
==== Front
Discov Oncol
Discov Oncol
Discover Oncology
2730-6011
Springer US New York

39235657
1286
10.1007/s12672-024-01286-5
Analysis
Single-cell RNA sequencing explored potential therapeutic targets by revealing the tumor microenvironment of neuroblastoma and its expression in cell death
Sun Lei 1
Shao Wenwen 1
Lin Zhiheng 1
Lin Jingheng 1
Zhao Fu 1
Yu Juan yujuan196804@163.com

2
1 grid.464402.0 0000 0000 9459 9325 Shandong University of Traditional Chinese Medicine, Jinan, 250014 Shandong China
2 https://ror.org/0523y5c19 grid.464402.0 0000 0000 9459 9325 Pediatric Tuina Health Care Clinic, Shandong University of Traditional Chinese Medicine Affiliated Hospital, No. 16369, Jingshi Road, Jinan, 250014 Shandong China
5 9 2024
5 9 2024
12 2024
15 4093 7 2024
28 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Neuroblastoma (NB) is the most common extracranial solid tumor in childhood and is closely related to the early development and differentiation of neuroendocrine (NE) cells. The disease is mainly represented by high-risk NB, which has the characteristics of high mortality and difficult treatment. The survival rate of high-risk NB patients is not ideal. In this article, we not only conducted a comprehensive study of NB through single-cell RNA sequencing (scRNA-seq) but also further analyzed cuproptosis, a new cell death pathway, in order to find clinical treatment targets from a new perspective.

Materials and Methods

The Seurat software was employed to process the scRNA-seq data. This was followed by the utilization of GO enrichment analysis and GSEA to unveil pertinent enriched pathways. The inferCNV software package was harnessed to investigate chromosomal copy number variations. pseudotime analyses involved the use of Monocle 2, CytoTRACE, and Slingshot software. CellChat was employed to analyze the intercellular communication network for NB. Furthermore, PySCENIC was deployed to review the profile of transcription factors.

Result

Using scRNA-seq, we studied cells from patients with NB. NE cells exhibited superior specificity in contrast to other cell types. Among NE cells, C1 PCLAF + NE cells showed a close correlation with the genesis and advancement of NB. The key marker genes, cognate receptor pairing, developmental trajectories, metabolic pathways, transcription factors, and enrichment pathways in C1 PCLAF + NE cells, as well as the expression of cuproptosis in C1 PCLAF + NE cells, provided new ideas for exploring new therapeutic targets for NB.

Conclusion

The results revealed the specificity of malignant NE cells in NB, especially the key subset of C1 PCLAF + NE cells, which enhanced our understanding of the key role of the tumor microenvironment in the complexity of cancer progression. Of course, cell death played an important role in the progression of NB, which also promoted our research on new targets. The scrutiny of these findings proved advantageous in uncovering innovative therapeutic targets, thereby bolstering clinical interventions.

Keywords

Neuroblastoma
Neuroendocrine cells
Single-cell sequencing
Important subgroups
New targets
Cell death
issue-copyright-statement© Springer Science+Business Media, LLC 2024
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pmcIntroduction

The tumor microenvironment (TME) has a significant impact on tumor progression and treatment. This article focuses on the components of the TME and its complex mechanisms in cancer [1, 2]. Neuroblastoma (NB) is a solid neuroendocrine tumor as an embryonic tumor of the nervous system [3]. It mainly occurs in early childhood. Over half of the diagnosed NB patients are infants under two years old. NB accounts for approximately 8–10% of childhood malignancies, with a mortality rate of about 15%.

Tumors can arise in any location where the sympathetic nervous system is present. Most NB primarily originates in the adrenal medulla but can also originate in the abdominal sympathetic ganglia, thoracic and cervical regions, or pelvic sympathetic ganglia. NB is thought to originate from trunk neural crest cells along the dorsal aorta, also known as sympathoadrenal progenitor cells [4], and are closely related to the early development and differentiation of NE cells [5].

NB is a highly malignant tumor, ganglioneuroblastoma (GNB) exhibits moderate malignancy, and ganglioneuroma (GN) is considered a benign tumor [6]. NB, mixed GNB, and mature GN symbolize the continuum from immature to mature stages of NB [7, 8]. NB shows significant clinical heterogeneity; it may differentiate into a benign tumor and gradually degenerate, or it may transform into a highly invasive malignant tumor and spread widely [9–11]. Moreover, the International NB Risk Group staging system divides patients into very low-risk, low-risk, intermediate-risk, and high-risk groups. Children with low risk have a good prognosis, with a 5-year survival rate of over 85%. However, the survival rate for high-risk NB patients is less than 50% [12]. Although intensive multimodal approaches (induction chemotherapy, surgical tumor resection, combining high-dose chemotherapy with autologous stem cell transplantation, differentiation agents, and anti-GD2 monoclonal antibody immunotherapy) have been employed over the past few decades to treat high-risk NB patients, this was still not ideal [13]. Additionally, the high recurrence rate among high-risk NB patients remains a critical issue that requires urgent attention.

The success of anti-GD2 therapy highlights the potential of immunotherapy in the treatment of NB. Identifying new immunotherapy targets is crucial, necessitating further exploration of the highly complex TME [14]. Cell death plays a critical role in biological development and homeostasis maintenance by removing damaged or obsolete cells [15, 16]. Programmed cell death (PCD) is vital for cancer treatment and is becoming the focus of our research, including autophagy, pyroptosis, necroptosis, ferroptosis, cuproptosis, parthanatos, and lysosome-dependent cell death [17–19]. Different cell death modes and treatment strategies are not the same. Cuproptosis is a new apoptosis process. Some studies have shown that it plays an important role in the development of NB and is associated with survival prognosis that still needs further verification. This article studies cuproptosis to promote the development of more effective therapeutic interventions.

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for elucidating TME and tumor heterogeneity. It plays an important role in exploring the cellular heterogeneity of various tumor tissues, such as ovarian cancer, microglioma, etc. Numerous studies have confirmed the significant impact of TME on NB progression, treatment, and prognosis. Despite extensive analyses, substantial gaps remain in understanding the malignant mechanisms of NB, as well as in identifying prognostic biomarkers and therapeutic targets [20]. Intensifying research on NB is imperative for developing safer and more effective treatments to guide clinical practice.

