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Introduction

Neuroblastoma (NB) is a type of cancer of the sympathetic nervous system characterized by extracranial solid tumors that arise from neural crest cells, and accounts for ~15% of cancer-related deaths in children (1,2). The median age at diagnosis is low (~18 months) with a notable number of cases occurring in infants (<1 year old for 40% of all NB cases) (3,4). Furthermore, over half of patients with NB are classified as high-risk, which results in a low 5-year survival rate (<50%) (3,4). The current major treatments for NB include spontaneous tumor regression, surgery, chemotherapy, radiotherapy and immunotherapy. Although therapy advancements have notably raised survival rates, the prognosis varies by the risk level (3,5–7). Furthermore, relapse, drug resistance and long-term side effects remain significant challenges for the treatment and recovery of these patients (8,9). Therefore, exploring new therapeutic drugs is of great clinical and scientific significance.

Multiple bioactivities have been reported for saikosaponin A (SSa), an oleanane-type saponin derived from Bupleurum chinensis DC. These include antitumor properties, anti-inflammatory effects and liver protection (10,11). Previous studies have shown that the antitumor activities of SSa, including suppressing cell proliferation, inducing apoptosis and inhibiting metastasis and angiogenesis, span various cell types including liver, colon, breast and pancreatic cancer cells (12). Specifically, in human hepatoma cell lines, SSa exhibits an antiproliferative effect (13,14). Such an effect in the HepG2 cell line is correlated with ERK activation and the upregulation of certain genes (15,16). In human colon carcinoma cells, SSa induces apoptosis by activating caspase-4, followed by activating sequential caspase-2 and −8 (17,18). In breast cancer cells, SSa inhibits proliferation and induces apoptosis (19,20). In both in vitro and in vivo conditions, SSa suppresses the activation of Akt and STAT3, decreases the levels of glycolysis and blocks the energy supply (21). In pancreatic cancer, SSa inhibits proliferation and induces apoptosis, which may be related to the inactivation of the EGFR/PI3K/Akt pathway (22).

A study has shown that SSa inhibits the proliferation of NB cells by enhancing apoptosis (23). SSa decreases the expression of Bcl-2 and increases the expression of Bax while activating proteins such as caspase-9, caspase-7 and poly (ADP-ribose) polymerase (23). Furthermore, SSa stops NB cells from invading and migrating by blocking certain signals (such as VEGFR2, Src and Akt) and by regulating proteins connected with epithelial-mesenchymal transition (EMT) (23). However, the molecular mechanism of SSa against NB cell remains unclear. In the present study, human NB SK-N-AS cells were treated with SSa and the transcriptome expression profiles of these cells were analyzed using RNA-sequencing (seq) combined with bioinformatic methods. The differentially expressed genes (DEGs) were then verified by reverse transcription-quantitative PCR (RT-qPCR).

Materials and methods

Cell culture

The human SK-N-AS NB cell line was purchased from Heilongjiang Jiufeng Bioengineering Co., Ltd. The human SH-SY5Y NB cell line was purchased from Haixing Biosciences Co., Ltd. (Suzhou, China) and was authenticated by STR profiling. The human MO3.13 oligodendrocytic cell line (normal cells) was purchased from Cyagen Biosciences, Inc. These cells were initially cultured in Dulbecco's Modified Eagle Medium (DMEM; Gibco; Thermo Fisher Scientific, Inc.) containing 10% fetal bovine serum [FBS; Cellmax Cell Technology (Beijing) Co., Ltd.] and 1% penicillin-streptomycin (Gibco; Thermo Fisher Scientific, Inc.) in an incubator maintained at 37°C with a humidified environment of 5% CO2 and 95% air. Afterwards, cells were transferred every 2–3 days depending on their density, and experiments began once they achieved 80–90% confluency.

