NR2F6 promotes the malignant progression of neuroblastoma as an indicator of poor prognosis

doi:10.21203/rs.3.rs-3346890/v1

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Research Article

NR2F6 promotes the malignant progression of neuroblastoma as an indicator of poor prognosis

https://doi.org/10.21203/rs.3.rs-3346890/v1

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Background

NR2F6 is an orphan nuclear receptor with dual tumorigenic activity in the immune system and tumor cells, playing an essential role in tumor differentiation and immunity. This study aimed to investigate the expression level of NR2F6 in various tumors and its effect on neuroblastoma.

Methods

We evaluated the role of NR2F6 in the genesis and development of 34 different tumors through multiple databases. In addition, we investigated the effects of NR2F6 expression levels on neuroblastoma risk factors and prognosis using pathology sections and clinical data from primary retroperitoneal neuroblastoma in children. The effects on cell proliferation, invasion, and migration were explored by knocking down NR2F6 expression in SK-N-BE(2).

Results

The findings showed that NR2F6 was significantly correlated with the prognosis of neuroblastoma and was an important indicator suggesting disease regression. In addition, NR2F6 knockdown slowed down neuroblastoma cells' proliferation, invasion, and migration ability in vitro.

Impact:

In conclusion, our results suggest that NR2F6 plays a crucial role in tumor-promoting effects and can be used as a potential prognostic marker for neuroblastoma.

NR2F6

pan-cancer

neuroblastoma

prognosis

Cancer is currently the most significant life-threatening public health problem encountered by humankind. According to a report by the World Health Organization, about 9.6 million people died of cancer worldwide in 2018 (https://www.who.int/news-room/fact-sheets/detail/cancer). Cancer treatment has been plaguing the healthcare system. Due to individual differences, patients have varying sensitivities to treatments. Thus, finding high-coverage or targeted treatments has become our new goal, and immunotherapy is gradually becoming a new direction in cancer treatment as research continues to deepen [1].

Advancements in sequencing technology and bioinformatics have enabled us to identify tumor driver genes, molecular subtypes, molecular interactions, and other features using large-scale cancer genomics databases such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Since cancer results from aberrant cell proliferation, differentiation, and immune stress [2], understanding the characteristics, alteration pathways, and immune properties of the unique tissue environment of cancer is significant for identifying potential targets for tumorigenesis and development.

NR2F6 (nuclear receptor subfamily 2F group member 6, also known as Ear-2) is an orphan nuclear receptor with dual pro-tumorigenic activity in the immune system and tumor cells [3].NR2F6 might be a critical immune checkpoint in the T-cell compartment, controlling tumor progression and growth.NR2F6 in T-cells inhibits the transcription of the cytokines NFAT and AP-1, which suppresses activated secretion of IL-2 and IFNγ and IL17 in activated T cells, promoting an aberrant immune response to tumors [4].NR2F6 is commonly expressed in resting T cells at low levels; however, it is highly inducible in effector (but not regulatory) T cells in the inflammatory tumor microenvironment [5]. In vitro studies have shown that NR2F6 is an inhibitory receptor in tumor-associated non-regulatory T cells, highly expressed on so-called "exhausted" T cells to reactivate tumor-specific T lymphocytes [6]. Upregulation produces effector T cells that are unable to achieve an adequate immune response to cancer, and NR2F6 downregulation sensitizes tumors to established PD-1/PD-L1 axis blockade, which produces a potent synergistic effect with established surface checkpoint blockade (PD-L1, CTLA-4) to inhibit tumor progression [7]. Ablation of NR2F6 enhances the expression of interleukin-2 (IL-2), interferon -γ (IFN-γ), and tumor necrosis factor-α (TNF-α) secretion, thereby promoting anti-tumor immune responses.NR2F6 gene ablation/PTT-mediated aPD-L1 immune-enhancing approach allows local treatment-stimulated immune responses to build long-term immune memories throughout the body, thereby inhibiting tumor metastasis [8]. Another immune cell type in which NR2F6 has been shown to play an important role is the macrophage, which may suppress immune cell genes in reverse transcriptional activation in human macrophages to promote tumor development [9].

NR2F6 has tumor-promoting properties and is up-regulated in ovarian [10], breast [11], cervical [12], colon [12], hepatocellular [8], and non-small-cell lung cancers [13], and is a prospective biomarker that plays a vital role in the proliferation, metastasis, and survivability of both tumor cells and long term hematopoietic stem cells (LT-HSCs) [14], as well as inhibiting the differentiation process of leukemia cells. Given the dual pro-tumorigenic activity of NR2F6 in immune cells and tumor cells, inhibition of NR2F6 expression offers a unique therapeutic potential to improve current treatment outcomes. With current studies demonstrating increased expression of NR2F6 in multiple cancer types, targeting NR2F6 could serve as an independent and potentially synergistic option. Gene modulation therapy, once feasible, may help promote local effector T cell responses at the tumor site with fewer systemic immune-related adverse events. Any rational drug design targeting NR2F6-mediated gene regulation would have critical clinical applications by affecting both tumor-abnormal effector T cells and tumor cells while inducing tumor cell death. Thus, the relationship between the level of NR2F6 expression in pan-cancer and immune infiltration and tumor prognosis deserves further investigation.

In this study, we comprehensively explored the expression of NR2F6 in 34 human cancers based on TCGA, TARGET, and GTEx analysis databases. We also evaluated the prognostic value of NR2F6 in pan-cancer, and we analyzed the relationship between the expression level of NR2F6 and the immune cell infiltration, immune checkpoints, and immune regulatory genes, in addition to the relationship between the expression level of NR2F6 and mutations (including TMB and MSI). Finally, we used neuroblastoma (NB) as an example to verify the expression level and prognostic value of NR2F6 in cancer and constructed the related column line diagram.