Therefore, the main purpose of this article is to conduct extensive scRNA-seq analysis of NB and elucidate the transcriptome characteristics of NE cells. Studies have shown that NB has the molecular characteristics of NE cells and neurons. Compared with normal cells, NE cells have increased levels of chromosome CNV, proving that they represent a malignant cell type. C1 PCLAF + NE cells are considered to be a key subpopulation due to their high specificity in cell cycle, cell development trajectory, etc. Based on these characteristics, new immunotherapy targets and prognostic biomarkers are found to improve the survival rate and quality of life of NB patients.

Methods

scRNA-seq data source

The scRNA-seq data for NB was sourced from the Gene Expression Omnibus (GEO) database (https //www.ncbi.nlm.nih.gov/geo/). The NB-related GSE216176 dataset was downloaded from the GEO public database.

Quality control of scRNA-seq data

The “Seurat” software package [21–24] was utilized to analyze and process the scRNA-seq data. Then we performed quality control (QC). We used the R package DoubletFinder [8, 25] to filter and remove low-quality cell data, including doublet cells and multi-cellularity [26, 27]. The screening conditions are as follows: (1) Feature_RNA less than 300 or more than 7500. (2) nCOUNT less than 500 or more than 10,000. (3) mitochondrial percentage exceeding 25%. (4) red blood cell value exceeding 5%0.5) unique molecular identifiers (UMIs) less than 1000. It ensured the reliability and accuracy of the analysis, enabling better identification of genuine biological differences.

Dimension reduction, clustering, and visualization

We utilized the “NormalizeData” [11, 12] function for normalizing the scRNA-seq data and employed the “FindVariableGenes” function to pinpoint the hypervariable genes (HVG), yielding 2000 hypervariable genes. By focusing on highly variable genes, the signal-to-noise ratio of downstream analyses was enhanced, leading to more accurate cell classification and subpopulation identification. Following this, the data was standardized using the “ScaleData” function, and principal component analysis (PCA) [13] was conducted to reduce the dimension representing each cell. Afterwards, the Harmony method was applied to mitigate batch effects. We visualized the cells and genes using post-dimensionality reduction clustering and exhibited them using uniform manifold approximation and projection (UMAP).

Marker genes

We used the CellMarker website (http://biocc.hrbmu.edu.cn/CellMarker/) [28] to identify marker genes in cell subsets. Furthermore, marker genes within cell clusters were discerned by referencing the average expression levels of marker genes documented in prior literature.

Differentially expressed genes

We set the parameters as Threshold = 0.25, min.pct = 0.25, min.difft.pct = 0.25. Then the "FindAllMarkers" function within the “Seurat” R package was utilized to find differential genes across different cell types in NB patients. The screening criteria for differentially expressed genes (DEGs) were as follows: 1. This gene belonged to a cell subgroup with a logFC value exceeding 0.25; 2. Over 25% of cells within the subgroup exhibited expression of this gene. 3. P-value was less than 0.05.

GO enrichment analysis, GSEA, and AUCell analysis

The process of annotating and classifying DEGs was called enrichment analysis [29]. GO enrichment analysis and GSEA belonged to the downstream analysis of DEGs. We used the “ClusterProfiler” R package to perform Gene Ontology (GO) biological process (BP) enrichment analysis [14] to explore the biological processes and molecular functions of DEG-related significant enrichment in each cell type [26, 27, 30–32]. Gene set enrichment analysis (GSEA) [22, 33, 34] was employed to discern gene sets manifesting consistency and notable discrepancies between two biological states, thereby enhancing comprehension of biological phenomena.

AUCell could be employed to identify the active gene set in the single-cell dataset and to score each cell based on the gene set as a whole, thereby evaluating its activity. This approach allowed for the acquisition of crucial information regarding the activity level of the gene set.

SCENIC analysis

SCENIC [35] was employed to reconstruct the gene regulatory network in scRNA-seq data and elucidate the cell states. We used the Python package pySCENIC [36] for analysis to infer the enrichment of transcription factors and the activity of regulatory factors in NB. The AUCell algorithm was utilized to assess the activity of cell regulatory factors, and the top 5 TFs with the highest scores were selected.

CNV situation

InferCNV (https://github.com/broadinstitute/inferCNV/wiki) [37] was used to infer gene CNV in scRNA-seq, such as chromosome amplification or deletion, and is often used to identify malignant cells. Compared with normal cells, the genome of malignant cells will be overexpressed or underexpressed on a large scale. InferCNV was employed to compute the variation in chromosome copy numbers among NE cells from tumor samples, utilizing NK-T cells as a reference point. This comparison was conducted to evaluate discrepancies in CNV between NE cells and normal cells, aiming to judge the malignant degree of cells.

The pseudotime trajectory analysis of NE cells

Leveraging scRNA-seq data, we employed the CytoTRACE software package to ascertain the stemness of cellular differentiations. Further, we utilized the Monocle2 [38] software package to chart single-cell trajectories, thereby aiding in the deduction of cellular differentiation sequences. To analyze the changes of cells in the progression of cancer. Subsequently, we turned to the Slingshot software package to delineate cellular differentiation lineages. To summarize, various cellular subsets were arranged according to their differentiation sequences to elucidate the progression of NB.

Cell communication in tumor samples

CellChat [39] was a database of interactions among ligands, receptors, and cofactors. It was employed to infer and analyze relevant intercellular communication networks from scRNA-seq data.

Statistical analysis

In our study, we employed the Wilcoxon's test and Students' t-tests [40–42]. Furthermore, we selected data with statistical significance indicated by P-values < 0.05, including: *P < 0.05, **P < 0.01, ***P < 0.001.

