Next Article in Journal
Size Effect in Hybrid TiO2:Au Nanostars for Photocatalytic Water Remediation Applications
Next Article in Special Issue
Upregulated FKBP1A Suppresses Glioblastoma Cell Growth via Apoptosis Pathway
Previous Article in Journal
Review on Catalytic Oxidation of VOCs at Ambient Temperature
Previous Article in Special Issue
METCAM/MUC18 Plays a Tumor Suppressor Role in the Development of Nasopharyngeal Carcinoma Type I
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 23
Issue 22
10.3390/ijms232213740
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

GDPD5 Related to Lipid Metabolism Is a Potential Prognostic Biomarker in Neuroblastoma

by
Tengling Luo
1,†,
Junwei Peng
2,†,
Qijun Li
1,
Yao Zhang
1,
Yun Huang
1,
Lei Xu
1,
Genling Yang
1,
Dongmei Tan
1,
Qian Zhang
1,* and
Yi Tan
1,*
1
Laboratory Animal Center, Chongqing Medical University, Chongqing 400016, China
2
Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Department of Pediatric Surgical Oncology, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children’s Hospital of Chongqing Medical University, Chongqing 400016, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2022, 23(22), 13740; https://doi.org/10.3390/ijms232213740
Submission received: 26 September 2022 / Revised: 26 October 2022 / Accepted: 27 October 2022 / Published: 8 November 2022
(This article belongs to the Collection Feature Papers in Molecular Oncology)
Browse Figures
Versions Notes

Abstract

:
Neuroblastoma (NB) is an extracranial solid tumor in children with poor prognosis in high-risk patients and its pathogenesis and prognostic markers urgently need to be explored. This study aimed to explore potential biomarkers related to NB from the aspect of lipid metabolism. Fifty-eight lipid metabolism-related differentially expressed genes between high-risk NB and non-high-risk NB in the GSE49710 dataset were analyzed using bioinformatics, including 45 down-regulated genes and 13 up-regulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis identified steroid hormone biosynthesis as an abnormal metabolic pathway in high-risk NB. Survival analysis established a three-gene prognostic model, including ACHE, GDPD5 and PIK3R1. In the test data, the AUCs of the established prognostic models used to predict patient survival at 1, 3 and 5 years were 0.84, 0.90 and 0.91, respectively. Finally, in the SH-SY5Y cell line, it was verified that overexpression of GDPD5 can inhibit cell proliferation and migration, as well as affect the lipid metabolism of SH-SY5Y, but not the sugar metabolism. hsa-miR-592 was predicted to be a potential target miRNA of GDPD5 by bioinformatics. In conclusion, this study develops a lipid-metabolism-related gene-based prognostic model for NB and demonstrates that GDPD5 inhibits SH-SY5Y proliferation and migration and may be targeted by hsa-miR-592 and inhibit SH-SY5Y fat synthesis.

1. Introduction

Neuroblastoma (NB) is a malignant childhood solid tumor derived from neuroectodermal cells of the sympathetic nervous system and is the leading cause of childhood cancer-related death [1]. Even when high-risk NB patients, especially those with metastases, are treated comprehensively, the prognosis remains poor. Thanks to various new patient stratification, new therapies and new drugs, the five-year survival rate for metastatic NB has improved dramatically over the past few decades [2]. Currently, the clinical outcome of NB is mainly predicted based on age, tumor stage, mitotic nuclear rupture index, MYCN (Neuroblastoma MYC Oncogene) amplification and ALK (Anaplastic Lymphoma Kinase) expression [3,4]. However, limitations remain in the risk stratification of NB patients. Therefore, there is an urgent need to explore new prognostic markers.
Recent studies have shown that lipid metabolism may be one of the important differences between cancer cells and normal cells and lipid metabolites may be important cancer markers. The growth, proliferation, invasion and metastasis of cancer cells can be affected by metabolic reprogramming, such as changes in lipid metabolism. This has been shown to occur in tumor cells and the tumor microenvironment [5,6,7]. Positron emission tomography in cancer patients has shown that NB tumors have high glucose uptake and high lactate production rates [8]. Moreover, inhibition of fatty acid synthesis results in increased neural differentiation and reduced tumor burden in NB xenograft experiments [9]. Rugolo et al. showed that very long-chain fatty acid protein 4 (ELOVL4) positively regulates neuronal differentiation and lipid droplet accumulation in NB cells and that high ELOVL4 expression is a marker of better overall clinical survival [10]. However, the roles and prognostic value of lipid-metabolism-related genes in NB remain to be elucidated, as the lack of a large-scale NB sample cohort limits the reliability and validity of previous findings. However, in the era of big data, the emergence of genome sequencing technology and data may help tumor diagnosis and prognosis prediction [11,12].
GDPD5 is a glycerophosphocholine phosphodiesterase identified in a family of bacterial genes [13], which is expressed in neurons, terminally differentiated oligodendrocyte subsets and vascular endothelium [14,15,16] and is critical for neuronal differentiation [17], growth and survival [18]. Recent studies have shown that GDPD5 promotes NB differentiation by releasing glypican [19].
In this study, the expression profiles of lipid-metabolism-related genes in the Gene Expression Omnibus (GEO) database (GSE49710 dataset) were downloaded and analyzed to identify differentially expressed genes in high-risk versus non-high-risk NBs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that genes were mainly involved in steroid hormone biosynthesis. Next, using the Least Absolute Shrinkage and Selection Operator (LASSO) and the multivariate Cox algorithm, we developed a risk score for predicting the prognosis of NB patients using the GSE49710 dataset. Finally, we validated the correlation of GDPD5 in risk scoring models with lipid metabolism in tumor cells and tumor malignancy through biological experiments. In conclusion, we built a three-gene trait model that can be used as a predictor of NB survival. In addition, the effects of GDPD5 on lipid metabolism, proliferation and migration of NB were verified by biological experiments.