Analysis of the presence of differential expression of NR2F6 in pan-cancer tissues

We obtained expression data for 34 cancer types. Significant upregulation of NR2F6 mRNA levels was observed in 25 tumors: GBM, GBMLGG, UCEC, BRCA, CESC, LUAD, ESCA, STES, PRAD, STAD, LUSC, LIHC, WT, BLCA, THCA, OV, PAAD, TGCT, UCS, ALL, LAML, PCPG, ACC, KICH, CHOL: we observed significant downregulation in 3 tumors such as LGG, KIPAN, KIRC (Figure 1). Our results showed that NR2F6 expression was significantly upregulated in human cancers relative to paraneoplastic tissues.

Expression levels of NR2F6 at different clinical stages in pan-cancer

By analyzing the expression of 26 cancer types, we observed significant differences in NR2F6 expression levels in four tumors (ESCA, BLCA, ACC, KICH) in different clinical stages (Figure 2).

Correlation analysis between NR2F6 expression level and prognosis of pan-cancer

We obtained expression data for 44 cancer types and Overall survival data for the corresponding samples. We observed poor prognosis for high expression in 9 tumor types (GBMLGG, LGG, SKCM, ALL, MESO, LAML, ACC, ALL-R, and NB) (Fig. 3A)) and poor prognosis for low expression in 1 tumor type (BLCA) (Fig. 3B).

We used disease-free survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) for survival analysis. We obtained expression data for 32 cancer types and DFI data for the corresponding samples and observed that NR2F6 high expression in four tumor types (CESC, KIRP), PRAD, and ACC) had a poor prognosis. In 1 tumor type (PCPG), NR2F6 low expression had a poor prognosis (Figure 4A). Expression data were obtained for a total of 38 cancer types as well as DSS data for the corresponding samples. Poor prognosis was observed for high expression in 8 tumor types (GBMLGG, LGG, KIRC, SKCM, SKCM-M, MESO, PCPG, ACC), and poor prognosis for low expression in 2 tumor types (BLCA, OV) (Figure 4B). The total OS was consistent with the OS of each tumor (Figure 4C). In the expression data of 38 cancer types and the PFI data of the corresponding samples, we observed poor prognosis of NR2F6 high expression in 9 tumor types (GBMLGG, LGG, CESC, KIRP, PRAD, KIRC, MESO, UVM, and ACC) and poor prognosis of NR2F6 low expression in 2 tumor types (BLCA, OV) (Figure 4D).

Expression and immune correlation analysis of NR2F6

Based on the CIBERSORT method, we obtained 22 categories of immune cell infiltration scores for 10,180 tumor samples in 44 tumor types. Finally, we observed that the expression of NR2F6 was significantly associated with immune infiltration in 44 cancer types (Figure 5A). Based on the TIMER method, six categories of immune cell infiltration scores were obtained for 9406 tumor samples in 38 tumor types. Finally, we observed that NR2F6 expression was significantly correlated with immune infiltration in 31 cancer types (Figure 5B).

We analyzed the association between NR2F6 expression and immunomodulatory genes in different cancer types. Pearson correlation analysis of NR2F6 with immunomodulatory genes showed that NR2F6 was significantly negatively co-expressed with immunomodulatory genes, except in a few cancers, such as KIPAN, TGCT, and BRAC; in the majority of cancers, NR2F6 was significantly and positively co-expressed with most of the immunomodulatory genes (Figure 6A). In addition, we analyzed the relationship between NR2F6 expression levels and immune checkpoint (ICP) genes. We calculated Pearson correlation coefficients between NR2F6 expression and 60 immune checkpoint genes, including 24 repressor genes and 36 stimulator genes. We showed that in most tumors, immune checkpoint genes were significantly positively co-expressed with NR2F6, whereas in a few cancers, such as CHOL, KIPAN, BRCA, TGCT, and PAAD, the majority of immune checkpoint genes were significantly negatively co-expressed with CRABP2 were significantly negatively expressed, especially genes such as CTLA4, SLAMF7, HAVCR2, and TIGIT (Figure6B).

Correlation analysis of NR2F6 expression levels and mutational landscape in pan-cancer

The mutational landscape suggested that NR2F6 could be observed as a distinct gene mutation in cancers such as colon cancer (COAD) and gastric adenocarcinoma (STAD) (Figure 7A). Therefore, we focused on analyzing the relationship between NR2F6 expression and specific genomic features, such as somatic mutations and copy number variations (copy number variants) in the COAD and STAD data. In COAD, 288 samples were tested for mutations, of which the mapping samples contained 173 (60.1%), and the top 10 genes in terms of mutation rate included KMT2D, LRP2, and SACS. There were significant differences in the mutation rate between the high and low NR2F6 expression groups. In STAD, 414 samples were detected with mutations, of which the mapping samples contained 335 (80.9%), and the top 10 genes with mutation rates included TTN, TP53, SYNE1, etc. There was a significant difference in mutation rates between NR2F6 high-expression and low-expression groups (Figure 7B).

Independent Risk Factors for NB and Development and Validation of the Column Line Chart

Combined with the actual situation of the department, we took NB as an example to analyze further whether NR2F6 was an independent risk factor related to tumor prognosis. We found that the independent risk factors for OS in NB patients were MKI, COG Risk, and NR2F6 expression by multifactorial COX regression analysis (Figure 8A, B). A column-line diagram for predicting OS in NB patients was constructed based on the above independent risk factors, and the C-index of this predictive model was: 0.69,95% CI (0.63-0.75), which demonstrated that the predictive model of the column-line diagram had an excellent discriminatory ability (Figure 8C). The calibration curve showed that the predicted values of the column-line diagram were highly consistent with the actual observed values, indicating that the column-line diagram had good accuracy.AUC was used to test the discriminative ability of the column-line diagram, and the AUC for the 1-, 3-, and 5-year periods were 90.0, 81.0, and 76.0, respectively (Fig. 8D).