Cell viability assay

Cell viability was measured using the MTT assay following the protocol by Cheng and Ying (23). In brief, a cell suspension (SK-N-AS/SH-SY5Y/MO13.3) in DMEM containing 10% FBS (106 cells/ml) was inoculated into 96-well plates with 100 µl per well and cultured at 37°C for 24 h with 5% CO2 and 95% air. The cells were then treated with SSa (purity: HPLC ≥98%, Baoji Herbest Bio-Tech Co., Ltd.) at various concentrations [0 µM (control), 5, 10, 15, 20 and 25 µM] for 24 h. Following the treatment, 10 µl of MTT solution (5 mg/ml) was pipetted into each well, allowing the cells to incubate for an additional 4 h at 37°C. The medium was discarded and 100 µl of DMSO was added to each well to dissolve the purple formazan while the absorbance was measured at 490 nm using a Microplate Reader (RT-6000; Rayto Life and Analytical Sciences Co., Ltd.). Cell viability was calculated as follows: Cell viability=[OD490 (SSa)/OD490 (control)] ×100%.

Transwell migration and invasion assay

The methods were performed following the protocol by Cheng and Ying (23). In brief, SK-N-AS cells were digested and suspended in serum-free DMEM containing SSA (0 or 5 µM) at a final concentration of 106 cells/ml. For the invasion assay, 50 µl diluted Matrigel (Matrigel and serum-free DMEM were prepared at a ratio of 1:3 at 4°C) was added to the upper Transwell chamber (24-well; pore size, 8 µm; Corning, Inc.) and incubated at 37°C for 5 h. Subsequently, DMEM containing 20% FBS was added to the lower Transwell chamber. Subsequently, 100 µl of SK-N-AS cell suspension was plated in the upper chamber and incubated at 37°C for 24 h. The cells that invaded into the lower surface of the Transwell membrane were fixed with 100% methanol (room temperature for 30 min) and stained with 0.1% crystal violet (room temperature for 20 min). Stained cells were observed by fluorescence inverted microscopy (Leica Microsystems GmbH). A total of five random fields were imaged to calculate the relative invasion rates based on the ratio of the number of invaded cells in the SSa group to the control group. For the migration assay, all the steps were the same as in the invasion assay but without the addition of Matrigel.

RNA isolation and sequencing

To isolate RNA, ~4 ml of SK-N-AS cells (106 cells/ml) were seeded into a T25 flask. The cells were cultured for ~2 days at standard conditions (37°C for 48 h in humidified atmosphere of 5% CO2 and 95% air) before being divided into two groups and treated for another 24 h: Those receiving SSa treatment (n=3) were incubated in FBS-free DMEM containing 15 µM SSa, and those in the control group did not receive any substance addition (n=3). Post-treatment, total RNA extraction from the SN-K-AS cells was performed using TRIzol Reagent (Invitrogen; Thermo Fisher Scientific, Inc.). NanoDrop spectrophotometer-based measurements (Thermo Fisher Scientific, Inc.) confirmed the concentration and purity levels alongside LabChip GX Touch HT Nucleic Acid analysis (PerkinElmer, Inc.).

The collected RNA samples (a total of 6, 3 for SSa treatment and 3 for control) were then transported to Wuhan Boyue Zhihe Biotechnology Co., Ltd., where library construction and sequencing tasks employing KAPA Stranded RNA-Seq Kit protocols plus multiplexing primer guidance were undertaken. In brief, mRNA was enriched by oligo(dT) beads and sequencing results were obtained using NovaSeq™ 6000 systems (Illumina, Inc.).

Data analysis

Raw data (raw reads) were filtered into clean data (clean reads) using Trimmomatic v0.36 (24). This involved removing reads with adapters, reads containing ploy-N and low-quality reads from the raw data. Clean data were then aligned to the human reference genome (http://asia.ensembl.org/Homo_sapiens/Info/Index) using Hisat 2 (25). The number of perfect clean tags for each gene was calculated and normalized as fragments per kilo bases per million mapped reads using featureCounts v1.5.0 (26). DEGs were identified using the DESeq R package (1.10.1) (27), with the following criteria: Fold-change ≥1.5 or ≤0.667 and adjusted P<0.05 (28).