Results

Identification and analysis of NB large groups

We analyzed single-cell transcriptome sequencing data from GSE216176. The data were processed using 10 × Genomics, and following rigorous quality control and filtration, 87,274 cells were chosen for analysis. These cells were visualized using the UMAP and classified into 35 Seurat clusters using unsupervised clustering (Fig. 1A). The cell clusters were differentiated based on three primary histological types, including GNB, GN, and NB, with NB exhibiting a significant dominance (Fig. 1D). Cell types were identified using marker genes, and eight main types were recognized, namely B-plasma, T-NK, Myeloid cells, NE cells, Proliferating cells, pCDS, Hematopoietic precursor cells, and Erythroid lineage cells (Fig. 1C). Additionally, we analyzed the cell cycle of NB and observed that most cell types were in the G1 phase, while the lower right corner (mainly NE cells and Proliferating cells) predominantly occupied the G2M and S phases (Fig. 1B). This indicated heightened cell activity. It also showed the distribution of patient samples (Fig. 1E). A correlation analysis was performed to determine the proportion of cell types among all patient samples corresponding to the three primary histological types. It was found that NE cells were scarce in GNB and GN, but they were more prevalent in NB. Notably, M2a and M3 samples exhibited a distinct specificity in terms of NE cell proportion (Fig. 1F). Comparing high-malignant NB with low-malignant GNB and benign GN, it became evident that NE cells were exclusive to NB, which piqued our interest. Furthermore, the proportions of the eight cell types in NB, GNB, and GN were compared. NE cells were predominantly present in NB, and this specificity was highly significant with a P-value of 0.033, consistent with the information presented in the figure (Fig. 1H). We also studied the G2M score and S score and observed that NE cells showed higher values, indicating that the cells were in an active state of excessive division and proliferation (Fig. 1G). The gene enrichment items of NB cell types were also depicted using the word cloud maps. The primary enrichment item of Myeloid cells was leukocytes, while B-plasma cells primarily enriched immune-related substances. Erythroid lineage cells participated in transition and cycle-related processes. Hematopoietic precursor cells were primarily associated with localization, and NE cells were significantly involved in localization items. T-NK cells primarily enriched the leukocyte item (Fig. 1I). Additionally, we showcased differentially up-regulated genes and down-regulated genes of these cell types (Fig. 1J). Based on existing literature data, we further proposed the marker genes associated with these cell types. B-Plasma cells highly expressed IGKC, IGLC2, IGHM, IGLC3,and CD79A; T-NK cells highly expressed GNLY, NKG7, CCL5, IL32,and IL7R; Myeloid cells highly expressed S100A8, S100A9, LYZ, FCN1,and CST3; NE cells highly expressed GAL, DDX1, CCND1, STMN2,and MEG3; Proliferating cells highly expressed TUBA1B, IGLL1, HIST1H4C, STMN1,and HMGB2; pCDS highly expressed JCHAIN, ITM2C, GZMB, PLD4,and PPP1R14B; Hematopoietic precursor cells highly expressed AVP, SPINK2, PRSS57, FAM30A,and C1QTNF4; Erythroid lineage cells highly expressed HBB, AHSP, HBD, CA1,and HBA1 (Fig. 1K).Fig. 1 Illustration of NB Cell Subsets. A The UMAP diagram revealed the 35 Seurat clusters within the NB sample, where distinct colors denoted various cell subsets. B The UMAP diagram delineated the G1, G2M, and S phases of all cells. Blue signified G1, orange represented G2M, and green signified S. C The UMAP images portrayed all cell types in the NB samples, each color signifying distinct cell subsets. D The UMAP map showed the grouping in the NB sample, with green representing GNB, blue representing GN, and purple representing NB. E The UMAP diagram illustrated the sample distribution among NB patients. F Bar charts indicated the proportions of the GNB, GN, and NB subgroups within different patient samples. G Violin plots showcased G2M scores of each cell type in the NB sample, alongside S.Score expression. H Box plots demonstrated the proportions of different groups (left) and different patient samples (right) within each cell type. *P < 0.05; **P < 0.01; ***P < 0.001; and ****P < 0.0001; ns indicated no significant difference. I. The word cloud maps displayed the GO enrichment analysis results for various cell types. The color went from blue to yellow; the higher the zscore. J Volcano plots exhibited differentially expressed genes across distinct cell types. K Bubble chart showcased the top five Marker genes in various cell types and groups. The intensity of yellow color indicated Z-score, while point size correlated with percentage

The related information about NE cells

There was a relationship between NE cells and the malignant degree of NB. To verify this hypothesis, we conducted an in-depth study of NE cells using scRNA-seq data. Initially, we identified four subsets of NE cells based on marker genes: C0 RGS4 + NE cells, C1 PCLAF + NE cells, C2 PAGE2 + NE cells, and C3 BIRC5 + NE cells. We visualized the scRNA-seq data using UMAP, presenting Seurat clusters, phases, groups, and orig.ident. Notably, the C1 subgroup was predominantly placed in the G2M and S phases, indicating its highly active state (Fig. 2A). To assess the malignancy of the cells, we performed inferCNV analysis and determined NE cells were malignant cells using T-NK cells as reference cells. And the malignancy degree of the four NE cell subsets was further determined. We observed chromosomal alterations such as Chr2 gain (C1-C4), Chr7 gain (C0, C1, C3), and Chr17 gain or Chr1 loss (Fig. 2B). The CNV indicated a high degree of malignancy in NE cells, particularly in the C1 subgroup, which displayed pronounced specificity and held significant research value. Subsequently, we showed the differential genes of the four subgroups (Fig. 2C). Moreover, the specific expression levels of marker genes in these subgroups were also described (Fig. 2D). The word cloud map showed the enrichment of differential genes in each subgroup. Notably, the GATA3 was primarily enriched in the C0 subgroup, while the C1 subgroup highly enriched the AURKB and CDT1. The NRP1 was most significant in the C2 subgroup, while the CCNB1, CDCAB, and BIRC5 were highly enriched in the C3 subgroup (Fig. 2E). AURKB promoted the transition from metaphase to telophase, effectively prolonging telophase and facilitating malignant cell proliferation and deterioration [43]. Similarly, CDT1 played a crucial role in maintaining cell cycle and genome stability [44]. To study further, we employed the bubble chart to demonstrate the tumor stem genes of the four subgroups and their relation to the G1, G2M, and S stages. Notably, the KDM5B and CD44 genes were primarily associated with the G1 phase, while the CTNNB1 and HIF1A genes were linked to the G2 phase, and the NES and SOX2 genes were connected to the S phase (Fig. 2F, G). These findings provided insights into the development of NB.Fig. 2 Presentation of NE Cell Subsets. A Display of different Seurat clusters, sub-clusters, groups, stages, and different patient samples in NE cells. Each color represented a distinct meaning. B Based on the chromosome CNV map of scRNA-seq, displayed the chromosome CNV of NE subgroups (C0-C3), with NK-T cells as reference. The X-axis represented the chromosome variation region, and the Y-axis represented NE subgroups. C Volcano maps of the top 5 up-regulated and top 5 down-regulated genes in NE cell subsets. D Expression bubble diagram of the top five marker genes in NE cell subsets. The intensity of yellow color indicated Z-score, while point size correlated with percentage. E The word cloud maps showcased gene enrichment of NE cell subsets. Font size indicated the amount of gene enrichment, while color was related to the Score of the gene. F Bubble chart displayed the expression of differential genes in NE cell subsets. G Bubble chart displayed the expression of differential genes in Phases. The average expression of genes transitioning from yellow to green decreased, and the larger the dot, the greater the percentage