IHC in tissue sections suggests that NR2F6 is associated with NB prognosis

Over the past five years, we selected 61 children with retroperitoneal neuroblastoma from Chongqing Children's Hospital for immunohistochemical staining of pathological tissues. We found that the expression of NR2F6 in pathological sections of children with different levels of risk and pathologic staging grades varied and might be associated with prognosis. There is no verifiable threshold because there is no validated information on the evaluation or significance of NR2F6 staining in neuroblastoma. We converted the mean value of staining intensity to a GIS score, with 0 -3 being low expression and 4 -7 being high expression. The distribution of NR2F6 staining intensity is shown in (Figure 9).

Clinical data statistics

We analyzed the clinical data based on the GIS score of NR2F6, as shown in Table 1. The one-way chi-square test suggested no significant difference between the low and high NR2F6 GIS expression groups in terms of age, gender, and prognostic type (P > 0.05). Compared with INSS grading (c ²=19.981，P <0.001, COG score (c ²=14.866，P = 0.001), MYCN gene expression (c ²=10.524，P = 0.001), degree of tissue differentiation (c ²=8.907，P = 0.031), number of tissue metastases (c ²=12.608，P = 0.027) , bone marrow metastasis (c ²=10.370，P = 0.001), and survival status (c ²=31.878，P <0.001) were statistically different.

Table 1 Univariate correlation analysis of NR2F6 gene and clinically relevant risk factors in children with neuroblastoma

		N=61	NR2F6 Low (N=39)	NR2F6 High (N=22)	c ²	P
Age:					7.007	0.328
	<12m	18(29.5%)	14(77.8%)	4(22.2%)
	12-18m	7(11.5%)	4(57.1%)	3(42.9%)
	>18m	36(59.0%)	21(58.3%)	15(41.7%)
Gender					0.766	0.382
	Male	35(57.4%)	24(68.6%)	11(31.4%)
	Female	26(42.6%)	15(57.7%)	11(42.3%)
INSS:
	1	12(19.7%)	12(100%)	0	19.981	<0.001
	2	3(4.9%)	2(66.7%)	1(33.3%)
	3	10(16.4%)	9(90%)	1(10%)
	4	36(59%)	16(44.4%)	20(55.6%)
COG:					14.866	0.001
	Low-risk	13(21.3%)	13(100%)	0
	Poor-risk	10(16.4%)	7(70%)	3(30%)
	High-risk	38(62.3%)	19(50%)	19(50%)
MYCN:					10.524	0.001
	Non-amplify	36(59.0%)	29(80.6%)	7(19.4%)
	Amplify	25(41.0%)	10(40%)	15(60%)
Differentiated:					8.907	0.031
	None	2(3.3%)	1(50%)	1(50%)
	Differential	40(65.6%)	25(62.5%)	15(37.5%)
	Poorly	11(18.0%)	5(45.5%)	6(54.5%)
	Mixed	8(13.1%)	8(100%)	0
Shimada:					2.086	0.149
	FH	21(34.4%)	16(76.2%)	5(23.8%)
	uFH	40(65.6%)	23(57.5%)	17(42.5%)
Organ metastasls:
	0	34(55.7%)	28(82.4%)	6(17.6%)	12.608	0.027
	1	15(24.6%)	6(40%)	9(60%)
	2	3(4.9%)	1(33.3%)	2(66.7%)
	3	3(4.9%)	1(33.3%)	2(66.7%)
	4	3(4.9%)	1(33.3%)	2(66.7%)
	5	3(4.9%)	2(66.7%)	1(33.3%)
Marrow metastasls:					10.370	0.001
	No	43(70%)	33(76.7%)	10(23.3%)
	Yes	18(30%)	6(33.3%)	12(66.7%)
Living condition:					31.878	<0.001
	Alive	37(60.7%)	34(91.9%)	3(8.1%)
	Death	24(39.3%)	5(20.8%)	19(79.2%)

NR2F6 expression level was assessed by immunohistochemical score and German immunohistochemical score (GIS), 0-3: low expression; 4-7: high term. Organ metastasis is the cumulative number of organ metastasis. The final statistical results were considered to be credible with P<0.05.

We used multifactorial logistic regression and multifactorial Cox regression to assess the difference in progression-free survival between NR2F6 and other influences on patient prognosis, the results are presented in Table 2. Multifactorial logistic regression equations were constructed by incorporating age, gender, INSS score, COG score, MYCN gene expression, degree of cell differentiation, prognostic type, number of tissue metastases, bone marrow metastases, and NR2F6 expression. The results revealed that COG score had a statistically significant effect on neuroblastoma outcome regression (OR = 0.34, 95%CI 3.19-538.41, P=0.004); the number of tissue metastases had a statistically significant effect on neuroblastoma outcome regression (OR = 1.63, 95% CI 1.15-2.30, P=0.006); and NR2F6 expression had a statistically significant effect on neuroblastoma outcome regression (OR = 32.46, 95% CI 5.62-187.29, P = 0.006).

