Table 3). NR4A1 in TGCT, NR4A2 in DLBC, and NR4A3 in KICH, PAAD, and THYM were negatively cor- related, while only NR4A2 in THYM was positively cor- related. Thus, we speculate that epigenetic alterations in NR4A family genes may cause tumorigenesis in several cancers.
UVM
UCS
UCEC
THYM
THCA
TGCT
STAD
SKCM
SARC
READ
PRAD
-log10(FDR)
PCPG
☒ 2
PAAD
☒ 4
OV
☒ 8
MESO
LUSC
LUAD
Spearman’s correlation
LIHC
0.50
LGG
0.25
LAML
0.00
KIRP
KIRC
-0.25
KICH
-0.50
HNSC
GBM
ESCA
DLBC
COAD
CHOL
CESC
BRCA
BLCA
ACC
NR4A1
NR4A2
NR4A3
Correlation between NR4A mRNA expression and methylation
Correlation between the expression of NR4A family genes and miRNA
Micro RNAs (miRNAs) are key regulators of mRNA expression. To clarify the interactions between miRNAs and NR4A family genes in human cancers, we calculated Spearman’s correlations between miRNAs targeting NR4A family genes and their expression across 33 cancer types. In total, 26 miRNAs that were predicted to target NR4A fam- ily genes in at least two databases were used. Among these, 12 miRNAs showed significant correlations with TGCT, SARC, ESCA, KIRC, LIHC, and THCA (absolute corre- lation coefficient>0.3, FDR<0.05; Fig. 4, Supplementary Table 4). In detail, miR-200b-5p expression was negatively correlated with NR4A1 expression in TGCT (Fig. 4A). Sim- ilar trends were observed between miR-409-3p and NR4A2 expression in SARC and TGCT (Fig. 4B). In case of NR4A3, the expression of seven miRNAs (miR-103a-3p, miR- 20b-5p, miR-3154, miR-455-5p, miR-494-3p, miR-501-5p, and miR-675-3p) and miR-136-5p were negatively corre- lated with NR4A3 expression in TGCT and SARC, respec- tively. Notably, all NR4A family genes in TGCT showed significant negative correlations with at least one miRNA. Furthermore, miR-10a-5p in ESCA, KIRC, and LIHC, miR- 10b-5p in KIRC, and miR-20b-5p in THCA were positively correlated with NR4A3 expression. These results indicate that regulation of the expression of NR4A family genes via miRNAs may be associated with cancer progression.
Association between the expression of NR4A family genes and patients’ overall survival
We investigated the association between the expression of NR4A family genes and overall survival (OS) using the Kaplan-Meier method. We found that the expression of NR4A family genes was significantly associated with patient outcomes and that the direction of this association varied (Fig. 5, p<0.05). NR4A1 is a favorable prognostic indicator for KICH and KIRC and plays a damaging role in STAD. NR4A2 was a positive prognostic factor for CHOL, PCPG, and SKCM and a negative prognostic factor for ACC and STAD. Similarly, NR4A3 is a positive prognostic factor for CHOL and plays a damaging role in ACC, LGG, and STAD. Particularly, all NR4A family genes play damaging roles in STAD. In addition, the hazard ratios (HR) of the NR4A family genes were calculated using univariate Cox regression (Supplementary Fig. 1, Supplementary Table 5).
Correlation between the expression of NR4A family genes and drug sensitivity
To analyze the potential association between the expression of NR4A family genes and drug sensitivity, we performed correlation analysis using data from the CellMiner database (Shankavaram et al. 2009). We found that the expression of NR4A family genes was significantly correlated with 197 (25.7%) of the 767 drugs under FDA approval or clinical trials (absolute correlation coefficient>0.3, FDR <0.05, Fig. 6, Supplementary Table 6). The expression of NR4A1 was positively correlated with seliciclib and hydrastinine HCl and negatively correlated with SGX-523. The expres- sion of NR4A2 was positively correlated with the sensitiv- ity to dabrafenib, AZ-628, cobimetinib, alvespimycin, and PF-03758309 and negatively correlated with EC-330. The expression of NR4A3 was negatively correlated with TPX- 0005, ON-123,300, and SAR-20,347 sensitivity. These results suggest that dysregulated expression of NR4A fam- ily genes may be involved in resistance to chemotherapy and targeted drug therapy.