Gene Ontology (GO) enrichment analysis was conducted by Database for Annotation, Visualization, & Integrated Discovery (DAVID) to reveal the biological function of DEGs (29,30). Kyoto Encyclopedia of Genes and Genomes (KEGG) was utilized to analyze the metabolic and signaling pathways inherent in the DEGs; enrichment and network construction were performed by Metascape 3.5 (31,32). Protein-protein interaction (PPI) networks of putative proteins encoded by DEGs were constructed with STRING 12.0 (33).

Validation by RT-qPCR

Total RNA was isolated from SK-N-AS cells using the SV Total RNA Isolation System (Promega Corporation), then reverse transcribed into cDNA using the GoScript™ kit (Promega Corporation) according to the manufacturer's protocol. qPCR was employed to assess DEG expression using the LineGene 9620 qPCR system (Hangzhou Bioer Co., Ltd.) following the GoTaq® qPCR protocol (Promega Corporation). The primer sequences for amplifying the DEGs are listed in Table I. Relative gene expression level was calculated based on the 2−∆∆Cq method, with GAPDH as the internal control gene (34).

Table I. Primer sequences used in reverse transcription-quantitative PCR.

Table I.

Primer sequences used in reverse transcription-quantitative PCR.

Gene	Forward primer (5′-3′)	Reverse primer (5′-3′)
IL24	TTGCCTGGGTTTTACCCTGC	AAGGCTTCCCACAGTTTCTGG
EGR1	GGTCAGTGGCCTAGTGAGC	GTGCCGCTGAGTAAATGGGA
RET	AAAGTGGCATTGGGCCTCTAC	GCAGGGCATGGACGTACAG
MDK	CGCGGTCGCCAAAAAGAAAG	TACTTGCAGTCGGCTCCAAAC
PDGFRA	TTGAAGGCAGGCACATTTACA	GCGACAAGGTATAATGGCAGAAT
HGF	GCTATCGGGGTAAAGACCTACA	CGTAGCGTACCTCTGGATTGC
VCAM1	CAGTAAGGCAGGCTGTAAAAGA	TGGAGCTGGTAGACCCTCG
SLIT3	GTCAGCGTCATCGAGAGAGG	TTCGGCGTGCTCTGGAAAAG
CD34	ACCAGAGCTATTCCCAAAAGACC	TGCGGCGATTCATCAGGAAAT
FN1	CGGTGGCTGTCAGTCAAAG	AAACCTCGGCTTCCTCCATAA
COL1A1	GTGCGATGACGTGATCTGTGA	CGGTGGTTTCTTGGTCGGT
NCAM1	GGCATTTACAAGTGTGTGGTTAC	TTGGCGCATTCTTGAACATGA
GAPDH	GGAGCGAGATCCCTCCAAAAT	GGCTGTTGTCATACTTCTCATGG

Statistical analysis

Data are presented as mean ± standard deviation. Student's unpaired t-test and one-way analysis of variance with Dunnett's post hoc test were applied for the statistical analyses of the cell viability and qPCR results. Fisher's exact test was used for bioinformatic analysis. P<0.05 was considered to indicate a statistically significant difference. Statistical analysis was performed using SPSS 26.0 (IBM Corp.).

Results

Inhibitory effect of SSa on human NB cells

As depicted in Fig. 1A and B, after 24 h, SSa reduced the viability of human SK-N-AS and SH-SY5Y NB cells in a dose-dependent manner. The half maximal inhibitory concentration values for SSa in SK-N-AS and SH-SY5Y cells after 24 h were 14.5 and 15.8 µM, respectively. SSa had no inhibitory effect on normal MO3.13 cells at doses <25 µM (Fig. 1C). This indicated that SSa was cytotoxic to SK-N-AS and SH-SY5Y cells and was non-toxic to normal cells at the effective doses, which aligned with the results of a previous study (23). In subsequent experiments, 15 µM was selected for transcriptomic analysis.

