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Introduction

Neuroblastoma (NB) is one of the most common extracranial solid tumors in children, accounting for 15% of all childhood deaths from cancer (1). It is almost exclusively a pediatric malignancy, and >90% of patients are diagnosed at <10 years of age (2,3). NB originates in neural crest cells of the sympathetic nervous system, which are mainly found in the adrenal gland, neck, chest, abdomen and pelvic cavity. Although the clinical diagnosis and treatment for NB are continually improving, the 5-year survival rate for children with high-risk NB remains <50% (4).

The mechanisms underlying NB pathogenesis and development are complex, involving genetic and epigenetic alterations, chromosomal changes, and the altered expression of microRNAs (miRNAs/miRs) and long non-coding RNAs (lncRNAs) (5,6). Among these genetic changes, the Children's Oncology Group revealed that MYCN proto-oncogene, bHLH transcription factor (MYCN) amplification is an independent marker for NB prognosis and risk stratification (7,8). However, MYCN amplification occurs in only 20-30% of patients with primary NB (9). Due to the diversity of clinical phenotypes and complex biological characteristics, there is an urgent need to identify precise biological markers for NB diagnosis and prognosis, as well as potential molecular targets for chemotherapy.

lncRNAs are non-coding R NA transcripts >200 nucleotides long, which are involved in transcriptional and post-transcriptional regulation (10), and have important value for the diagnosis and treatment of tumors. They contribute to various biological processes in tumorigenesis, including tumor proliferation, metastasis, differentiation and cell death. lncRNAs can be detected in urine, and prostate cancer-associated 3 was recently approved by the US Food and Drug Administration to identify prostate cancer (11,12). In pediatric NB, various lncRNAs, including metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), HOXD antisense growth-associated long non-coding RNA and miR-100-let-7a-2-miR-125b-1 cluster host gene (linc-NeD125), are involved in differentiation, tumor proliferation, invasion and migration (13-16). Furthermore, in patients with high-risk NB, small nucleolar RNA host gene 1 is highly expressed and is strongly correlated with MYCN amplification (17,18).

As a member of the small nucleolar RNA host gene family, small nucleolar RNA host gene 16 (SNHG16) is highly expressed in several types of cancer (18-21). It is regulated by the Wnt pathway in colorectal cancer and induced breast cancer cell migration (19,20). Although this suggests that SNHG16 may function as an oncogene in cancer, its underlying molecular mechanisms are unclear, particularly in pediatric NB. Therefore, the present study further investigated the effects of SNHG16 on NB.

Materials and methods

Clinical patients

All patients with NB (aged between 7 months and 8 years) were clinically and histopathologically diagnosed at Beijing Children's Hospital between May 2015 and December 2016 based on the International Neuroblastoma Staging System (INSS) for clinical staging of NB (22). The present study was approved by the Ethics Committees of Beijing Children's Hospital. A total of 40 surgical specimens were immediately snap-frozen in liquid nitrogen prior to total RNA extraction.

Cell culture and transfection

The NB cell line SH-SY5Y (#CRL-2266; MYCN non-amplified) was obtained from the American Type Culture Collection (ATCC); this cell line is widely used in mechanistic and drug development studies regarding NB (23,24). Cells were cultured in Dulbecco's modified Eagle's medium (Corning, Inc.) supplemented with 10% fetal bovine serum (FBS; Corning, Inc.) in a humidified incubator containing 5% CO2 at 37°C. According to the manufacturer's protocol, synthetic small interfering (si)RNAs were transfected into cells at ~50% confluence using the Lipofectamine RNAiMAX kit (Invitrogen; Thermo Fisher Scientific, Inc.). Cells were further analyzed 8 h post-transfection.

RNA interference

SH-SY5Y cells in the exponential growth phase were seeded for 24 h and were then transfected with 100 nM siRNA at room temperature using Lipofectamine RNAiMAX (Invitrogen; Thermo Fisher Scientific, Inc.). siRNA oligonucleotides were synthesized by Sangon Biotech Co., Ltd., as follows: SNHG16 (siRNA1-SNHG16, 5′-CAGCAGUUGAGGGUUUGCUGUGUAUdTdT-3′, siRNA2-SNHG16, 5′-GGACAACCUAGCUGUUGAAdTdT-3′); non-targeting control (5′-UUCUCCGAACGUGUCACGUTT-3′). Cells were returned to the incubator and were refreshed with normal medium 8 h post-transfection, and further experiments were performed at scheduled times.