Expression of NR4A family genes using different immune subtypes
To investigate the potential association between the expres- sion levels of NR4A family genes and the six different immune subtypes of TCGA cancers, differential expres- sion analysis was performed using the Kruskal-Wallis test. We found that the expression of NR4A family genes across the immune subtypes was significantly different in BRCA, KIRC, and LUAD (Fig. 7, FDR <0.05 for all NR4A fam- ily genes). In these cancers, the high expression of NR4A family genes was mainly associated with the C3 subtype. In BRCA, NR4A1 was highly expressed in C3, NR4A2 was highly expressed in C3 and C4, and NR443 was highly expressed in C2, C3, and C6 (Fig. 7A). In KIRC, NR4A1 and NR4A3 were highly expressed at C5, while NR4A2 showed high expression at C3 (Fig. 7B). In LUAD, all NR4A family genes showed the highest expression at C3 (Fig. 7C). These results suggest that NR4A family genes may be related to tumor immunity in BRCA, KIRC, and LUAD.
Association between the expression of NR4A family genes and TME
To explore the association between the expression of NR4A family genes and the TME, the immune score of TCGA tumor samples was computed using the ESTIMATE algo- rithm. Spearman’s correlation between the immune score and the expression of NR4A family genes was examined. We found that the expression of all NR4A family genes
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UVM
UVM
UCS
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TGCT
TGCT
STAD
STAD
SKCM
SKCM
SARC
-log10(FDR)
SARC
-log10(FDR)
READ
READ
PRAD
2
PRAD
2
PCPG
4
PCPG
4
PAAD
8
PAAD
8
OV
OV
MESO
MESO
16
LUSC
LUAD
Spearman’s correlation
LUSC
LIHC
0.2
LUAD
Spearman’s correlation
LIHC
LGG
0.0
LGG
LAML
LAML
0.2
KIRP
-0.2
KIRP
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KICH
-0.4
KIRC
KICH
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HNSC
HNSC
ESCA
ESCA
DLBC
DLBC
COAD
COAD
CHOL
CESC
CHOL
BRCA
CESC
BLCA
BRCA
BLCA
ACC
ACC
miR-200b-5p
miR-342-5p
miR-33b-5p
miR-409-3p
miR-93-5p
Correlation between NR4A1 mRNA expression and miRNA expression
Correlation between NR4A2 mRNA expression and miRNA expression
(C)
UVM
UCS
UCEC
THYM
THCA
TGCT
STAD
SKCM
SARC
-log10(FDR)
READ
PRAD
2
PCPG
4
PAAD
8
OV
MESO
16
LUSC
LUAD
Spearman’s correlation
LIHC
0.4
LGG
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0.0
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KICH
HNSC
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ESCA
DLBC
COAD
CHOL
CESC
BRCA
BLCA
ACC
miR-103a-3p
miR-106b-5p
miR-107
miR-10a-5p
miR-10b-5p
miR-136-5p
miR-20a-5p
miR-20b-5p
miR-3154
miR-411-3p
miR-455-5p
miR-494-3p
miR-495-3p
miR-501-5p
miR-506-3p
miR-508-3p
miR-6503-3p
miR-664b-3p
miR-675-3p
miR-7-5p
miR-877-5p
Correlation between
NR4A3 mRNA expression and miRNA expression
(A)
Cancer : KICH
(B)
Cancer : KIRC
(C)
Cancer : STAD
NR4A1
Group=High
Group=Low
NR4A1
Group=High
Group=Low
NR4A1
Group=High
Group=Low
1.00-
1.00 -
1.00-
Survival probability
Survival probability
Survival probability
0.75
0.75
0.75
0.50
0.50
0,50
0.25
p = 0.037
0.25
p = 0.043
0.25
p= 0.0059
ـها
0.00
0.00
0.00
0
2
4
6
8
10
12
0
2
4
6
8
10
12
0
2
4
6
8
10
Time(year)
Time(year)
Time(year)
NR4A1
Number at risk
Group-High Group-Low
32
25
役
1.4
3
1
NR4A1
Number at risk
Group-High Group=Low
266
195
114
48
20
9
0
NR4A1
Number at risk
Group=High Group=Low
205
203
21
13
2
3
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0
2
4
6
8
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12
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6
Time(year)
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10
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12