Table 2 Multivariate logistic regression analysis of neuroblastoma disease outcome with NR2F6 gene levels and other risk factors

Variable	Group	b	S.E	OR	OR(95%CI)	P
Age:
	<18m *
	≥18m	-1.072	0.471	0.342	0.294-1.359	0.240
Gender
	Male *
	Female	-0.594	0.625	0.552	0.162-1.880	0.324
INSS:
	1 *
	2/3/4	-0.625	0.696	0.535	0.137-2.095	0.369
COG:
	Low-risk*
	Poor-risk
	High-risk	3.724	1.309	41.412	3.185-538.410	0.004
MYCN:
	Non-amplify*
	Amplify	0.676	0.587	41.412	0.623-6.214	0.249
Differentiated:
	None*
	Differential
	Poorly	0.681	0.435	1.975	0.842-4.631	0.117
	Mixed
Shimada:
	FH*
	uFH	-0.982	0.846	0.375	0.71-1.965	0.246
Organ metastasls:
	0*
	1/2/3/4/5	0.489	0.177	1.630	1.153-2.305	0.006
Marrow metastasls:
	No*
	Yes	-0.291	0.551	0.748	0.254-2.202	0.598
NR2F6:	Low*
	High	3.480	0.894	32.462	5.624-187.291	<0.001

P<0.05 indicates that the feature is available

We included P<0.005 in multifactorial logistic regression and critical information that might affect survival in multifactorial Cox regression to exclude confounding factors. We established a multifactorial Cox proportional risk model to predict the regression of the children's disease, the results are presented in Table 3. Age, COG score, MYCN gene expression, tumor tissue differentiation, and NR2F6 expression were included to construct multifactorial linear regression equations. It was found that the COG score (high score compared to low score) had a statistical difference in survival regression (b=0.19,t=0.31, P =0.009).NR2F6 expression status (high expression compared to low expression) had a statistically significant difference in survival regression of the children (b=0.605,t=5.964, P <0.001).

Table 3 Multifactor Cox proportional risk model for survival analysis

Variable	b	S.E	t	P
Age:	-0.028	0.063	-0.448	0.656
COG:	0.191	0.070	0.319	0.009*
MYCN	0.039	0.105	0.369	0.714
Differentiated:	0.049	0.067	0.724	0.472
NR2F6:	0.605	0.101	5.964	<0.001*

* P<0.01, indicating that the risk factor was significantly associated with the disease outcome of the children

In vitro, knockdown of NR2F6 inhibits NB cell proliferation, invasion and migration

To investigate the biological function of NR2F6 in neuroblastoma progression, we performed loss-of-function experiments., the results suggested that the mRNA levels of NB cells were all significantly reduced after the knockdown of NR2F6, and si-NR2F6 3-1 and si-NR2F6 3-2 were selected for subsequent experiments after comprehensive consideration (Figure 10A). The knockdown efficiency was verified at the protein level using the Western blotting (Figure 10B). To verify the effect of NR2F6 on the proliferation of NB cells, we performed a cell viability assay using CCK8, and the results showed that NR2F6 knockdown reduced the viability of SK-N-BE(2) cells in vitro (Figure 10C). We selected the si-NR2F6 3-1 sequence for flow cytometric cell cycle assay, suggesting that the percentage of cells in the G1 phase was significantly increased and the percentage of cells in the S phase was significantly decreased by NR2F6 knockdown, indicating that the cell cycle is blocked in G1 phase. (Figure 10D). The results of the Transwell assay also showed that NR2F6 knockdown significantly reduced the invasive ability of NB cells (Figure 10E). In addition, the scratch assay suggested that the migration ability of neuroblastoma SK-N-BE(2) cells was reduced after NR2F6 knockdown (Figure 10F)

Cancer-related research is currently a prominent focus in medicine. Given that cancer arises from a combination of abnormal cell proliferation, differentiation, and immune stress, identifying genes that can concurrently impact these processes for targeted therapies has become a compelling avenue of investigation. In our literature search, we discovered that NR2F6 exhibits dual tumor-promoting activities in both the immune system and tumor cells. Nevertheless, the role of NR2F6 in pan-cancer remains poorly understood. To address this gap, we conducted gene expression difference analysis using data from 34 cancer-related sources in the UCSC database, revealing a significant up-regulation of NR2F6 in various cancers. Our study thoroughly examined NR2F6 expression in cancer and normal tissues, highlighting significant differences and discussing its potential as a predictive marker.

Neuroblastoma (NB) is a cancer originating from neural crest cells and can develop anywhere in the sympathetic nervous system. It is among the most common malignant solid tumors in childhood and infancy, accounting for approximately 8% of childhood cancers and 15% of childhood cancer-related deaths [15]. NB is a complex disease, exhibiting biological, clinical, morphological, and genetic heterogeneity. Despite modified multimodal therapies, nearly half of high-risk patients do not respond to initial treatments or experience recurrence within the first two years of treatment [16]. The prognosis for high-risk children is grim, with a 5-year survival rate of less than 50% [17]. The variable response of children to standard therapy is largely attributed to multidrug resistance and drug toxicity [18, 19]. Neuroblastoma has thus become a significant health problem and a clinical treatment challenge, seriously impacting children's well-being. Consequently, finding specific therapeutic targets for NB and improving children's survival rate has become an urgent research priority. In light of this, we focused on investigating the role of the orphan nuclear receptor NR2F6 in retroperitoneal neuroblastoma tissues, validating its potential as a prognostic factor and its significance in cancer development.

NR2F6 (Ear-2) is an orphan nuclear receptor with varying expression levels in different standard and tumor tissues. Our study demonstrated that NR2F6 was differentially expressed in most tumors and adjacent normal tissues. Analysis of mRNA expression profiles and protein expression levels revealed significant up-regulation in 25 cancer tissues and significant down-regulation in low-grade glioma (LGG), papillary cell carcinoma of the kidney (KIPAN), and clear cell carcinoma of the kidney (KIRC) tissues, suggesting distinct roles for NR2F6 in the development of different cancers. Furthermore, we evaluated the relationship between NR2F6 expression levels and OS, DSS, DFI, and PFI, finding correlations with the prognosis of various cancers. Previous studies have also shown increased NR2F6 expression in leukemia patients [14], breast cancer with poor prognosis [20], cervical cancer with pelvic lymph node metastasis and poor prognosis [12], and chemotherapy-resistant ovarian cancer [21]. Additionally, NR2F6 has been associated with colorectal cancer progression and hepatocellular carcinoma cell proliferation and metastasis [22, 23]. Together with our pan-cancer analysis, these results suggest that NR2F6 may serve as a potential marker for poor prognosis in different cancers, further indicating its involvement in cancer progression.