RNA extraction and reverse transcription-quantitative PCR (RT-qPCR)

A total of 3×105 SH-SY5Y cells/well were seeded in 6-well plates at ~50% confluence and transfected with siRNA for 72 h. Tumor tissues and cells were homogenized in TRIzol® reagent (Invitrogen; Thermo Fisher Scientific, Inc.), and total RNA was extracted using the Direct-zol™ RNA Miniprep kit (Zymo Research Corp.). The RevertAid™ H Minus First Strand cDNA Synthesis kit (Invitrogen) was used for RT, and cDNA templates were amplified with the SYBR Green Master mix (Applied Biosystems; Thermo Fisher Scientific, Inc.). Both RNA extraction and RT procedures were performed according to the manufacturers' protocols. qPCR thermal cycling was set as follows: Initial denaturation at 95°C for 10 min; 40 cycles at 95°C for 15 sec and 60°C for 60 sec; followed by melt curve at 95°C for 15 sec, 60°C for 60 sec and 95°C for 15 sec. For RNA expression detection, GAPDH was used as a reference gene. The primer sequences were as follows: SNHG16, forward 5′-CAGTCAGCCTCAGTTTCCAA-3′, reverse 5′-AGGCAGGGCTGTGCTGAT-3′; and GAPDH, forward 5′-CGAGTCAACGGATTTGGTGGTAT-3′ and reverse 5′-AGCCTTCTCCATGGTGAAGAC-3′. The relative fold-change in mRNA expression was calculated using the 2−ΔΔCq method (25).

Real-time cell proliferation assay

Cell proliferation was measured by xCELLigence real-time cell analysis (RTCA) (ACEA Biosciences, Inc.). Briefly, a total of 4 ×103 SH-SY5Y cells were cultured in an adaptive E-plate (ACEA Biosciences, Inc.) and transfected with siRNA. The E-plate was then incubated at 37°C within the RTCA Station inside the incubator, and device-defined cell index values were recorded every 20 min for 72 h. Increases in cell numbers altered the baseline impedance, which was monitored by gold micro-electrodes located at the bottom of the E-plate. Data analysis was performed using the RTCA Control Unit and preinstalled RTCA software 2.0 (ACEA Biosciences, Inc.).

Colony formation assay

A total of 2×103 SH-SY5Y cells/well were seeded in 6-well plates and transfected with siRNA. After 14 days, cells were fixed with 4% paraformaldehyde for 10 min and stained with 0.1% crystal violet (Sigma-Aldrich; Merck KGaA) for a further 10 min at room temperature. Images were captured using a light microscope (IX73; Olympus Corporation), in order to observe cell colony formation. Colonies containing >50 cells were counted and recorded for statistical analysis. Independent assays were conducted three times.

Wound healing assay

A total of 3×105 SH-SY5Y cells/well were seeded in 6-well plates at ~50% confluence, and a 1-ml micropipette tip was used to scratch the surface of the plate to create a 'wound'. After gentle washing with phosphate-buffered saline (PBS), the attached cells were transfected with siRNA. Wound images were captured under a light microscope (IX73; Olympus Corporation) at 0, 24, 48 and 72 h. The wound width was measured and analyzed by AlphaView SA 3.4.0 software (ProteinSimple).

Transwell assay

Transwell chambers (pore size, 8 μm; Costar; Corning, Inc.) were used to conduct a migration assay. A total of 250 μl serum-free medium containing 4×104 cells was added into the upper chamber, whereas 500 μl complete medium was added into the bottom chamber. After 24 h at 37°C, the cells on the upper chamber were discarded and the cells that had migrated to the lower surfaces of the filters were fixed with 4% para-formaldehyde for 10 min at room temperature. Images of the cells were captured using a light microscope (IX73; Olympus Corporation) following further staining with 0.1% crystal violet (Sigma-Aldrich; Merck KGaA) at room temperature for 10 min.