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Cancer : ACC
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Cancer : CHOL
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Cancer : PCPG
NR4A2
Group-High
Group=Low
NR442
Group-High
Group=Low
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Group-High
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1.00
1.00
1.00-
Survival probability
Survival probability
Survival probability
0.75
D.75
0.75
0.50
0.50
0.50
0.25
p = 0.019
D.25
p = 0.035
0.25
p = 0.045
0.00
0.00
0.00
0
2
4
6
8
1D
12
0
2
4
0
2
4
6
8
10
12
14
Time(year)
16
18
20
22
24
26
Time(year)
Time(year)
NR4A2
Number at risk
Number at risk
Group=High Group=Low
8
1
NR4A2
Number at risk
32
13
5
3
Group- High Group=Low
18
9
3
NR4A2
Group=High Group=Low
85
SZ
19
12
10
3
9
0
0
9
0
0
9
0
0
2
4
6
8
10
0
2
4
0
2
4
6
8
10
12
14
24
Time(year)
12
Time(year)
16
18
20
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26
Time(year)
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Cancer : SKCM
(H)
Cancer : STAD
(1)
Cancer : ACC
NR4A2
GroupeHigh
Gmup=Low
NR442
Group=High
Group=Low
NR4A3
Group-High
Group=Low
1.00-
1.004
1.00-
Survival probability
Survival probability
Survival probability
0.75
0.75
0.75
0.50
0.50
0.50
0.25
p = 0.0056
0.25
p = 0.046
0.25
p < 0.0001
0.00
0.00
0.00
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
0
2
4
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8
10
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Time(year)
2
4
6
8
10
Time(year)
NR4A2
Number at risk
Group=High Group=Low
28
10
A
JA
0+
NR4A2
Number at risk
NR4A3
Number at risk
AL
168
123
117
4
A
A
Group=High Group=Low
205
20
13
4
3
0
Group=High
Group=Low
33
X
18 2
탈
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3
1
0
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4
A
6
8
10
12
14
16
18
20
22
24
26
28
30
0
2
4
6
8
Time(year)
10
0
2
4
6
a
10
12
Time(year)
Time(year)
(J)
Cancer : CHOL
(K)
Cancer : LGG
(L)
Cancer : STAD
NR4A3
GroupeHigh
Group=Low
NR443
Group=High
Group=Low
NR4AS
Group=High
Group=Low
1.00-
1.00-
1.00-
Survival probability
Survival probability
0.75
Survival probability
0.75
0.75
0.50
0.50
0.50
0.25
p = 0.033
0.25
p = 0.038
0.25
p = 0.015
0.00
0.00
0.00
0
2
4
0
2
4
6
8
10
12
14
16
18
20
0
2
4
6
Time(year)
a
Time(year)
Time(year)
10
NR4A3
Number at risk
Number at risk
Number at risk
Group=High
Group=Low
18
9
3
NR4A3
Group=High Group=Low
287
a
52
3
1.7
12
2
3
b
8
8
NR4A3
Group=High Group=Low
ER
13
3
0
6
0
2
4
0
2
4
6
8
Time(year)
10
12
Time(year)
14
16
18
20
0
2
4
6
Time(year)
a
10
was significantly correlated with immune infiltration in dif- ferent cancer types (absolute correlation coefficient> 0.3, FDR <0.05; Fig. 8A, Supplementary Table 7). The expres- sion of NR4A1 and NR4A2 was significantly and positively correlated with immune scores in LAML, DLBC, and TGCT. Particularly, the expression of NR4A3 was signifi- cantly correlated with the immune score in multiple cancer types. For instance, the expression of NR4A3 was positively correlated with the immune score in BLCA, BRCA, CESC, CHOL, COAD, DLBC, ESCA, HNSC, LUSC, PAAD, READ, STAD, THYM, and UCS and negatively corre- lated with ACC and UVM. Furthermore, we analyzed the relationship between the NR4A family genes and stromal and ESTIMATE scores using the ESTIMATE algorithm to infer the ratio of stromal cell infiltration to tumor purity
(Fig. 8B-C). In most cases, NR4A3 was positively corre- lated with stromal, immune, and ESTIMATE scores, indi- cating that NR4A3 may be associated with high stromal cell infiltration, high immune cell infiltration, and low tumor purity. The correlation coefficients are shown in Supple- mentary Table 7.