Figure 1. Viability of cells treated with various concentrations of SSa for 24 h. (A) Viability of the human SK-N-AS NB cell line treated with SSa. (B) Viability of the human SH-SY5Y NB cell line treated with SSa. (C) Viability of the human MO3.13 oligodendrocytic cell line (normal cells) treated with SSa. Data are displayed as the mean ± SD, n=6. *P<0.05, **P<0.01 vs. the 0 µM SSa group. NB, neuroblastoma; SSa, saikosaponin A.

Inhibitory effect of SSa on the migration and invasion of SK-N-AS cells

Transwell assays were performed to verify the inhibitory effect of SSa on the migration and invasion of SK-N-AS cells. To prevent interference from the inhibitory effect of SSa on SK-N-AS cell viability, a concentration of SSa (5 µM) was used in these assays. As shown in Fig. 2, SSa significantly inhibited the migration and invasion of SK-N-AS cells, consistent with previous literature (23).

Figure 2. Invasion and migration of SK-N-AS cells treated with SSa for 24 h. (A) Transwell invasion assay images (magnification, ×200) and the relative invasion rate of SK-N-AS cells treated with SSa (0 and 5 µM). (B) Transwell migration assay image (migration, ×200) and the relative migration rate of SK-N-AS cells treated with SSa (0 and 5 µM). Data are displayed as mean ± SD, n=3. *P<0.05 vs. the 0 µM SSa group. SSa, saikosaponin A.

DEGs of SK-N-AS cells in the treatment and control groups

RNA-Seq was carried out to analyze the transcriptomic profile of the SK-A-AS cell samples, producing means of 42.5 million and 46.9 million clean reads for the control group and SSa treatment group, respectively. The overall mapped rates of clean reads to the human reference genome in all samples ranged from 92.41 to 94.68% (Table II). Compared with the control group, there were 297 DEGs in the SSa treatment group; among these genes, 23 were upregulated while 274 were downregulated (Table SI). The volcano plot in Fig. 3A illustrates the DEGs between the SSa treatment and control groups; genes were arranged based on their log2 fold change value along the x-axis and their-log10 adjusted P-value along the y-axis. A hierarchical clustered heatmap was created to visualize the gene expression relationship patterns between the samples. The 3 samples from each group were clustered on the same branch, indicating that SSa treatment significantly changed the gene expression patterns in SK-N-AS cells (Fig. 3B).

Figure 3. Analysis of the DEGs between the SSa treatment and control groups. (A) Volcano plot shows the expression level of each gene. The x-axis represents the log2 fold-change of the gene expression and the y-axis the represents-log10 P adj value. The red spots represent significantly upregulated DEGs and the blue spots represent significantly downregulated DEGs in the SSa treatment group compared with the control group. (B) Heatmap shows the gene expression pattern of each sample. The rows correspond to genes and the columns correspond to samples. DEGs, differentially expressed genes; SSa, saikosaponin A.

Table II. Alignment statistics of the reads aligned to the human reference genome.

Table II.

Alignment statistics of the reads aligned to the human reference genome.

Sample	Total clean reads	Total mapped reads	Uniquely mapped reads	Total mapped rate (%)
Control 1	41,986,280	39,101,793	36,821,952	93.13
Control 2	39,422,620	36,429,580	34,329,297	92.41
Control 3	46,085,672	42,722,112	40,263,786	92.70
SSa 1	42,659,786	39,484,619	37,215,349	92.56
SSa 2	39,815,270	37,695,401	35,546,191	94.68
SSa 3	58,333,588	54,581,167	51,362,190	93.57

[i] SSa, saikosaponin A.