Cell cycle analysis

A total of 3×105 SH-SY5Y cells/well were cultured in 6-well plates; 72 h post-transfection, cells were trypsinized, centrifuged at 110 × g for 5 min, and resuspended in ice cold ethanol (70%) at 4°C overnight. PBS containing 2% FBS was added to spin down the cells at 440 × g for 5 min. The cell pellet was then incubated in PBS containing 2% FBS, 10 μl 1 mg/ml propidium iodide solution and 2 μl 10 mg/ml RNAseA (Tiangen Biotech Co., Ltd.) for 30 min at 37°C in the dark. Cell cycle progression was assessed using a flow cytometer and CellQuest Pro 6.1 software (BD Biosciences); ≥1×104 cells were analyzed for each sample.

Acridine orange (AO)/ethidium bromide (EB) staining

AO and EB staining (Nanjing KeyGen Biotech Co., Ltd.) was used to visualize nuclear alterations characteristic of apoptosis. AO is a vital dye that stains live and dead cells, whereas EB only stains cells that have lost membrane integrity, thus indicating apoptosis. Live cells appear uniformly green, whereas apoptotic cells will incorporate EB and therefore will be stained red-orange with condensed nuclei (26). A total of 3×105 SH-SY5Y cells/well were cultured in 6-well plates, transfected with siRNA for 72 h, and stained with AO (10 μg/ml) and EB (10 μg/ml) for 30 min at room temperature. Cellular apoptosis was subsequently viewed and images were captured under a fluorescence microscope (Olympus Corporation).

Caspase-3/7 activity detection

Caspase-3/7 activation was measured using the Caspase-Glo 3/7 assay (Promega Corporation) according to the manufacturer's protocol. Briefly, a total of 1×104 cells/well were seeded into 96-well plates, transfected with siRNA, and cultured for 72 h. Subsequently, 100 μl Caspase-Glo 3/7 reagent was added to each well and the well contents were agitated on a plate shaker. Finally, samples were incubated at room temperature for 2 h and luminescence was measured using a SpectraMax Microplate Luminometer (Molecular Devices, LLC).

RNA-binding protein (RBP) prediction analysis

Potential RBPs that bind SNHG16 (27) were systematically identified using starBase v2.0 software (starbase.sysu.edu.cn). The Cytoscape plug-in ClueGO was then used to identify Gene Ontology (GO) terms and interpret functions enriched for the predicted RBPs (28). Statistical parameters were set as follows: Right-sided hypergeometric test, P<0.05 with Benjamini-Hochberg correction; GO levels, 6-14; Kappa score threshold, 0.4.

Statistical analysis

Publically available Gene Expression Omnibus (GEO) datasets (www.ncbi.nlm.nih.gov/geo) GSE62564 (29-32) and GSE16237 (33) were downloaded for SNHG16 expression analysis. Kaplan-Meier survival analysis and the log-rank test were performed based on survival times collected from the GSE62564 dataset. All data were analyzed using SPSS 19.0 software (IBM Corp.), and graphs were generated using GraphPad Prism 5.0 (GraphPad Software, Inc.). All results are expressed as the means ± standard deviations. Multiple comparisons were assessed by one-way analysis of variance followed by Bonferroni post hoc test. Student's t-test was performed to analyze differences between two groups. P<0.05 was considered to indicate a statistically significant difference.

Results

SNHG16 expression is positively associated with NB clinical characteristics

To explore the relationship between SNHG16 expression and the pathophysiological features of patients with NB, the GEO dataset GSE62564 (498 samples) was analyzed by stratification analysis based on INSS stage, risk group and MYCN status. SNHG16 was expressed at significantly higher levels in stage 4 tumors compared with in tumors at other stages (Fig. 1A). In addition, it was upregulated in high-risk NB and MYCN amplification subtypes compared with in low-risk NB (Fig. 1B) and MYCN non-amplification subtypes (Fig. 1C). Analysis of another independent dataset, GSE16237 (51 samples), confirmed these results (Fig. 1D and E). Analysis of NB tissue samples (Table I) revealed that SNHG16 expression was increased alongside clinical staging of NB tumor progression (Fig. 1F) and in MYCN-amplified NB (Fig. 1G). These findings validated that SNHG16 expression was positively associated with NB progression.