Immune deconvolution analysis for breast cancer
As the expression of NR4A3 in BRCA showed the most significant positive correlation with the immune score and the most significant association with immune subtypes, we further analyzed immune cell infiltration in BRCA. Twelve types of immune cells (naïve B cells, resting dendritic cells, M1 macrophages, activated mast cells, monocytes,
NR4A2, Dabrafenib Cor=0.497, FDR=0.00925
NR4A2, AZ-628
Cor=0.461, FDR=0.00925
NR4A2, Cobimetinib (isomer 1 Cor=0.538, FDR=0.00925
NR4A1, Seliciclib
Cor=0.481, FDR=0.00925
NR4A2, Alvespimycin
Cor=0.449, FDR=0.00925
2-
2-
2-
1-
1 -
1 -
1
…
1 -
0-
0
0-
0-
-1-
0-
:
-1
-1
-1-
-2-
-2-
0
1
2
3
-2-
0
1
2
3
0
1
2
3
0
1
2
3
4
5
-3-
0
1
2
3
NR4A2, PF-03758309
Cor=0.452, FDR=0.00925
NR4A2, ulixertinib Cor=0.458, FDR=0.00925
NR4A2, Tanespimycin Cor=0.447, FDR=0.00925
NR4A2, EC-330
Cor =- 0.449, FDR=0.00925
NR4A3, TPX-0005
Cor =- 0.449, FDR=0.00925
1
0-
2.
1 .
1.
1.
0
2-
-1-
0-
-2-
3
0
-1.
%
0
Drug sensitivity
-1-
-3-
-1-
-2-
0
1
2
3
0
1
2
3
0
1
2
3
0
1
2
3
-2-
0
1
2
3
4
NR4A3, ON-123300
Cor =- 0.443, FDR=0.01048
NR4A2, LY-3214996 Cor=0.434, FDR=0.01156
NR4A2, ABL-001 Cor=0.434, FDR=0.01156
NR4A2, Altiratinib Cor=0.428, FDR=0.01156
NR4A2, TAK-632 Cor=0.429, FDR=0.01156
2.
2
6.
3-
1.
1.
5.0-
4-
2-
0
0
2.5-
2-
1-
-1
0
-1
0.0-
0-
-2.
-1-
0
1
2
3
4
0
1
2
3
0
1
2
3
0
1
2
3
0
1
2
3
NR4A2, LXH-254 Cor=0.429, FDR=0.01156
NR4A1, SGX-523
Cor =- 0.425, FDR=0.01156
NR4A1, Hydrastinine HCI Cor=0.424, FDR=0.01156
NR4A3, SAR-20347 Cor =- 0.422, FDR=0.01156
NR4A2, MLN-2480
Cor=0.422, FDR=0.01156
2
4-
3-
2
3
1.
3-
2.
1-
2-
2-
0
1.
0-
1-
1.