In this context, we observed that NR2F6 regulates cell growth at specific stages of differentiation, with overexpression inhibiting granulocyte and monocyte differentiation, while under expression induces granulocyte differentiation [14]. Our immunohistochemistry results aligned with this finding, as poorly differentiated tumor cells exhibited high NR2F6 expression, with a shift from nuclear to cell membrane localization, showing nuclear membrane-positive expression. In nodular cell neuroblastoma, NR2F6 expression was low, primarily concentrated in the nucleus, with significantly reduced expression in the cell membrane. This raises the question of whether nuclear membrane migration of NR2F6 is indicative of tumor progression or related to the cell differentiation mechanism, necessitating further experimental exploration.

NR2F6 is a transcription factor that can influence the tumor microenvironment in various ways. In the inflammatory tumor microenvironment, NR2F6 is highly inducible in effector T cells, locally activating T-cell immunity and reducing immune-related adverse effects [7]. Pan-cancer analysis in our study revealed NR2F6 enrichment in T cells, and considering its dual anti-tumor effects, targeting NR2F6-mediated gene regulation could impact both effector T cells and tumor cells while inducing tumor cell death.

Moreover, our pan-cancer survival analysis demonstrated that low NR2F6 expression was associated with better disease-free survival and overall survival in neuroblastoma patients, showing a similar predictive effect with COG score. Clinical data analysis validation showed a 40-fold increase in the risk of death in children with neuroblastoma for every one-level increase in COG score. NR2F6 expression became a component of a multifactorial Cox proportional risk model for survival analysis after excluding confounding factors, suggesting that NR2F6 could serve as an early indication of disease regression in children, optimizing the accuracy of COG score in neuroblastoma treatment protocols. In cellular experiments, down-regulation of NR2F6 inhibited neuroblastoma cells' proliferation, migration, and invasion, making it a potential therapeutic target for neuroblastoma. These findings provide pre-theoretical support for future clinical validation of NR2F6 targeting as a mechanistic-independent and potentially synergistic option to enhance the efficacy of immuno-oncology therapy.

Analysis of CHD5 expression levels in the pan-cancer dataset

We extracted expression data from the UCSC (https://xenabrowser.net/) database (accessed on May 24, 2023), including TCGA TARGET GTEx (PANCAN, N = 19131, G = 60499). Further, we extracted from it the ENSG00000160113 (NR2F6) gene in the We further extracted the expression data of the ENSG00000160113 (NR2F6) gene in each sample. Further, we screened the samples from Solid Tissue Normal, Primary Solid Tumor, Primary Tumor, Normal Tissue, Primary Blood-Derived Cancer - Bone Marrow, and Primary Blood-Derived Cancer - Peripheral Blood samples. Furthermore, we performed a log2(x + 0.001) transformation for each expression value. Finally, we also excluded the cancer types with less than three samples in a single cancer type, and finally, we obtained the expression data of 34 cancer types. We calculated the expression differences between normal and tumor samples in each tumor using R software (version 3.6.4), and the significance of differences was analyzed using unpaired Wilcoxon Rank Sum and Signed Rank Tests.

Clinical staging and gene expression analysis

We screened the samples from Primary Blood-Derived Cancer - Peripheral Blood, Primary Tumor. We performed a log2(x + 0.001) transformation for each expression value. Finally, we excluded the cancers with less than 3 samples in single cancer, and finally, we obtained the expression data of 26 cancer types. We used R software (version 3.6.4) to calculate the expression differences of genes in each tumor in samples with different clinical stages. We used unpaired Student's t-Test for a two-by-two analysis of the significance of differences and ANOVA to test the differences of samples in multiple groups.

Prognostic correlation analysis

We screened samples from Primary Blood-Derived Cancer - Peripheral Blood (TCGA-LAML), Primary Tumor, Metastatic of TCGA-SKCM, Primary Blood-Derived Cancer - Bone Marrow, Primary Solid Tumor, and Recurrent Blood-Derived Cancer - Bone Marrow samples. In addition, we obtained a high-quality prognostic dataset for TCGA from a previous TCGA prognostic study published in Cell [24] and a high-quality prognostic dataset for TCGA from the Cancer Browser at UCSC ( https://xenabrowser.net/datapages/), supplemented with TARGET follow-up data and excluded samples with a follow-up time shorter than 30 days; furthermore, a log2(x + 0.001) transformation was performed for each expression value, and finally, we also excluded cancers with less than 10 samples in single cancer. Expression data in pan-cancers as well as prognostic data on overall survival OS, disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) of the corresponding samples were obtained. We used the Coxph function of the R package survival (version 3.2-7) to build a Cox proportional hazards regression model [citation: Cox's Regression Model for Counting Processes: A Large Sample Study] to analyze the significance of the NR2F6 gene in predicting OS, DSS, DFI and PFI in pan-cancer, and statistical tests were performed using Logrank test to obtain the prognostic significance and to draw forest plots.

For cancer types with different OS, we calculated the optimal cutoff value of NR2F6 using the R package max stat (Maximally selected rank statistics with several p-value approximations version:0.7–25) and set the minimum group sample size. The optimal truncation value was 4.4958, based on which the patients were divided into two groups. The prognostic difference between the two groups was further analyzed using the surfeit function of the R package survival. The significance of the prognostic difference between the samples of the different groups was assessed using the log-rank test method.