Figure 1 SNHG16 expression is positively associated with the clinical characteristics of NB. (A-C) SNHG16 expression in samples stratified according to stage, risk group and MYCN status; data were obtained from the GSE62564 dataset. (D and E) SNHG16 expression according to stage and MYCN status; data were obtained from the GSE16237 dataset. (F and G) SNHG16 expression was measured by reverse transcription-quantitative PCR in collected NB samples. Data were obtained from three independent experiments. *P<0.05, **P<0.01, ***P<0.001. MYCN, MYCN proto-oncogene, bHLH transcription factor; NB, neuroblastoma; SNHG16, small nucleolar RNA host gene 16.

Table I Clinical characteristics of patients with neuroblastoma enrolled in this study (n=40).

Table I

Clinical characteristics of patients with neuroblastoma enrolled in this study (n=40).

Parameter	Number of patients (%)
Age at diagnosis (years)
<1.5	10 (25)
1.5-3	7 (17.5)
3-7	18 (45)
>7	5 (12.5)
Sex
Male	21 (52.5)
Female	19 (47.5)
MYCN status
Amplified	8 (20)
Non-amplified	20 (50)
Not clear	12 (30)
Tumor stage
I	3 (7.5)
II	7 (17.5)
III	13 (32.5)
IV	17 (42.5)

[i] MYCN, MYCN proto-oncogene, bHLH transcription factor.

To explore the prognostic value of SNHG16, Kaplan-Meier survival analysis was conducted based on GSE62564 data. Patients with low or high expression of SNHG16 were grouped according to RNA sequencing data. Patients with high expression had worse event-free survival (P<0.0001) and overall survival (P<0.0001) than those with low expression (Fig. 2A and B), thus indicating that SNHG16 may be a prognostic marker for patients with NB.

Figure 2 Kaplan-Meier survival analysis for patients with neuroblastoma in the GSE62564 dataset. Kaplan-Meier curves for (A) event-free survival and (B) overall survival of low-expression versus high-expression groups based on median SNHG16 expression. SNHG16, small nucleolar RNA host gene 16.

SNHG16 silencing inhibits proliferation of SH-SY5Y cells

The gene knockdown efficiency of siRNAs was validated by RT-qPCR. siRNA1-SNHG16 and siRNA2-SNHG16 effectively silenced SNHG16 expression (Fig. 3A). The real-time cell proliferation assay revealed that cell growth in the SNHG16 silencing group was significantly inhibited (Fig. 3B). This was confirmed by the colony formation assay, which demonstrated that SNHG16 silencing markedly reduced colony formation in SH-SY5Y cells (Fig. 3C and D). These findings indicated that SNHG16 may contribute to NB cell proliferation. Both siRNA1 and siRNA2 exhibited effective knockdown efficiency, and were confirmed to inhibit cell proliferation; siRNA1 was used for subsequent experiments.

Figure 3 SNHG16 silencing inhibits cell proliferation in SH-SY5Y cells. (A) mRNA expression levels of SNHG16 in NC and siRNA-SNHG16 groups. (B) Real-time proliferation monitoring of SH-SY5Y cells. (C) Colony formation assay and (D) quantification analysis. Data were obtained from three independent experiments. *P<0.05, **P<0.01, ***P<0.001 vs. the siRNA-NC group. NC, non-targeting control; siRNA, small interfering RNA; SNHG16, small nucleolar RNA host gene 16.

SNHG16 silencing inhibits migration of SH-SY5Y cells

To explore whether SNHG16 affects cell migration, a wound healing assay was conducted using SH-SY5Y cells wounded with a micropipette tip. As shown in Fig. 4A, the wider width in the SNHG16 silencing group suggested that SNHG16 knockdown significantly suppressed the migratory ability of SH-SY5Y cells. Further quantification analysis revealed a significant difference in the width between the SNHG16 silencing group and the control group at 48 and 72 h after wounding (Fig. 4B). Furthermore, the results of a Transwell assay further indicated that the migratory capacity of SH-SY5Y cells transfected with siRNA-SNHG16 was markedly decreased compared with the control group (Fig. 4C). These results suggested that SNHG16 knockdown inhibited migration of SH-SY5Y cells.