-1
0-
0-
-1-
0
-1-
-2-
&
0
1
2
3
-1-
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
0
1
2
3
Gene expression
neutrophils, activated NK cells, activated CD4 memory T cells, resting CD4 memory T cells, CD8 T cells, follicular helper T cells, and gamma delta T cells) were significantly enriched in NR443 high group (Fig. 7D). NR4A3 low group showed high fractions of M0/M2 macrophages, resting mast cells, resting NK cells, plasma cells, and naïve CD4 + T cells (Fig. 7D). These findings suggest that the expression level of NR4A3 could be a relevant marker of the infiltration level of immune cells in breast cancer and could provide options for breast cancer immunotherapy. The different immune cell fractions based on the expression levels of NR4A1 and NR4A2 are shown in Supplementary Fig. 2.
Discussion
Since NR4A family genes are known to function as both pro-oncogenes and tumor suppressors, their biological func- tions are ambiguous. To address this, we focused on clarify- ing the characteristics of the NR4A family genes across 33 types of cancers.
We found that NR4A family genes were downregulated in most cancers. First, NR4A1 was found to be downregulated in 24 cancers, including BRCA and COAD. Low expression of NR4A1 in triple-negative breast cancer has been associ- ated with lymph node metastasis, advanced tumor stage,
and poor relapse-free survival (Wu et al. 2017). A previous study demonstrated that NR4A1 inhibited Wnt signaling and retarded tumorigenesis in a mouse model of colorectal can- cer (Chen et al. 2012). Second, NR4A2 was downregulated in 20 cancers, including BLCA. NR4A2 activation via DIM- C-pPhCl induces apoptosis and inhibits growth of bladder cancer cell lines (Inamoto et al. 2008). Third, NR4A3 was downregulated in 18 cancers including BRCA, LUAD, and LUSC. Previously, NR4A3 played a tumor-suppressive role in breast and lung cancers by triggering apoptosis (Fedorova et al. 2019). In summary, consistent with previous findings, we confirmed the tumor-suppressive role of the NR4A fam- ily genes in certain types of cancer.
Correlation analysis between DNA methylation and mRNA expression showed that NR4A family genes are regulated by promoter methylation in several cancers. The mRNA expression levels of NR4A1 in TGCT, NR4A2 in DLBC, and NR443 in THYM were downregulated, and their methylation levels were negatively correlated with mRNA expression. NR4A2 is methylated in B-cell lymphoma cell lines and unmethylated in healthy controls (Bethge et al. 2013). Regarding NR4A3, promoter hypermethylation and mRNA down-regulation were reported in patients with gas- tric cancer (Yeh et al. 2016).
We also investigated the association between the expression of NR4A family genes and miRNAs targeting them. We found
(A)
Cancer : BRCA
NR4A1
NR4A2
NR4A3
10.0
7.5
Immune_subtype
Expression
C1
5.0
C2
C3
C4
C6
2.5
0
0.0
d
A
C1
C2
C3
C4
C6
C1
C2
C3
C4
C6
C1
C2
C3
C4
C6
(B)
Cancer : KIRC
NR4A1
NR4A2
NR4A3
**
**
10.0
7.5
0
Immune_subtype
Expression
C1
C2
5.0
8
C3
C4
C5
2.5
1
C6
A
0.0
C1
C2
C3
C4
C5
C6
C1
C2
C3
C4 C5
C6
C1
C2
C3
C4
C5
C6
(C)
Cancer : LUAD
NR4A1
NR4A2
NR4A3
10.0
7.5
Immune_subtype
Expression
C1
5.0
C2
C3
C4
C6
2.5
0.0
C1
C2
C3
C4
C6
C1
C2
C3
C4
C6
C1
C2
C3
C4
C6
(A)
UVM
(B)
UVM
uCs
UCS
UCEC
UCEC
THYM
THYM
THCA