Immunocorrelation analysis

We extracted the expression data of the NR2F6 gene and 60 genes of the 2 types of immune checkpoint pathways (Inhibitory (24), Stimulatory (36), and marker genes derived from the literature [25] in individual samples. We filtered samples originating from Primary Solid Tumor, Primary Tumor, Primary Blood-Derived Cancer - Bone Marrow, and Primary Blood-Derived Cancer - Peripheral Blood, and we also filtered all standard samples and performed a log2 (x + 0.001) transformation for each expression value. Next, we calculated the Pearson correlation between NR2F6 and the marker genes of the five classes of immune pathways.

We extracted the expression data of the NR2F6 gene and 150 marker genes of five types of immune pathways (chemokine (41), receptor (18), MHC (21), Immunoinhibitor (24), Immunostimulator (46)) in individual samples. Furthermore, we screened the sample sources. We further filtered the samples from Primary Solid Tumor, Primary Tumor, Primary Blood-Derived Cancer-Bone Marrow, and Primary Blood-Derived Cancer-Peripheral Blood, and we also filtered all standard samples. We further performed a log2(x + 0.001) transformation for each expression value, and next, we calculated the Pearson correlation between NR2F6 and the marker genes of the five immune pathways.

Mutation-related analysis

We also downloaded the Simple Nucleotide Variation dataset at level 4 of all TCGA samples processed by MuTect2 software [26] from GDC (https://portal.gdc.cancer.gov/). We integrated the mutation data of the samples and obtained the structural domain information from the R package. We integrated the mutation data of the samples and obtained the structural domain information of the proteins from the R software package map tools (version 2.2.10). The STAD mutation landscape contained a total of 414 samples with detected mutations, of which the mapping samples contained a total of 335 (80.9%). The COAD mutation landscape contained a total of 288 samples with detected mutations, of which the mapping samples contained a total of 173 (60.1%). We evaluated the mutations using the chi-square test. We used chi-square tests to assess differences in the frequency of mutations in each group of samples.

Tumor immune infiltration correlation analysis

We screened samples originating from Primary Blood-Derived Cancer - Peripheral Blood (TCGA-LAML), Primary Tumor, Metastatic of TCGA-SKCM, Primary Blood-Derived Cancer - Bone Marrow, Primary Solid Tumor, Recurrent Blood-Derived Cancer - Bone Marrow samples, and we performed a log2(x + 0.001) transformation for each expression value, in addition, we extracted gene expression profiles from them for each tumor separately and mapped the expression profiles to the GeneSymbol, and using the Timer method of the R package IOBR (version 0.99.9, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8283787/) [27], we reassessed the expression profiles of each patient in each tumor based on the gene expression of the B cell, T cell CD4, T cell CD8, Neutrophil, Macrophage, and DC infiltration scores. Using the DECONVO_CIBERSOR method [28], B_cells_naive, B_cells_memory, Plasma_cells,T_cells_CD8,T_cells_CD4_naive,T_cells_CD4_memory_resting,T_cells_CD4_memory_activated,T_cells_follicular_helper,T_cells_regulatory_(Tregs),T_cells_gamma_delta,NK_cells_resting,NK_cells_activated,Monocytes,Macrophages_M0,Macrophages_M1,Macrophages_M2,Dendritic_cells_resting,Dendritic_cells_activated,Mast_cells_resting,Mast_cells_activated,Eosinophils,Neutrophils infiltration score.

Multifactor COX regression analysis and construction of column line graphs

In this study, we evaluated the independent risk factors for NR2F6 in 151 samples using the R package survival, integrating data on survival time, survival status, eight characteristics, and gene expression of NR2F6 using the Cox method. Based on the multifactorial Cox regression analyses, we performed ROC analyses to obtain the AUC using the R package pROC (version 1.17.0.1). Specifically, we obtained the follow-up time of the patients and NR2F6 gene expression. We performed ROC analyses at 365, 1095, and 1825 time points using the roc function of pROC and used the ci function of pROC to evaluate AUC and confidence intervals to obtain the final AUC results. Using the R software package rms, data on survival time, survival status, and three characteristics were integrated, and a nomogram was built using the Cox method to assess the prognostic significance of these characteristics in 151 samples.

Immunohistochemical (IHC) labeling

Tissue sections were provided by the Department of Pathology, Affiliated Children's Hospital of Chongqing Medical University. 60℃ Oven deparaffinization, microwave repair with citrate solution, and 0.5% BSA were closed at room temperature for 1 hour before applying NR2F6 antibody (1:200, Proteintech, 60117-2-lg, China) overnight. Place at room temperature for 30 min to rewarm and incubate goat anti-Mouse IgG-HRP secondary antibody (1:200, Zen-Bio, China) after three washes with PBS. The sections were left at room temperature for 1 hour. Then the sections were washed with PBS, visualized with diaminobenzidine tetrahydrochloride (DAB) staining (Zhongshan Jinqiao, China), rinsed with PBS, and stained with hematoxylin. After that, the sections were dehydrated in standard-graded alcohol, xylene cleared, and sealed with neutral resin. The stained sections were then scanned using a TEKSQRAY slide scanning imaging system (Biostrong Technology, China), after which the slides were evaluated for NR2F6 expression by an experienced pathologist. Five high-magnification fields of view (×400) were randomly observed on each slide. They averaged, and the scoring criteria were based on the German immunohistochemical score (GIS): (1) Staining intensity: no staining of the cells, 0 points; light yellow, 1 point; medium yellow or brown, 2 points; dark brown or tan, 3 points; (2) Proportion of positive cells: no positive cells, 0 points;< 25%, 1 point; less than 50%, 2 points; < 75%, 3 points; > 75%, 4 points. Finally, the two scores were added together: 0–3: low expression; 4–7: high expression.