Figure 4 SNHG16 silencing inhibits migration of SH-SY5Y cells. (A) Representative wound healing images captured at 0, 24, 48 and 72 h after siRNA transfection. (B) Quantification analysis of wound width at the indicated time points. Data were from three independent experiments. (C) Transwell assay was performed to assess the migration of SH-SY5Y cells transfected with siRNAs. Scale bar, 200 μm. *P<0.05, **P<0.01. NC, non-targeting control; siRNA, small interfering RNA; SNHG16, small nucleolar RNA host gene 16.

SNHG16 silencing induces cell cycle arrest in SH-SY5Y cells

The cell cycle is closely associated with cell proliferation; therefore, flow cytometry was used to investigate the potential effects of SNHG16 on cell cycle alterations. The percentage of cells in G0/G1 phase was significantly increased, whereas the percentage in S phase was decreased in the SNHG16-knockdown group compared with in the control group (Fig. 5A and B; Table II). These results demonstrated that SNHG16 silencing in SH-SY5Y cells induced cell cycle arrest at the G0/G1 phase.

Figure 5 SNHG16 silencing induces cell cycle arrest in SH-SY5Y cells. (A) Flow cytometry was used to detect cell cycle alterations in SH-SY5Y cells after transfection with siRNA-NC or siRNA-SNHG16. (B) Percentage of cells in the G0/G1 phase was significantly increased in the siRNA-SNHG16 group compared with in the siRNA-NC group. Data were obtained from three independent experiments. *P<0.05 vs. the siRNA-NC group. NC, non-targeting control; PI, propidium iodide; siRNA, small interfering RNA; SNHG16, small nucleolar RNA host gene 16.

Table II Cell cycle arrest of SH-SY5Y cells following siRNA-small nucleolar RNA host gene 16 transfection.

Table II

Cell cycle arrest of SH-SY5Y cells following siRNA-small nucleolar RNA host gene 16 transfection.

	Distribution of cell cycle (%)
Group	G0/G1	S	G2/M
NC	67.60±0.65	24.34±1.52	8.08±0.97
siRNA	76.35±0.59a	15.94±0.41a	7.72±0.98

{ label (or @symbol) needed for fn[@id='tfn2-ijo-55-01-0093'] } Data are expressed as the means ± standard deviation from three independent experiments.

a P<0.05. NC, negative control; siRNA, small interfering RNA.

Apoptosis is undetectable in SH-SY5Y cells following SNHG16 silencing

Cellular apoptosis was measured by AO/EB staining following siRNA transfection. As shown in Fig. 6A, the cells in both groups appeared uniformly green with no distinct red-orange staining, indicating no detectable apoptotic cells. However, both cell number and density were reduced in the SNHG16-knockdown group, suggesting that SNHG16 silencing inhibited cell proliferation without apoptosis. Detection of caspase-3/7 activity also revealed that SNHG16 knockdown had no significant effect on caspase-3/7 activity (Fig. 6B). These results indicated that SNHG16 knockdown did not induce apoptosis in NB.

Figure 6 Apoptosis is undetectable in SH-SY5Y cells following SNHG16 silencing. (A) AO/EB staining revealed that cells in both groups appeared uniformly green without exhibiting distinct red-orange staining; however, cell number and density were reduced in the SNHG16-knockdown group. (B) Caspase-3/7 activity was not significantly different between the siRNA-NC and SNHG16-knockdown groups. Data were normalized to the control and were obtained from three independent experiments. Scale bar, 400 μm. AO, acridine orange; EB, ethidium bromide; NC, non-targeting control; siRNA, small interfering RNA; SNHG16, small nucleolar RNA host gene 16.