The differentiation trajectory of NE cells

In Fig. 3, we explored the differentiation of NE cells to gain a comprehensive understanding of tumor progression. We visually represented the total pseudotime situation of the NE cell subgroups using UMAP and described their differentiation trajectories using ridge diagrams. The expression of the C0, C1, and C3 subgroups displayed relative uniformity during tumor differentiation, while the C2 subgroup exhibited higher expression in the later stage of the differentiation trajectory, featuring a distinct peak (Fig. 3A–C). We employed UMAP visualization, bar charts, and violin plots to elucidate the expression between subgroups, states, and phases. We found that the C1 subgroup mainly fell under states 1, 2, and 5 (Fig. 3D, E). The pseudotime differentiation order was determined to be C0, C3, C1, and C2 (Fig. 3F). Regarding the phases, the C1 subgroup was chiefly present in the S and G2M phases (Fig. 3G, H). The differentiation order in terms of the phases was G1, G2M, S (Fig. 3I). The G2M and S phases corresponded to DNA synthesis and cell mitosis, crucial stages in tumor cell proliferation. The high proliferation characteristics of the C1 subpopulation indicated that it was in an active state of excessive division and proliferation to promote the generation and progression of NB. We showed the general pseudotime differentiation trajectory (Fig. 3J). The overall pseudotime trajectory (Fig. 3K), with the upper left corner as the starting point, differentiated to the lower right to the differentiation point 1, which was called state 1. From differentiation point 1, it was divided into two branches, and one branch differentiated to the upper right corner, which was called state 2; the other branch differentiated to the lower left corner to differentiation point 2, which was called state 3; a branch extending from differentiation point 2 to the lower left was called state 4; and a branch extending from the lower right was called state 5. Furthermore, we showcased the differentiation trajectories of nomenclature genes in four subgroups and their dynamic expression changes during differentiation (Fig. 3L–N). Heatmap displayed the dynamic changes of marker genes throughout the entire pseudotime differentiation trajectory, observing gradual increases in the expression of most marker genes in clusters 1, 2, and 4, while the expression of most marker genes in cluster 3 decreased progressively (Fig. 3O). These analyses provided insights into the differentiation kinetics and transcriptional changes in NE cells during NB progression.Fig. 3 Pseudotime Analysis of NE Cells. A Visualized the complete progression of NE cells using UMAP. The color spectrum transitioned gradually from ebony to crimson, delineating the chronological sequence of cellular differentiation. B–C The ridge diagrams illustrated the variation of four subgroups of NE cells. D The UMAP depiction demonstrated the distribution proportions of four NE cell subgroups across cell states. E A bar chart delineated the distribution proportions of four NE cell subgroups within the cell states. F A violin plot depicted the pseudotime trajectory characteristics of the four NE cell subgroups. G The UMAP illustration exhibited the cell phases in the four NE cell subgroups. H A bar chart illustrated the cell cycle proportion in the four NE cell subgroups. I Violin plot showcased the cell cycle proportion in the four NE cell subgroups. J The diagram of pseudotime trajectory delineated the trajectory of NE cells. K Trajectory analysis showcased the pseudotime trajectory of NE cells in each state. L The pseudotime trajectory of each NE subgroup was presented and color-coded according to cell type. M The pseudotime trajectory displayed of four NE cell subgroups (RGS4, PCLAF, PAGE2, BIRC5). N Scatter plots exhibited the dynamic changes of named genes in NE cells within the pseudotime trajectory.The expression of RGS4 gradually diminished along the trajectory. O Heatmap displayed the dynamic changes of marker genes in NE cells within the pseudotime trajectory. The X-axis represented the pseudotime trajectory, while the Y-axis denoted the C0-C3 subgroup of NE cells