THCA
TGCT
TGCT
STAD
STAD
SKCM
SKCM
SARC
SARC
READ
-log10(FDR)
READ
-log10(FDR)
PRAD
2
PRAD
2
PCPG
4
PCPG
4
PAAD
8
PAAD
OV
OV
8
MESO
16
MESO
16
LUSC
< 16
LUSC
< 16
LUAD
LUAD
LIHC
Spearman’s correlation
LIHC
Spearman’s correlation
LGG
0.50
LGG
LAML
LAML
KIRP
0.25
0.4
KIRP
0.2
KIRC
0.00
KIRC
KICH
0.0
HNSC
-0.25
KICH
HNSC
-0.2
GBM
GBM
ESCA
ESCA
DLBC
DLBC
COAD
COAD
CHOL
CHOL
CESC
CESC
BRCA
BRCA
BLCA
BLCA
ACC
ACC
NR4A1
NR4A2
NR4A3
NR4A1
NR4A2
NR4A3
Correlation between
NR4A mRNA expression and Immune score
Correlation between NR4A mRNA expression and Stromal score
(C)
UVM
UCS
UCEC
THYM
THCA
TGCT
STAD
SKCM
SARC
READ
-log10(FDR)
PRAD
2
PCPG
4
PAAD
OV
8
MESO
16
LUSC
< 16
LUAD
LIHC
Spearman’s correlation
LGG
LAML
0.4
KIRP
0.2
KIRC
0.0
KICH
HINSC
-0.2
GBM
-0.4
ESCA
DLBC
COAD
CHOL
CESC
BRCA
BLCA
ACC
NR4A1
NR4A2
NR4A3
Correlation between NR4A mRNA expression and ESTIMATE score
(D)
0.8
log2(Signature score+1)
0.6
Group
0.4
high
low
0.2
0.0
B cells memory
B cells naive
Dendritic cells activated
Dendritic cells resting
Eosinophils
Macrophages MO
Macrophages M1
Macrophages M2
Mast cells activated
Mast cells resting
Monocytes
Neutrophils
NK cells activated
NK cells resting
Plasma cells
T cells CD4 memory activated
T cells CD4 memory resting
T cells CD4 naive
T cells CD8
T cells follicular helper
T cells gamma delta
T cells regulatory (Tregs)
significant correlations between the expression of NR4A fam- ily genes and miRNAs in several cancer types. Interestingly, all NR4A family genes exhibited significant negative cor- relations with at least one miRNA in TGCT, suggesting their role as oncogenes: miR-200b-5p targeted NR4A1 in TGCT, miR-409-3p targeted NR4A2 in SARC and TGCT, and miR- 455-5p and miR-675-3p targeted NR4A3 in TGCT. Although our miRNA findings are novel for TGCT and SARC, their
between the fraction of immune cell infiltration and the expression of NR4A3 in BRCA( *: FDR<0.05; ** : FDR<0.01; *** : FDR <0.001; ****: FDR<0.0001)
oncogenic role in other cancers has been previously reported in emerging studies. Evidence was found in studies on miR- 200b-5p and miR-409-3p in prostate cancer (Lin et al. 2014; Josson et al. 2014). In contrast, two miRNAs (miR-10a-5p in ESCA, KIRC, and LIHC and miR-10b-5p in KIRC) were positively correlated with the expression of NR4A3, suggesting a tumor-suppressive role in cancer. Notably, both miR-10a-5p and miR-10b-5p were found in KIRC, and these results were
supported by previous studies in which they were significantly downregulated as tumor suppressors in kidney renal cell carci- noma (Khella et al. 2017; Tan et al. 2021).
The aberrant expression of NR4A family genes was sig- nificantly associated with OS and drug responses in several cancers. In particular, increased expression of all NR4A family genes was associated with a poor prognosis in STAD, which is consistent with a previous study (Han et al. 2013). In addition, we are the first to report that increased expression of NR4A2 and NR4A3 is associated with a poor prognosis in ACC. We also revealed an elevated expression of NR4A2, which enhances responses to various chemotherapeutic drugs, whereas NR4A3 decreases drug sensitivity. Further studies are necessary to elu- cidate the biological interactions between NR4A family genes and drugs.