GO enrichment results of the DEGs

GO annotation enrichment analyses within the DEGs were assessed to determine the significant biological function changes. A total of 717 GO terms were enriched including 610 biological processes, 59 cellular components and 48 molecular functions. In the biological process category, ‘cell adhesion’ (GO: 0007155), ‘biological adhesion’ (GO: 0022610), ‘neurogenesis’ (GO: 0022008), ‘extracellular matrix organization’ (GO: 0030198) and ‘extracellular structure organization’ (GO: 0043062) were the top five significantly enriched processes. Biological processes related to the antitumor activity of SSa on human NB cells were also enriched, such as ‘apoptotic process’ (GO: 0006915), ‘angiogenesis’ (GO: 0001525) and ‘epithelial to mesenchymal transition’ (GO: 0001837). In the molecular function category, ‘extracellular matrix structural component’ (GO: 0005201), ‘integrin binding’ (GO: 0005178), ‘glycosaminoglycan binding’ (GO: 0005539), ‘receptor binding’ (GO: 0005102) and ‘heparin binding’ (GO: 0008201) represented the major functions encompassed by the DEGs. Moreover, in the cellular component category, the DEGs primarily contributed to ‘extracellular matrix’ (GO: 0031012), ‘extracellular region’ (GO: 0005576), ‘extracellular region part’ (GO: 0044421), ‘cell surface’ (GO: 0009986) and ‘extracellular space’ (GO: 0005615). Fig. 4 illustrates the top 20 GO terms within each category.

Figure 4. Enrichment results of the Gene Ontology terms associated with differentially expressed genes between the saikosaponin A treatment and control group.

KEGG pathway enrichment results of the DEGs

The metabolic and signaling pathways contributing to SSa inhibition of SK-N-AS cells were also analyzed using the KEGG database. The results showed that the DEGs were enriched in 55 KEGG pathways (Table SII). Pathways related to the invasion and migration of cancerous cells were significantly enriched, such as ‘ECM-receptor interaction’ (hsa04512), ‘Focal adhesion’ (hsa04510) and ‘Cell adhesion molecules’ (hsa04514). In addition, 14 pathways belonged to signal transduction and 7 pathways were related to cancer. The signaling pathways included ‘PI3K-Akt signaling pathway’ (hsa04151), ‘TNF signaling pathway’ (hsa04668) and ‘Apelin signaling pathway’ (hsa04371). Cancer-related pathways included Metabolism of central carbon in cancer (hsa05230), ‘Proteoglycans in cancer’ (hsa05205) and Non-small cell lung cancer (hsa05223). Other pathways mainly encompassed human disease and metabolic pathways. The top 20 significantly enriched pathways are shown in Fig. 5A, with connections among the enriched pathways shown as a network in Fig. 5B.

Figure 5. Enrichment results of the Kyoto Encyclopedia of Genes and Genomes pathway analysis involving differentially expressed genes between the saikosaponin A treatment and control groups. (A) Bubble diagram of the top 20 significantly enriched pathways. (B) Network diagram illustrating the connections among the enriched pathways.

PPI networks based on the products of the DEGs

The list of gene symbols was submitted to the STRING database to construct the PPI network of putative proteins encoded by the DEGs, with ‘organisms’ set to Homo sapiens and the ‘minimum required interaction score’ set to high confidence. As shown in Fig. 6, the largest network contained 98 nodes and 201 edges, while 139 isolated nodes without any connections were hidden. Genes such as FN1, COL1A1, DDX58, PTPRC, THBS1, CCL2, IFIH1, IL1B, MX1 and VCAM1 ranked among the 10 most-connected genes and acted as hubs in the network. A total of 29 clusters were obtained based on Markov clustering, with the largest cluster containing 21 genes whose functions mainly referred to cell adhesion (hsa04512, hsa04514, hsa04510, GO:0007155, GO:0030155 and GO:0016477).