Implied RNA-related mechanism of SNHG16 function

To explore the underlying molecular mechanism of SNHG16 function, starBase v2.0 was used to identify potential RBPs that bind SNHG16 (34). Predicted target proteins of SNHG16 are listed in Table SI, and functional enrichment analysis was conducted by Cytoscape. SNHG16-related RBPs were revealed to be mainly involved in the regulation of mRNA metabolic processes, gene silencing by miRNA, mRNA transport, RNA splicing and translation (Fig. 7A-C), implying that the molecular mechanism underlying the effects of SNHG16 is associated with RNA-related processes.

Figure 7 Functionally grouped Gene Ontology and pathway annotation of small nucleolar RNA host gene 16-related RBPs by ClueGO. (A) RBPs were functionally grouped and visualized in a network. (B) Overview chart presents functional groups for the RBPs. The name of the group is given by the group leading term (the most significant term in the group). (C) The chart presents the specific terms for the RBPs. The bars represent the number of genes from the analyzed cluster found to be associated with the term, and the label displayed on the bars is the percentage of found genes compared to all the genes associated with the term. RBP, RNA-binding protein.

Discussion

The results of the present study revealed that SNHG16 expression was associated with the clinical progression and prognosis of NB. In addition, SNHG16 silencing inhibited NB cell proliferation and migration, and induced cell cycle arrest at G0/G1 phase in SH-SY5Y cells.

lncRNAs have been reported to serve as signals of specific cellular states, and may identify cellular pathologies, provide prognostic value and predict therapeutic options for patients with cancer (35). The differential regulation of lncRNAs relative to coding genes may underlie the high degree of tissue-specific lncRNA expression. Furthermore, previous studies have suggested that the expression and dysregulation of lncRNAs are cancer type-specific compared with protein-coding genes (36,37). This suggests that lncRNAs should be studied separately in different tumor types.

Sustaining proliferation and metastasis are the most fundamental features of tumors (38). The present results indicated that SNHG16 may contribute to NB progression. Using GEO datasets, SNHG16 upregulation was revealed to be associated with clinical staging of NB, and this was validated in a series of NB samples. SNHG16 knockdown significantly inhibited proliferation and migration of SH-SY5Y cells, whereas patients with high SNHG16 expression had poor overall survival. Previous studies have indicated that SNHG16 promotes tumor proliferation via a diverse range of mechanisms. For example, SNHG16 contributes to cervical cancer by directly targeting miR-216-5p (39), and enhances tumor proliferation through epigenetically silencing p21, which acts as a tumor suppressor in bladder cancer (21,40). SNHG16 has also been reported to be involved in the Wnt pathway in colorectal cancer and affects genes involved in lipid metabolism (19). Notably, lncRNAs have both positive and negative effects on NB proliferation. For example, high expression of lncRNAs, such as MYCN upstream transcript, account for NB tumorigenesis (38), whereas lincNeD-125 negatively controls NB proliferation and activates the anti-apoptotic factor BCL2 (15).

The primary aim of this study was to investigate the effects and underlying mechanism of SNHG16 in NB proliferation, and a series of experiments were performed. Notably, MYCN amplification is an independent marker for NB prognosis and risk stratification (7,8). Existing evidence has suggested that SNHG16 is upregulated in a MYCN amplification cell line and in NB samples (17,18). Therefore, MYCN amplification may affect SNHG16 expression and subsequent function. In line with previous findings, the present results demonstrated that the expression of SNHG16 was increased in MYCN-amplified NB samples compared with in MYCN non-amplified NB. A MYCN non-amplified cell line was selected to examine the role of SNHG16 in NB, because SH-SY5Y is a representative cell line of NB, according to the ATCC, and it has been widely used in mechanistic and drug development studies regarding NB (23,24). The potential link between SNHG16 and MYCN amplification requires independent research for further clarification.

Various biological processes are associated with cell proliferation, including DNA damage, autophagy, apoptosis and the cell cycle (41). In the present study, SNHG16 silencing was revealed to affect cell cycle progression and lead to G0/G1 arrest in SH-SY5Y cells. This result supports a previous finding that SNHG16 silencing is able to arrest gastric cancer cells at G0/G1 (42), as well as the reports that many lncRNAs are associated with G0/G1 block in various tumor types. For example, Yang et al (43) reported that hepatocellular carcinoma up-regulated EZH2-associated lncRNA is highly expressed in hepatitis B virus-related hepatocellular carcinoma and negatively regulates the expression of cyclin-dependent kinase inhibitors. Furthermore, Ye et al (44) reported that lncRNA bladder cancer-associated transcript 1 silencing induces G0/G1 arrest through sponging miR-144. Therefore, cell cycle control may be a potential mechanism underlying SNHG16-mediated NB proliferation.