We identified significant associations between the expres- sion of NR4A family genes and immune subtypes in BRCA, KIRC, and LUAD. In BRCA and LUAD, NR4A1 showed the highest expression at C3 (inflammatory). In KIRC, the expression of NR4A1 was the highest in C3 followed by C5 (immunologically quiet), while high expression of NR4A2 was associated with C3 and C6 (TGF-ß dominant). In BRCA, high expression of NR4A2 was associated with C3 and C4 (lympho- cyte-depleted), whereas NR4A3 was highly expressed in C2, C3, and C6. Interestingly, as the C3 subtype is known to have the most favorable prognosis and the C6 subtype is related to the worst prognosis in a pan-cancer study, NR4A3 may have both oncogenic and tumor-suppressive roles (Thorsson et al. 2018). These characteristics imply a potentially crucial role for the NR4A family genes in the immune system.
Our TME analysis revealed the most significant association between NR4A3 and immune infiltration score in BRCA. Fur- ther investigation of the effect of NR4A3 on BRCA revealed 12 higher fractions and six lower fractions of immune cell types in NR4A3 high group than in the NR4A3 low group. Certain cell types with higher proportions (e.g., M1 macrophages, activated NK cells, activated CD4 memory T cells, CD8 T cells, follicu- lar helper T cells, and gamma delta T cells) have anti-tumor functions (Lei et al. 2020). In contrast, among the lower frac- tions of immune cells, M2 macrophages and resting NK cells are mainly associated with pro-tumor roles (Lei et al. 2020). The roles of neutrophils (lower fraction) and monocytes (higher fraction) can be either anti-tumor or pro-tumor, depending on the interactions among neighboring cells in the TME (Ugel et al. 2021; Que et al. 2022). Previous studies have reported an association between NR4A3 and the tumor immune microen- vironment. NR4A3 inhibits the memory potential and effector functions of CD8+T cells (Odagiu et al. 2020). Moreover, double knockout of PRDM1 and NR4A3 transformed CAR-T cell phenotypes from TIM-3+CD8+to TCF1+CD8+cells to counter the exhaustion of tumor-infiltrated CAR T cells and improve antitumor responses, whereas this effect was not
achieved with a single knockout of PRDM1 or NR4A3 alone (Jung et al. 2022). Based on our findings and previous reports, NR4A3 may have both oncogenic and tumor-suppressive func- tions under specific immune cell conditions, independent of its downregulation in breast tumor tissues. Although the effects of NR4A3 on the tumor immune system are debatable, our results may help in understanding the role of NR4A3 in select- ing appropriate breast cancer immunotherapies.
Our study has some limitations. Further validation is required because our findings have not been validated using independent datasets or in vitro/in vivo experiments. For TME analysis, immune deconvolution results should be investi- gated using single-cell sequencing or immunological experi- ments. Future studies focusing on the biological mechanisms of NR4A family genes at both the cellular and molecular levels are necessary to confirm our findings.
Our study provides the first comprehensive pan-cancer anal- ysis of NR4A family genes which may be candidate diagnostic and prognostic factors for various cancers. Their expression is significantly downregulated in most cancers and is associated with methylation levels, miRNA expression, immune sub- type, overall survival, drug sensitivity, and TME. In particular, NR4A3 was significantly associated with the immune subtypes and infiltrating immune cell types in BRCA. Although these findings need to be validated experimentally, we have provided the transcriptomic features and biological functions of NR4A family genes in pan-cancer. Thus, we suggest blueprints for further studies on pan-cancer function and the use of NR4A family genes as new diagnostic and prognostic markers and therapeutic targets.
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s13258- 024-01539-1.
Acknowledgements The authors gratefully acknowledge the collabo- ration of the study participants and research staff included in this study.
Author contributions Conceptualization: Mi-Ryung Han; Methodol- ogy: Mi-Ryung Han; Data curation: Seong-Woo Park; Formal analysis and investigation: Seong-Woo Park; Writing - original draft prepara- tion: Seong-Woo Park, Mi-Ryung Han; Writing - review and editing: Mi-Ryung Han; Funding acquisition: Mi-Ryung Han.
Funding This research was supported by Incheon National University Research Grant in 2022.
Declarations
Conflict of interest No potential conflicts of interest relevant to this article are reported.
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