2. Results

2.1. Identification of Differentially Expressed Genes Related to Lipid Metabolism between High-Risk Groups and Non-High-Risk Groups and Patient Survival in Neuroblastoma (NB)

According to the method of Zheng et al., human lipid-metabolism-related pathways were downloaded from the molecular signature database (version 7.0). Herein, a total of 776 lipid metabolism genes was sorted out from the six lipid metabolism pathways from the databases KEGG and Reactome [20]. We obtained a total of 776 lipid metabolism genes (Table S1). We then explored differentially expressed genes between high-risk and non-high-risk NB samples in dataset GSE49710. The cohort and clinicopathological information in the 498 NB samples of the GSE49710 dataset are shown in Table 1. We found 58 lipid-metabolism-related genes that were significantly associated with NB risk and survival (Figure 1A,B), of which 13 were up-regulated and 45 were down-regulated. GO analysis showed that these genes were mainly involved in a cellular lipid metabolic process and lipid metabolic process, most of which were located in the endomembrane system and were associated with catalytic activity and anion binding (Figure 1C). KEGG analysis showed that these genes were mainly involved in metabolic pathways, steroid hormone biosynthesis and arachidonic acid metabolism (Figure 1D). The protein interaction network also showed complex correlations among these 58 genes (Figure 1E). These results suggest that lipid metabolism biological processes play an important role in high-risk NB.

2.2. Construction of a Prognostic Model for Lipid-Metabolism-Related Genes

Difference analysis and survival analysis showed that these lipid-metabolism-related genes were significantly associated with NB risk (Figure A1A) and overall survival time (Figure A1B). Next, we performed LASSO Cox regression analysis to integrate survival time, survival status and gene expression data to obtain the optimal survival prediction model (Figure 2A–C). Finally, when the Lambda value is 0.120812060485213, the model formula constructed by six genes is obtained:
Risk Score = −a*ACHE−b*CROT−c*GDPD5−d*HSD17B3−e*PIK3R1−f*PRKACB.
where:
a = 0.0345687415492563
b = 0.0489503629082964
c = 0.302176914910914
d = 0.0321116010448932
e = 0.0978743044989993
f = 0.0915003516148756.
Kaplan–Meier analysis showed that the risk score could effectively distinguish patients with better and worse prognosis (Figure 2D). ROC curve analysis (Figure 2E) showed that the area under the curve (AUC) values for the 1-, 3- and 5-year OS of the risk score were higher than those of individual genes (Figure A2), indicating that the prognostic power of risk score was better than that of individual genes alone.

2.3. Further Refinement of the Prognostic Model

To further optimize the prognostic model, we performed multivariate Cox analysis on the six genes (ACHE, CROT, GDPD5, HSD17B3, PIK3R1, PRKACB) in the risk score formula. The results showed that ACHE, GDPD5 and PIK3R1 were independent prognostic factors (Figure 3A). We then constructed a new risk score using these three genes:
Risk Score = ACHE*0.862854240578636 + GDPD5*0.777001638391889 + PIK3R1*0.646763659363925.
where:
a = 0.862854240578636
b = 0.777001638391889
c = *0.646763659363925.
Interestingly, the ROC curve of the new scoring formula, composed of ACHE, GDPD5 and PIK3R1 (Figure 3B), was similar to the ROC curve composed of the previous six genes (Figure 2E) and better than the ROC curve of any single gene among them (Figure A2). In addition, the new scoring formula and ACHE, GDPD5 and PIK3R1 as independent prognostic factors can effectively distinguish populations with different prognosis (Figure 3C–F). This allows us to reduce the number of genes without affecting the AUC value, resulting in better clinical applicability.
Further, we investigated the optimal cutoff (Table 2) and median cutoff (Table 3) to evaluate the accuracy of risk score in distinguishing high-risk or non-high-risk (low and medium risk) children, respectively. The results showed that the optimal cut-off value grouping method had an accuracy of 85.23% and 93.48% in identifying children in the high-risk group and the non-high-risk group, respectively. The median stage grouping methods were 95.45% and 74.84%, respectively. The median grouping method was better at identifying high-risk children, but lower than the optimal cut-off grouping method at identifying non-high-risk children.