Sometimes cell cycle arrest is beneficial for DNA repair to maintain homeostasis; however, it can also cause cellular damage. Cells are equipped with cell cycle checkpoints to maintain genome stability; when cells have DNA damage to be repaired, or DNA replication is not complete, these checkpoints arrest the cell cycle (45,46). This kind of cell cycle delay offers more time for the repair of DNA damage and cellular homeostasis. However, when cellular injuries are so severe that they exceed the cellular repair capacity, apoptosis may occur. Blocking the cell cycle is often associated with apoptosis; however, in this study, apoptosis was not detected by AO/EB staining and caspase-3/7 activity assay following SNHG16 silencing. This finding contradicts the previous findings of Christensen et al (19); this previous study reported that SNHG16-induced apoptosis is dependent on caspase-3/7 activity in colorectal cancer cells. In addition, Lu et al (47) demonstrated that SNHG16 knockdown inhibits viability and induces apoptosis in glioma cells. Because lncRNA function is highly cancer type-specific compared with protein-coding genes (48,49), this discrepancy may be accounted for by the different cell types or regulatory mechanisms between these studies and the present study.

The mechanisms underlying the effects of lncRNAs are highly extensive and complex, and may involve protein or DNA interactions (50). lncRNAs typically function through RBPs, including the polycomb-group proteins, heterochromatin protein 1 and DNA methyltransferases, whose expression fluctuates in tumor samples and therefore may provide prognostic clues (51). Chen et al (52) reported that the lncRNA MALAT1 regulates p53 through its interaction with cell cycle and apoptosis regulator 2, whereas RNA pulldown and RNA immunoprecipitation assays have demonstrated that the physical association of colon cancer-associated transcript 1 with Livin inhibits apoptosis in renal cell carcinoma (53). In the present study, bioinformatics analysis predicted various RBPs that may interact with SNHG16, and functional enrichment analysis demonstrated that the potential RBPs regulated transcription and translation. However, to confirm these predictions, in-depth study and molecular experiments are urgently required. Nevertheless, the present results suggested that SNHG16 regulated cell proliferation, migration and the cell cycle in NB through multiple pathways associated with transcription and translation.

In conclusion, the present study demonstrated that high expression of SNHG16 was associated with NB progression and poor clinical outcome. SNHG16 silencing inhibited cell proliferation and migration, and induced cell cycle arrest at G0/G1. Bioinformatics analysis revealed that SNHG16 regulated the proliferation of NB cells through multiple pathways associated with transcription and translation. Therefore, SNHG16 may serve as a molecular marker for NB therapy or prognosis.

Supplementary Materials

Funding

This work was supported by the National Natural Science Foundation of China (grant nos. 81702463 and 81702787) and the Beijing Health System Top Level Technical Personnel Training Plan (grant no. 20153079).

Availability of data and materials

All data generated or analyzed in this study are included in this published article.

Authors' contributions

YYu and FC performed most of the experiments and wrote the manuscript. YYa, YJ JS and JL performed some experiments. JT, SW and WY collected the clinical data and tissue samples. PC and SH performed the statistical analysis. YG, XN and HW designed the experiments and edited the manuscript. All authors have read and approved the final manuscript.

Ethics approval and consent to participate

This study was approved by the Ethics Committees of Beijing Children's Hospital, and followed the principles of the Helsinki Declaration II. Written informed consent was obtained from the legal guardians of the patients.

Patient consent for publication

The legal guardians of the patients involved in this study provided written informed consent prior to inclusion in the study.

Competing interests

The authors declare they have no competing interests.

Acknowledgments

The authors would like to thank Sarah Williams, PhD, for language editing a draft of this manuscript.

References