Figure 6. Protein-protein interaction network of differentially expressed genes between the saikosaponin A treatment and control groups, with the four largest clusters based on the Markov clustering shown on the right.

Confirmation of transcriptomic sequencing with qPCR

To verify the transcriptomic sequencing results, genes related to the SSa inhibition of SK-N-AS cells were determined by qPCR. These genes included IL24, EGR1, RET, MDK, PDGFRA, HGF, VCAM1, SLIT3, CD34, FN1, COL1A1 and NCAM1. In the group treated with SSa, certain gene expression levels increased (IL24 and EGR1) or decreased (RET, MDK, PDGFRA, HGF, VCAM1, SLIT3, CD34, FN1, COL1A1 and NCAM1) significantly compared with the control group (Fig. 7). Therefore, the RNA-seq and qPCR data were consistent.

Figure 7. Relative expression levels of candidate genes validated by reverse transcription-quantitative PCR. Data is shown as mean ± SD, n=6 for all groups. **P<0.01 vs. control group. SSa, saikosaponin A.

Discussion

SSa is one of the main active components of Bupleurum chinensis DC (10,11). In the present study, the antitumor activity of SSa on human NB cells (SK-N-AS and SH-SY5Y) and the absence of SSa inhibition on normal cells (MO3.13) was verified at the effective concentrations, indicating its safety. The inhibitory effects of SSa on SK-N-AS migration and invasion were also confirmed by Transwell assay. Furthermore, RNA-seq was performed in conjunction with bioinformatics analysis, which revealed the molecular mechanism underlying the antitumor effects of SSa against NB at the mRNA level. As shown in the GO enrichment results, DEGs involved in apoptosis were enriched under the GO term GO:0006915 (apoptosis). The RNA-seq results alongside the qPCR verification indicated that the upregulated genes were IL24 and EGR1 and the downregulated genes were RET and MDK, which was consistent with the apoptosis pathway detected following GO enrichment, as indicated in previous studies (35–39). These results suggested that SSa may be an inhibitor of RET and MDK expression, and hence against NB. Notably, RET has been shown to be expressed in numerous NB cells (40), and its activation inhibits apoptosis in NB cells (38). RET inhibition is a promising antitumor therapeutic strategy and drugs targeting RET have shown promising effects in NB (41,42). As to MDK, some therapeutic strategies aimed at inhibiting MDK expression have also shown promising antitumor effects (43–45). In NB, increased MDK levels are correlated with an unfavorable prognosis, and reducing MDK expression may serve as an effective strategy for NB treatment (39).

In the present study, DEGs linked to angiogenesis were enriched under the GO term GO:0001525 (angiogenesis). The RNA-seq results alongside the qPCR verification indicated that the downregulated genes were PDGFRA, HGF, VCAM1, SLIT3 and CD34 after SSa treatment, which was consistent with the angiogenesis pathway detected in the GO enrichment, as suggested in previous studies (46–50). SSa could inhibit angiogenesis in NB by targeting the PDGFRA and HGF genes (and thus HGF/c-MET signaling). Notably, upregulation of PDGFRA has been observed in a number of malignancies and is associated with poor prognosis (51,52). Additionally, HGF promotes angiogenesis in NB and reports suggest that inhibiting the HGF/c-MET signaling pathway may have therapeutic effects against neuroblastoma (47,53). Meanwhile, VCAM1 and SLIT3 may be proangiogenic factors (48,54), and CD34 has been used as a marker in immunohistochemistry to assess angiogenesis in tumors (50,55). However, the roles of VCAM1, SLIT3 and CD34 in the tumorigenesis of NB are currently unknown. In addition, the PI3K-Akt signaling pathway was downregulated according to the KEGG enrichment results obtained in the present study. This pathway influences angiogenesis by activating multiple angiogenic factors and its activation involves both PDGFRA and HGF (56,57). These findings suggest that SSa may suppress angiogenesis by inhibiting the PI3K-Akt signaling pathway, consistent with previous reports (22,23). The results of the present study also showed that after SSa treatment, the ECM-receptor interaction (hsa04512) in NB cells was the most significantly enriched KEGG pathway, which is closely linked to angiogenesis (GO:0001525). This ECM-receptor interaction pathway participates in the regulation of EMT progression, which is critical for metastasis (58). The expression levels of the FN1, COL1A1 and NCAM1 genes are all related to the EMT process (59–61).