CytoTRACE and slingshot display of NE cells

Moving on to Fig. 4, we utilized CytoTRACE to predict the cell stemness in NE cells. The prediction order was found to be C1, C3, C2, and C0. The C1 subgroup exhibited a low degree of differentiation but high cell stemness, while the C0 subgroup displayed a high degree of differentiation but low cell stemness (Fig. 4A, B). Several genes, including RPS2, RPL35, and Ybx1, were found to be positively correlated with CytoTRACE, whereas genes like PPP1R1a, VSNL1, and CYB561 were negatively correlated (Fig. 4C). Furthermore, we employed Slingshot to analyze the trajectory and obtained two linear differentiation trajectories. Lineage 1 followed the order C1, C0, and C3, while Lineage 2 proceeded as C1, C0, and C2. These trajectories were validated using the UMAP plots, which depicted the transition from blue to red gradually (Fig. 4D). Both linear differentiation trajectories began with the C1 subgroup, consistent with the CytoTRACE predictions. We examined the linear differentiation trajectorys of named genes, visually presenting their expression changes during differentiation (Fig. 4E). Additionally, a heatmap was used to demonstrate the dynamic expression changes of genes and enrichment pathways in Lineage 1 and Lineage 2 (Fig. 4F). Overall, these findings shed light on the differentiation trajectories and gene expression patterns of NE cells in NB.Fig. 4 CytoTRACE and Slingshot Analysis of NE Cells. A The left side exhibited the projected CytoTRACE scores, while the right side showcased the coloration of each NE cell subgroup. B A box plot illustrated the differentiation potential of four subsets of NE cells. Cell sequences were C1, C3, C2, and C0, respectively. C A bar chart visualized the correlation of CytoTRACE-associated genes, with red indicating a positive correlation and blue indicating a negative correlation. D The Slingshot diagrams and UMAP visualizations depicted the trajectories of four NE cell subgroups, divided into lineage 1 and lineage 2. The gradual color transition from red to blue signified the direction of cellular differentiation. E The Slingshot maps of NE cells highlighted the expression patterns of named genes within each subgroup, partitioned into lineages 1 and 2. F The heatmaps illustrated the dynamic alterations in genes and GOBP enrichment pathways across the subgroups of NE cells in lineage 1 and lineage 2

The differences in cuproptosis in NE cells

The status of NE cells had been confirmed, and we analyzed some of the contents related to them in the hope of obtaining some new discoveries. To investigate the potential association between C1 PCLAF + NE cells and NB development and to reveal the expression of NE cells in different cell death pathways, we studied cell death and scored cell death-related pathways for all NE cell subpopulations.

Firstly, we used bar graphs to display the values of the four NE cell subpopulations and cell cycle phases in cuproptosis, showing that the expression levels of C1 PCLAF + NE cells were significantly higher, and there was little difference among G1, G2M, and S phases. (Fig. 5A). Next, we visualized the distribution of AUC values for the four NE cell subpopulations and cell cycles in cuproptosis using UMAP and facet plots, with individual sections separated. These sections were all displayed on a unified UMAP plot. We observed that the AUC values of C1 PCLAF + NE cells in cuproptosis were significantly higher, and the values for the G1 and G2M phases were slightly lower than the S phase (Fig. 5B).Fig. 5 Cuproptosis in NE cell subpopulations. A Bar graphs showed differential expression of cuproptosis in all cell types and during different cell cycles. B UMAP and facet plots provided detailed insights into the distribution differences of cuproptosis AUC values among each subpopulation and across different cell cycles, with colors ranging from purple to yellow indicating increasing AUC values

Cell communication in NB

Figure 6 provided insights into cell-to-cell interactions and communication networks using CellChat. This approach enabled us to understand the interactions of various cell types during tumor progression and explore the TME. Initially, we visualized the number and weight interactions between each cell type as the source and target in tumor samples, revealing a complex communication network among all cell types (Fig. 6A). Focusing on NE cells, we depicted the number and weight interactions with NE cells as the source and target using cellular interactions circle plots. Notably, the interaction intensity among the four subsets of NE cells was notably stronger (Fig. 6B, C). We then studied the transmission of incoming and outgoing signal pathways between cell types. In the case of NE cells, the outgoing signaling was especially strong across all four subgroups. MIF and APP signal pathways ranked highly in the incoming and outgoing signal networks of all cell types. Notably, the MK signal pathway exhibited significant specificity in the C1 PCLAF + NE cells (Fig. 6D). Moreover, we presented the incoming communication patterns of target cells and the outgoing communication patterns of secretor cells of 11 cell types. In the case of C1 PCLAF + NE cells, the main input signal pathways included MK, PTN, NCAM, BMP, and JAM, while the major output signals consisted of MK, MIF, APP, BMP, and JAM (Fig. 6E). Finally, through the heatmap, the expression of signal paths in the three signaling modes of incoming and outgoing in NB was demonstrated. In the incoming mode, the majority of signal paths exhibited high expression levels in pattern 1 (CD6, EPHA), while the outgoing mode was dominated by pattern 1 and displayed elevated expression levels of SEMA6B and other pathways (Fig. 6F). These findings provided valuable information regarding cell communication networks and their relevance to NB.Fig. 6 Interactions between various cell types in NB. A The cell interaction circle diagrams showed the number and weight of cell types in all as sources and targets. B–C The cell interaction circle diagrams showed the number and weight of cell types in NB as sources and targets. D The heatmap illustrated the situation of input and output signal pathways for each cell type in NB, with darker shades indicating higher scores. E Contributions from secretory cells to signal output and target cells to signal input were represented in the bubble diagram, with point sizes proportional to their degree of contribution. F The heatmaps presented the identification of three patterns of incoming and outgoing interactions within NB

The MK signaling pathway and MDK-NCL ligand-receptor pair expression in C1 PCLAF + NE cells

Due to the remarkable specificity of the C1 PCLAF + NE cell subgroup in scRNA-seq data, we focused on their cell interactions. Initially, we demonstrated the number and weight interactions with C1 PCLAF + NE cells as the source and target, highlighting the strong interaction intensity among the four subsets of NE cells (Fig. 7A, B). Subsequently, we illustrated the ligand-receptor interaction between various cell types in NB (especially the four subsets of NE cells). Notably, the MDK-NCL ligand-receptor pair showed high expression in all four subsets of NE cells (Fig. 7C, D). Furthermore, the chord diagram showed the MK signaling pathway between NE cells and other cells and the expression of MDK-NCL ligand-receptor pairs.(Fig. 7E, F). The MK signaling pathway played a crucial role in this study. It played an important role in the occurrence, development, and metastasis of cancer [45]. To explore the mechanism of MK in tumor progression, we conducted the following analysis: Initially, we calculated the centrality score of the MK signal pathway. Among receivers and influencers, C1 PCLAF + NE cells displayed the highest scores (Fig. 7G). These findings were further supported by the violin plot and bubble chart (Fig. 7H, I). Lastly, we presented a layered diagram of the MK signaling pathway network, demonstrating the interactions among components (Fig. 7J). In summary, the MK signaling pathway and MDK-NCL ligand-receptor pair showed significant specificity in C1 PCLAF + NE cells.Fig. 7 Intercellular Interaction of Four Subgroups of NE Cells. A–B The number and weight of the C1 subgroup of NE cells as sources and targets were depicted in the chordal graphs. C–D The bubble charts depicted the ligand-receptor pairs of NE cells. The gradual shift in color from blue to red indicated a progressive increase in the importance of ligand-receptor pairs. E–F Chord diagrams showed cellular interactions in the MK signaling pathway and MDK-NCL ligand-receptor pairs. G The centrality score diagram of the MK signal path network, which encompassed sender, receiver, mediator, and influencer, calculated the relative importance of each cell type. H The violin plot depicted the cellular interactions in the MK signaling pathway network. I The bubble chart displayed the cellular interactions in the MK signaling pathway network. J The layer diagram showcased the interaction between NE cells and other cell types in the MK signaling network