Clinical data statistics

We retrospectively included children with primary retroperitoneal neuroblastoma who underwent surgical management at the Children's Hospital of Chongqing Medical University from January 2017 to May 2023. This study was approved by the Ethics Committee of Chongqing Medical University:(2022) Nianlun Audit (Research) No. (170). The Declaration of Helsinki conducted all studies—informed consent of legal guardians for children under 14. Exclusion criteria were no surgical treatment, patient consent, and insufficient tumor tissue. Sixty-one patients largely met the inclusion criteria. We reviewed the medical history, surgical details, histology, tumor staging, and postoperative data. Statistical analysis was performed using IBM SPSS 25.0 window statistics (SPSS Statistics, Statistics 25, IBM Corporation, Armonk, NY, USA). We used chi-square for univariate and NR2F6 simple correlation analysis. Survival data multivariate analyses were performed using multifactorial logistic regression and multifactorial Cox regression. p ≤ 0.05 was considered to be different.

Cell lines and cell culture

Neuroblastoma cell line SK-N-BE (2) was purchased from the cell bank of the Chinese Academy of Sciences (Procell Life Science༆Technology Co., Ltd., China). DM-EM basal medium was used, adding 10% serum (GIBCO fetal bovin serum, New Zealand) and 1% antibiotics (100 mg/mL streptomycin and 100 U/mL penicillin). The cell incubator was set up at 37℃ with CO2 of 5%.

Cell transfection

Negative control (NC) siRNA and siRNA targeting NR2F6 were purchased from Qingdao MDBio, Inc.(China).6-well cell culture plates were inoculated with 3 × 10⁵ SK-N-BE (2) cells per well. Based on transfection of this oligomer into SK-N-BE (2) according to the manufacturer's protocol, using Lipofectamine 2000 reagent (Invitrogen, CA, USA) [10]. 5µM of Si-NR2F6 3 − 1, Si-NR2F6 3 − 2, Si-NR2F6 3–3 and the corresponding controls ( Genepharma, Shanghai, China) were transfected into cells. After 6 hours of transfection, the medium was changed to a complete medium, and the cells were cultured until 48 hours. Cells were collected, and the knockdown efficiency of NR2F6 was detected by western blotting and qPCR, respectively. Follow-up experiments were completed. The RNA sequences were as follows in Table 4:

Table 4

NR2F6 RNA primer sequence
Gene name	Sense(5’-3’)	Antisense(5’-3’)
h-NR2F6 3 − 1	CGGCAAGCAUUACGGUGUCUUTT	AAGACACCGUAAUGCUUGCCGTT
h-NR2F6 3 − 2	CGUGGCUUUCAUGGACCAGGUTT	ACCUGGUCCAUGAAAGCCACGTT
h-NR2F6 3–3	UGAGACACUGAUCAGAGACAUTT	AUGUCUCUGAUCAGUGUCUCATT

The design and production of Si-RNA primers for NR2F6 came from Qingdao MDBio, Inc.(China).

Reverse transcription-polymerase chain reaction (RT-PCR)

Total RNA from transfected SK-N-BE(2) cells were extracted according to the method provided in the instructions for 250 TRIzol reagents (Invitrogen, CA, USA). Isolated total RNA was reverse transcribed for qPCR according to the instructions for RT Master Mix (MCE, NJ, USA). Reactions were performed using SYBR Green qPCR Master Mix (MCE, NJ, USA). The quantities were detected using the Bio-Rad CFX linkage real-time system. We used the expression level of GAPDH mRNA as a reference [28]. The sequences of primer bars used for NR2F6 and GAPDH are shown in the following table comparison of the Ct method to calculate mRNA expression. Sub-replicates were set up for all experimental samples. qRT-PCR analysis primers were synthesized by Sango Biotech (Shanghai, China) with the following sequences:

NR2F6: Forward primer: 5’-GGGGCTCTCCAGAACATC-3’; Reverse primer: 5’-TGACACGTTGGCAGTGG − 3’

GAPDH: Forward primer: 5’-GCCGCAACCTCAGCTACA-3’; Reverse primer: 5’-CACCGCCTCCTTCCTCA-3’

Western blotting

Cells were lysed in RIPA buffer (P0013C; Beyotime) supplemented with 0.1mM phenylmethanesulfonyl fluoride (329-98-6; MedChemExpress). Centrifuge the lysed cells. Mitochondrial and cytoplasmic proteins were extracted separately using the Mitochondrial/Cytoplasmic Protein Separation Kit (BioWorks, China), following the manufacturer's instructions. Equal amounts of proteins were subjected to 6%-12.5% SDS-PAGE ((EpiZyme, Shanghai, China)) and then transferred to a polyvinylidene fluoride membrane (microtiter). The membranes were blocked with 5% fat-free milk. Immunoblotting was performed with antibodies targeting the following proteins: GAPDH(ZENBIO, Shanghai, China); NR2F6(Proreintech,China)。 The secondary antibodies were obtained from ZSGBBio (Shanghai, China). The enhanced chemiluminescence substrate (34075; Thermo Scientific, Waltham, USA) was used to visualize the protein bands. The western blot images shown are representative of three independent repeats[29].All bands used in WB were from different molecular weight positions of the same gel without any degree of exposure adjustment. Statistical data was obtained from the internal reference ratio of the bands of each entry to the same gel, and three statistical replicates were performed. The images were acquired using the ChemiDoc MP Imaging System (Bio-Rad), and densitometry analysis was performed using the Image Lab software (Version 3.0)—Photoshop CS6 software for overall brightness adjustment and annotation addition. .