In the present study, the PPI network results showed that the proteins in the extracellular matrix (FN1 and COL1A1) were the most connected proteins. Meanwhile, the RNA-seq results alongside the qPCR verification showed that the corresponding FN1 and COL1A1 genes were both downregulated in the treatment group. These results suggest that SSa could inhibit the metastasis-related EMT process by targeting FN1 and COL1A1. The PPI network analysis also demonstrated that the largest protein cluster primarily encompassed cell adhesion. Consistently, the RNA-seq results alongside the qPCR verification showed that after SSa treatment, the cell adhesion molecule gene, NCAM1, was downregulated in NB cells. Therefore, SSa could inhibit the metastasis-related EMT process by suppressing the expression of NCAM1. Notably, the FN1 and COL1A1 proteins (and the corresponding genes) could be potential targets for cancer treatment (62–64), with NCAM1 more specifically for NB (65,66).

There are certain limitations of the present study. The present study only focused on the impact of SSa on SK-N-AS cells. Given the varying genetic makeup of different NB cells (2), further research is needed to determine whether the findings can be generalized to other NB cells. The present study elucidated the mechanism of SSa against NB at the transcriptional level. However, examination at the protein level is equally crucial for the determining the antitumor activity of the drug. Taking the aforementioned PI3K-Akt signaling pathway as an example, in addition to expression levels, the phosphorylation status of core proteins also plays a vital role in pathway activation. Therefore, further investigation is warranted to gain deeper insights into the molecular mechanisms underlying the anti-neuroblastoma effects of SSa. Additionally, subsequent animal studies should be performed to facilitate the clinical translation of SSA for neuroblastoma treatment.

In conclusion, the results of the present study showed that SSa significantly inhibited the viability, migration and invasion of SK-N-AS NB cells within 24 h. SSa likely induced apoptosis in SK-N-AS cells by upregulating IL24 and EGR1 and downregulating RET and MDK expression. SSa may have anti-angiogenesis effects through downregulating PDGFRA, HGF, VCAM1, SLIT3 and CD34. SSa may suppress metastasis by inhibiting EMT through downregulating FN1 (central hub), COL1A1 and NCAM1 expression as well as downregulating the PI3K-Akt signaling pathway. Therefore, the present study provided an in-depth comprehension of the molecular processes and signal transduction pathways driving the effects of SSa against NB through RNA-seq and bioinformatics analyses. However, the specific role of the relevant genes in the anti-NB process of SSa and the direct target of SSa still needs further investigation. These findings suggest that SSa can be a potential therapeutic agent for NB.

Supplementary Material

Supporting Data

Acknowledgements

Not applicable.

Funding

This work was supported by the National Natural Science Foundation of China (grant nos. 81503271 and 81573539), University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province (grant no. UNPYSCT-2017217) and the Heilongjiang Touyan Innovation Team Program [grant no. (2019) No. 5].

Availability of data and materials

The RNA-seq data generated in the present study may be found in the Sequence Read Archive database under accession no. PRJNA1158969 or at the following URL: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1158969. All other data generated in the present study may be requested from the corresponding author.

Authors' contributions

NG was responsible for study design and contributed to data interpretation. WZ, LD and HC performed the experiments. JS, BL and YC analyzed the data and modified the manuscript. BL and YC confirm the authenticity of all the raw data. All authors read and approved the final version of the manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References