The related metabolism of NE cells in NB

Metabolic changes in malignant cells were a focus of cancer research. The top 20 metabolism-related pathways in NB were displayed, with Oxidative Phosphorylation, Glycolysis/Gluconeogenesis, and Pyruvate Metabolism ranking as the three main metabolic pathways (Fig. 8A). Among them, Oxidative Phosphorylation was the most specific, as shown below (Fig. 8B). Notably, in NE cells, the prominent metabolism-related pathways included Oxidative Phosphorylation, Glycolysis/Gluconeogenesis, Tyrosine Metabolism, Phenylalanine Metabolism, Pyruvate Metabolism, Cysteine and Methionine Metabolism, and Glutathione Metabolism. All four subsets of NE cells highly expressed Oxidative Phosphorylation metabolic pathway (Fig. 8C). The bubble chart demonstrated that the C0 RGS4 + NE cells exhibited high expression levels of Tyrosine Metabolism and Phenylalanine Metabolism, while C1 PCLAF + NE cells displayed high expression in nearly all metabolic pathways (Fig. 8D). We then delved into two specific metabolic pathways: Oxidative Phosphorylation and Glycolysis/Gluconeogenesis. Oxidative Phosphorylation provided energy for cell survival, proliferation, synthesis, and signal transduction, making it crucial for tumor cell proliferation [46]. UMAP visualization illustrated that the expression of this pathway was high in the upper-left corner and low in the lower-right corner; the upper-left corner was dominated by the C1 subgroup (Fig. 8E). Additionally, we showcased the expression of the four subsets of NE cells and their relation to the G1, G2M, and S phases (Fig. 8F). UMAP visualization of the Glycolysis/Gluconeogenesis metabolic pathway [47] demonstrated high expression in the upper-left corner (Fig. 8G). We also presented the expression of the four subsets of NE cells and their relation to the G1, G2M, and S phases (Fig. 8H). These findings shed light on the metabolic characteristics of NB and NE cells, specifically their involvement in Oxidative Phosphorylation and Glycolysis/Gluconeogenesis.Fig. 8 Metabolism of NB. A The heatmap displayed the top 20 metabolic-related pathways of various cell types in NB, among which the Oxidative Phosphorylation metabolic pathway was closest to NE cells, with the value of Aucell gradually transitioning from blue to red. B The violin plot revealed that the expression level of Oxidative Phosphorylation was highest in all cell types of NB, particularly in NE cells. C The heatmap exhibited the first five metabolic-related pathways of four subgroups of NE cells, among which the Oxidative Phosphorylation metabolic pathway was closest to the C1 subgroup. D The bubble chart illustrated the first five metabolic-related pathways of four subgroups in NB. E–F The UMAP plots depicted the expression of the oxidative phosphorylation pathway in NE cells (E), four subsets of NE cells, and the cell cycle (F). G–H. The UMAP plots displayed the expression of Glycolysis/Gluconeogenesis in NE cells (G), four subgroups of NE cells, and the cell cycle (H)

TFs expression of various subgroups and phases of NE cells

Transcription factors (TFs) exerted regulation over downstream target genes within signal transduction cascades, assuming pivotal roles in the advancement of neoplastic lesions. Targeting TFs held promising potential as a novel therapeutic intervention for addressing NB. Initially, we demonstrated the expression levels of TFs in the four subsets of NE cells using a heatmap. The C1 PCLAF + NE cells displayed high expression levels of E2F2, E2F7, and E2F3 (Fig. 9A). Additionally, we showcased the top five TFs in each NE cell subgroup and visualized the TFs with the highest expression in each subgroup using UMAP. These top TFs included ERF in the C0 subgroup, E2F2 in the C1 subgroup, HOXB2 in the C2 subgroup, and NFYB in the C2 subgroup (Fig. 9C). Furthermore, we used the heatmap to show the expression level of TFs in the cell cycle (Fig. 9B). we presented the top five TFs in the G1, G2M, and S phases, including ZNF467, MAFK, FOSB, RARA, and FOSL1 in the G1 phase, NFYB, E2F7, E2F2, E2F3, and TEAD4 in the G2M phase, E2F2, E2F7, E2F3, E2F1, and HMGA1 in the S phase (Fig. 9D). Interestingly, the specific expression of E2F2 in the C1 subgroup and the G2M and S phases suggested its irreplaceable role in the progression of NB and its potential as a therapeutic target for NB.Fig. 9 TFs Display of NB. A The heatmap depicted the TFs expression of four subsets of NE cells. B The heatmap illustrated the TFs expression of each subgroup of NE cells in phase. C The UMAP showed the distribution of four subsets of NE cells (red) (left), the top 5 regulons in four subsets of NE cells (green) (middle), and the distribution of the most active regulons in each subgroup (right). D The UMAP visualization of G1, G2M, and S phases (left), along with the expression in the regulator-specific score (middle), and the UMAP visualization of the highest-ranked regulons different phases (right)

The GOBP enrichment pathways in NE cells and the C1 PCLAF + NE cells enrichment pathways