Cellular phenotypes

The CCK-8 assay (HY-K0301; Med Chem Express, China) and EdU (5-ethyl-2′-deoxyuridine) (C0085S; Beyotime, China) were used to detect cell proliferation. The experimental and control groups were inoculated in 96-well plates, and 5×103 cells were inoculated in 100 µL culture medium with 10% fetal bovine serum added to each well. CCK-8 reagent was added at 0h, 24h, and 48h and incubated at 37◦ C for 2 h. Finally, optical density (OD) was measured at 450 nm wave using an enzyme labeler (Bio-Rad, Harklues, USA). Cell viability was calculated by OD treatment/OD control. The scratch assay was performed by inoculating the experimental and control groups in 6-well plates with 5×105 cells per well and scratching vertically with a 1ul tip. The photos were taken at 0h, 24h, 48h, and 72h after washing with PBS and fixed, and then changed to serum-free medium. After taking photos at each time point, the gap area was calculated by ImageJ to take the mean value to count the differences. Before the Transwell experiment, 50µL matrigel gel was spread in the chamber and placed in the incubator at 37℃ for 30min to solidify.5×10⁴ cells of each of the experimental group and the control group were inoculated in the Transwell chamber, and 800µl of complete culture solution with 10% FBS was added to the lower layer of the 24-well plate. FBS complete culture solution, three replicate wells of each group, placed in the 37℃ incubator for 48h and then paraformaldehyde fixed at room temperature for 10min; Aspirate off the fixative, wash once with one xPBS, add 1mL 0.5% crystal violet solution to each well, stain for 30min and then wash three times with 1xPBS, air dry; Carefully wipe off the cells that did not migrate within the Transwell chamber with a cotton swab, place under a 200x microscope and observe. 200x microscope to observe and count the number of cells in each field of view after counting. Flow cycle experiments were performed by digesting the cells after 48 hours of transient rotation and centrifuging them routinely, resuspending them in pre-cooled PBS by blowing and resuspending them repeatedly to disperse them sufficiently; the washing was repeated once in PBS. Add 3ml of cold 75% ethanol, resuspend, shake the shaker for 10 seconds, and fix at 4 degrees for more than 24 hours. Centrifuge the precipitate, discard the supernatant, add 4ml of PBS to thoroughly wash once, shake the shaker for 8 seconds; centrifugation, resuspend the cells in 500µl of PI staining solution, mix thoroughly, and let them stand at room temperature for 30min before going on the machine.

Statistical analysis

Data were analyzed, and statistical graphs were created using SPSS 25.0 and the graphics board Prism 8. All measurements were expressed as mean ± standard deviation (mean ± SD) and compared using a t-test. The rank sum test was used for sorted data. p < 0.05 was considered a statistically significant difference.

In this study, our comprehensive analysis of NR2F6 expression in pan-cancer led to several significant findings. Firstly, NR2F6 emerges as a tumor-promoting factor across various cancer types, and its expression is closely linked to patient prognosis, with higher expression associated with reduced survival times. Additionally, NR2F6 plays a distinctive role in the tumor immune microenvironment, influencing immune infiltration within tumors. We further validated the pan-cancer analysis results using pathological tissue sections and clinical data, highlighting the critical impact of NR2F6 on overall and progression-free survival in neuroblastoma (NB), where NR2F6 overexpression was correlated with poorer prognosis in children with neuroblastoma.

Furthermore, our in vitro cellular experiments provided substantial evidence supporting the notion that NR2F6 knockdown may reduce the malignant biological behavior of neuroblastoma cells. These collective findings position NR2F6 as a promising biomarker and potential therapeutic target for predicting neuroblastoma prognosis. However, to fully grasp the mechanisms underlying NR2F6's actions, further investigation is warranted. Understanding NR2F6's role could open new avenues for the diagnosis and treatment of neuroblastoma, ultimately offering novel strategies to improve patient outcomes.

Ethics approval and consent to participate

All studies involving human tissues were conducted in strict accordance with the Declaration of Helsinki and were approved by the Ethics Committee of Chongqing Medical University:(2022) Nianlun Audit (Research) No.(170).This study involved cases of children under the age of 14, all of whom had obtained the informed consent of their legal guardians. All participants were informed and consented to sample collection, intended research, and publication usage. Written consent was collected according to the ethical regulations of the Children's Hospital of Chongqing Medical University.

Consent for publication

Not applicable.

Competing interests

The authors declare no conflict of interest.

Funding

Chongqing Science and Technology Commission, CSTB2022TIAD-STX0013

Authors' contributions

Yimeng Liu: Conceptualization, Data curation Methodology, Validation, Formal analysis, Writing - Original Draft, Visualization. Zhaoxia Zhang: Methodology, Writing -Original Draft. Tao Mi: Software, Data Curation. Liming Jin:Investigation,Visualization..Zhaoying Wang: Visualization. Mujie Li: Validation, Formal analysis. Jinkui Wang: Visualization. Xin Wu: Investigation. Junyi Luo: Investigation. Jiayan Liu: Investigation . Chunnian Ren: Formal analysis.Dawei He: Resources, WritingReview & Editing, Supervision, Project administration and Funding acquisition.

All authors read and approved the final version of the manuscript.

Acknowledgments

Not applicable

Availability of data and material

All data in our study are available upon request.

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No competing interests reported.

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NR2F6 promotes the malignant progression of neuroblastoma as an indicator of poor prognosis

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Abstract

Background

Methods

Results

Impact:

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Introduction

Results

Discussion

Materials and methods

Analysis of CHD5 expression levels in the pan-cancer dataset

Clinical staging and gene expression analysis

Prognostic correlation analysis

Immunocorrelation analysis

Mutation-related analysis

Tumor immune infiltration correlation analysis

Multifactor COX regression analysis and construction of column line graphs

Immunohistochemical (IHC) labeling

Clinical data statistics

Cell lines and cell culture

Cell transfection

Reverse transcription-polymerase chain reaction (RT-PCR)

Western blotting

Cellular phenotypes

Statistical analysis

Conclusion

Declarations

References

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Supplementary Files

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