We conducted GOBP enrichment analysis on the four subsets of NE cells to uncover relevant biological pathways associated with NB. The main enrichment pathways in C0 RGS4 + NE cells included the regulation of nerve projection development, nerve projection guidance, axon guidance, axonogenesis, axon development, and sympathetic nervous system development. In C1 PCLAF + NE cells, DNA replication, nuclear division, nuclear chromosome segregation, sister chromatid segregation, DNA-templated DNA replication, chromosome segregation, regulation of chromosome organization, and regulation of chromosome segregation were the primary enrichment pathways. DNA replication specifically emerged as the most distinctive enrichment pathway in NE cells (Fig. 10A, B). The heatmap displayed gene expression and GOBP enrichment pathways in NE cells, with MYADM, CD24, and VEGFA predominantly expressed in C0 RGS4 + NE cells, while MAZ, POLR2L, C12orf75, CEP135, CRNDE, PARPBP, MTHFD1, and FAM111B were primarily expressed in C1 PCLAF + NE cells. GNAI2, VEZF1, CLEC2L, EIF1AD, and CCYN were mainly expressed in C2 PAGE2 + NE cells, and H2AFY2, STIP1, MARCKSL1, and HNRNPA2B1 were predominantly expressed in C3 BIRC5 + NE cells (Fig. 10C). Moreover, GSEA was performed on C1 PCLAF + NE cells, resulting in the enrichment of ten significantly related pathways, including five upregulated pathways and five downregulated pathways. DNA-templated DNA replication, DNA replication, DNA geometric change, chromosome organization, and DNA conformation change were the high-ranking upregulated pathways, while kidney morphogenesis, regulation of synaptic plasticity, learning, neuron projection guidance, and axon guidance were the downregulated pathways (Fig. 10D). These insights shed light on the GOBP enrichment pathways relevant to NB and the expression changes observed specifically in the C1 PCLAF + NE cells enrichment pathway.Fig. 10 Enrichment Analysis and Display of NE Cells. A The bubble chart displayed the GO enrichment analysis of four subsets of NE cells. B The specific expression of the enrichment pathway in all NE cells indicated that DNA replication was the most specific enrichment pathway. C The heatmap exhibited the genes enriched by various subsets of NE cells and the GOBP enrichment pathway. D GSEA diagrams depicted the GSEA of C1 PCLAF + NE cells, resulting in 10 significantly related enrichment pathways

Discussion

Neuroblastoma (NB) is a type of neuroendocrine tumor categorized as an embryonic tumor of the autonomic nervous system, originating from trunk neural crest cells along the dorsal aorta. NB accounts for 7–8% of malignant tumors in children, with a mortality rate of approximately 15% of pediatric cancers [48]. Due to its high malignancy, patients with high-risk NB may experience tumor emergencies during the course of their disease or treatment, posing a life-threatening risk. These patients are also more prone to developing drug resistance, resulting in a survival rate that remains below 50% [49]. Currently, the clinical management of high-risk NB encompasses a variety of approaches, including surgery, immunotherapy, stem cell transplantation, conventional chemotherapy, and radiation therapy. Despite the advancements these treatments have brought to prognosis, they fall short of ideal outcomes. Existing therapies face significant limitations, with treatment resistance being a primary obstacle. Cancer cells may exhibit either primary or acquired resistance after prolonged therapy. The tumor heterogeneity of NB, marked by aberrant telomere maintenance, MYCN amplification, and mutations in RAS and/or the p53 pathway, adversely impacts prognosis and survival rates. The emergence of immunotherapy, particularly anti-GD2 therapy, highlights its potential in treating NB.

This study aims to explore targeted therapies and the molecular mechanisms underlying NBprogression, with the goal of identifying novel therapeutic targets or signaling pathways to slow disease advancement and enhance treatment strategies.To discern pivotal subgroups within NB, we employed scRNA-seq to scrutinize NB patient samples, yielding 8 principal cell classifications. Through UMAP visualization and phase analysis, NE cells were identified in the G2M and S phases, evincing vigorous cellular division. Hence, we hypothesized that NE cells were the key cell type in NB. Previous studies had also indicated a close association between NE cells and the occurrence and development of NB, aligning with our findings [50].

Further scRNA-seq of NE cells identified four subgroups: C0 RGS4 + NE cells, C1 PCLAF + NE cells, C2 PAGE2 + NE cells, and C3 BIRC5 + NE cells. We analyzed these subgroups from various perspectives to explore their correlation with NB. The inferCNV software package unveiled the chromosomal CNV of NE cell malignancy. This indicated that NE cells represent malignant cells, especially the C1 PCLAF + NE cell subset. Subsequent analyses employing CytoTRACE, Monolce2, and Slingshot delineated NE cell differentiation trajectories in NB progression. The investigation revealed that C1 PCLAF + NE cells showcased a low degree of differentiation, heightened stemness, and were primarily congregated at the onset terminus of the differentiation pathway, manifesting remarkable specificity. C1 PCLAF + NE cells assumed a pivotal role in propelling NB advancement.

C1 PCLAF + NE cells were a key subpopulation in NB progression, and we selected the named genes of this subpopulation as target genes. PCLAF (PCNA clamp-associated factor), a nuclear protein interacting with proliferating cell nuclear antigen (PCNA), plays a significant role in cell proliferation, DNA repair, cell cycle regulation, and DNA damage response [51]. Research indicates that PCLAF is closely associated with several malignancies, including prostate cancer, lung cancer, ovarian cancer, esophageal cancer, cholangiocarcinoma, breast cancer, and gastric adenocarcinoma [52–54], serving as a crucial regulator in cancer initiation and progression [55]. Overexpression of PCLAF in NB is correlated with a poor prognosis, promoting NB cell proliferation both in vivo and in vitro [56].

In recent years, numerous cell death-oriented cancer therapy studies have emerged, offering new avenues for exploring novel cancer treatment methods. Research suggests that cuproptosis is a copper-dependent form of cell death that occurs through direct binding of copper to the lipoylated components of the tricarboxylic acid (TCA) cycle, leading to lipoylated protein aggregation and subsequent iron-sulfur cluster protein loss, and these ultimately lead to proteotoxic stress, causing cell death [57]. it is potentially implicated in the development of various cancers [58]. Studies have shown that it is maybe related to the prognosis of cancer [59]. In NB, some cuproptosis-related genes can promote the invasion, proliferation, and metastasis of NB by regulating the cell cycle [60]. We observed that C1 PCLAF + NE cells scored highly in cuproptosis; the scores of other subgroups were low. Combined with the relevant characteristics of cuproptosis, we can infer that it can be used as a potential target for cancer treatment. To investigate the mechanisms of NB occurrence and progression, we initially employed CellChat to explore intercellular interactions and communication networks among various cell types within NB. We found that the MK signaling pathway was secreted in NE cells, indicating that it was a very important signaling pathway. In addition, the MK signaling pathway showed a high centrality score in C1 PCLAF + NE cells, which confirmed the strong association between this signaling pathway and this subpopulation. We also conducted in-depth studies on the ligand receptor pair MDK-NCL of the MK signaling pathway. Malignant cells mainly interacted with each other through the MDK-NCL pathway, which could promote tumor invasion. Midkine (MK) [61, 62], a heparin-binding growth factor with a protein molecular weight of 13 kDa, is shown to play roles in development, inflammation, and tissue protection. It is highly expressed in various cancers, including NB [63, 64]. The MDK-NCL interaction has been implicated in cancers such as endometrial and lung adenocarcinoma [65, 66], suggesting its role in NB as well. Thus, the MK signaling pathway and the MDK-NCL ligand-receptor pair can be a promising therapeutic target.

Additionally, we investigated NB-related metabolic conditions. Among all NB cell types, NE cells exhibited the highest specificity in the Oxidative Phosphorylation metabolic pathway. Within NE cells, the C1 PCLAF + NE cells prominently expressed both Oxidative Phosphorylation and Glycolysis/Gluconeogenesis pathways. Oxidative Phosphorylation refers to the process where the oxidation of organic substances (such as sugars, fats, and amino acids) releases energy to drive ATP synthesis. Glycolysis is the process by which glucose is broken down into pyruvate in the cytoplasm under anaerobic conditions, producing ATP. Gluconeogenesis, on the other hand, maintains blood glucose levels during fasting. Malignant cells can switch between glycolysis and oxidative phosphorylation to adapt to changing metabolic environments [67]. Thus, it is evident that C1 PCLAF + NE cells not only provide the necessary energy for cell survival, proliferation, synthesis, and signal transduction through oxidative phosphorylation but also sustain energy supply under anaerobic and nutrient-deprived conditions, contributing to the formation of a Regarding Glycolysis/Gluconeogenesis, tumor cells underwent metabolic reprogramming, favoring a “glycolysis-dominated” state, which provided energy for the tumor cells and created a TME. Conducive to carcinogenesis [68]. This further corroborates the significance of C1 PCLAF + NE cells as a crucial subpopulation and suggests that oxidative phosphorylation and glycolysis/gluconeogenesis are worthy of investigation as potential therapeutic targets.

Transcription factors (TFs), crucial in downstream gene regulation, have emerged as key participants in tumor progression. To explore the key TFs associated with NE cells in NB, we performed gene regulatory network analysis. Notably, active TFs in C1 PCLAF + NE cells were E2F2, E2F7, and E2F3, with E2F2's specificity during G2M and S phases underscoring its potential role in NB. Recent research reports [69, 70], emphasize that the E2F transcription factor family can control the cell cycle by regulating related response genes and also regulate cell proliferation and apoptosis, thereby affecting the progression of cancer [71]. Despite its established role in other cancers such as glioma and ovarian cancer, research into E2F2’s role in NB remains nascent, highlighting its novelty as a potential therapeutic target.

Finally, our exploration of NB enrichment through GOBP and GSEA revealed pathways pivotal in tumor cell proliferation and immunity [72, 73]. We found that differentially expressed genes in C1 PCLAF + NE cells were significantly enriched in pathways related to DNA replication, nuclear division, nuclear chromosome segregation, sister chromatid segregation, DNA-templated DNA replication, chromosome segregation, regulation of chromosome organization, and regulation of chromosome segregation. Most of these pathways are directly related to cell growth, proliferation, apoptosis, and immunity. While our findings present novel avenues for clinical research, the limitations stemming from sample size underscore the potential for research bias.

In the future, we will continue to study the mechanism of NE cells, aiming to discover new therapeutic targets, which are expected to improve the prognosis of NB patients. However, the endeavor will remain encumbered by limitations stemming from the absence of corroborative experiments, impeding the comprehensive validation of our findings. As we forge ahead, we will delve deeper into the mechanisms underpinning NB, endeavoring to unearth fresh therapeutic targets primed for clinical deployment.

Conclusion

In the present investigation, we discerned that the C1 PCLAF + subset within NE cells assumed a pivotal role in advancing the progression of NB, surpassing the impact of other cellular subsets. It merited attention that within C1 PCLAF + NE cells, the participation of the MK signaling pathway, MDK-NCL ligand-receptor pair, E2F2 transcription factor, and metabolic pathways (encompassing Oxidative Phosphorylation and Glycolysis/Gluconeogenesis) intricately orchestrated the disease's advancement and fostered NB progression via interconnected biological mechanisms. In addition, we also analyzed cell death in NB and scored some cell death pathways. We found that cuproptosis played an important role in the progression of NB, which also promoted our research on new targets. In essence, we unveiled significant novel therapeutic targets. Our commitment persists to further exploring related malignancies and wholeheartedly dedicating ourselves to their clinical implementation.

Author contributions

Lei Sun, Zhiheng Lin, Wenwen Shao, Jingheng Lin, and Fu Zhao conceived and designed the study. Lei Sun and Zhiheng Lin downloaded and collected the data. Lei Sun analyzed the data and drafted the manuscript. Lei Sun, Wenwen Shao, and Zhiheng Lin performed quality control on the manuscript. Lei Sun supervised the submission. All authors read and approved the final manuscript. Juan Yu was the corresponding author.

Funding

None.

Data availability

The single-cell RNA sequencing (scRNA-seq) data for neuroblastoma was sourced from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). The neuroblastoma-related GSE216176 dataset was downloaded from the GEO public database.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Lei Sun, Wenwen Shao and Zhiheng Lin have contributed equally to this work.
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