2.4. Independent Prognostic Factor GDPD5 Is Associated with Immune Infiltration

Among three important independent prognostic genes (ACHE, GDPD5, PIK3R1), GDPD5 caught our attention. GDPD5 was previously shown to promote neurogenesis and it was recently shown to promote NB cell differentiation in an autonomous manner [19]. We first examined oncological signatures and KEGG signaling pathways associated with GDPD5 expression. The results showed that samples with high GDPD5 expression were enriched in cysteine and methionine metabolism and nucleotide excision repair (Figure 4A), as well as two oncological features (Figure 4B). It is generally believed that the degree of immune cell infiltration is related to the occurrence, development, treatment and clinical prognosis of tumors [21,22]. The tumor microenvironment that constitutes various immune cell subsets influences the antitumor effect of immunotherapy [23]. Therefore, it is necessary to observe whether the expression of GDPD5 correlates with the immune infiltration of NB. Our results showed that GDPD5 was not only associated with stromal score (Figure 4C), immune score (Figure 4D) and estimate score (Figure 4E), but also with the majority of immune cell infiltration (Figure A3). These results suggest that GDPD5 is not only related to lipid metabolism in NB, but also to its immune status.

2.5. Hsa-miR-592 Is a Potential Target miRNA of GDPD5

MicroRNAs (miRNAs) are small noncoding RNAs that broadly regulate gene expression in animals, plants and protozoa. miRNAs typically act post-transcriptionally by base-pairing to the 3′-untranslated region of mRNA to inhibit protein synthesis through a mechanism that is not fully understood [24]. First, we predicted GDPD5-targeted miRNAs in the miRSystem system (Table S2) and then identified 193 differentially expressed miRNAs (DEMs) in GSE121513 between patients in the high-risk group and non-high-risk group and obtained 2 miRNAs after taking the intersection upregulated DEM (hsa-miR-107, hsa-miR-592) and 2 downregulated (DEM) (hsa-miR-604, hsa-miR-636) (Figure 5A). Then, Kaplan–Meier analysis showed that hsa-miR-592 could effectively distinguish high-risk and low-risk groups (Figure 5B), but not hsa-miR-604 (Figure 5C). GDPD5 was down-regulated in high-risk patients, whereas miRNA-silencing mRNAs were generally expressed in the opposite manner. That is to say, the two down-regulated DEMs (hsa-miR-604 and hsa-miR-636) are not target miRNAs of GDPD5.

2.6. Overexpression of GDPD5 Affects Lipid Metabolism, Migration and Proliferation of SH-SY5Y Cell Line

SH-SY5Y cells were established in 1970 and are often used as a cell model of neuroblastoma [25]. To further explore the biological function of GDPD5, we overexpressed GDPD5 in the human SH-SY5Y cell line (Figure 6A) and then examined the changes in its metabolism, as well as the migration and proliferation behavior of cancer cells. ACC, ACLY, HADH and PPARA are energy and lipid enzymes [26,27,28] and our Western blot results indicated that overexpression of GDPD5 resulted in a reduction in ACC (acetyl-coenzyme A carboxylase), a key enzyme in fat synthesis, but not the other (Figure 6B). Meanwhile, we detected changes in the glucose metabolism of SH-SY5Y by detecting PFKFB3, ALDOA, ENO2, HK2 and LDHA [29,30,31,32,33]. The results showed that overexpression of GDPD5 hardly affected the changes in SH-SY5Y glucose-metabolism-related markers (Figure 6C). Furthermore, overexpression of GDPD5 inhibited the migration and proliferation of SH-SY5Y cells (Figure 7). This suggests that GDPD5 may affect the lipid synthesis of the SH-SY5Y cell line by reducing ACC, thereby inhibiting the migration and proliferation of SH-SY5Y.

3. Discussion

According to the Children’s Oncology Group (COG) risk stratification, neuroblastoma (NB) can be divided into three grades: low, intermediate and high [34]. The five-year survival rate for low- and intermediate-risk patients is higher than 90%, while the long-term survival rate for high-risk patients is less than 50%, even after multiple approaches [35]. Therefore, it is necessary to explore new prognostic indicators to improve the prognosis of high-risk NB patients. The arrival of second-generation sequencing technology and the era of big data have provided new ideas for the prognosis evaluation of NB patients. Risk profiles related to MYCN [36,37], immunity [38] and glycosyltransferases are emerging [39]. Numerous studies have shown that lipid metabolism is related to cancer progression, metastasis and treatment and has the potential to become a new biomarker [40,41,42]. Risk scores of lipid-metabolism-related genes have been constructed to predict the survival rate of colon adenocarcinoma [43], lung adenocarcinoma [44], bladder cancer [5] and serous ovarian carcinomas [20]. However, the association between lipid-metabolism-related risk models and prognosis in NB patients remains unknown. To our knowledge, this study is the first to identify lipid-metabolism-related genes associated with NB prognosis using a bioinformatic approach. In this study, we identified 58 differentially expressed genes associated with risk stratification in NB patients in the GEO dataset (GSE49710). In addition, these genes were mainly enriched in steroid hormone biosynthesis and arachidonic acid metabolism. We identified a risk score, including ACHE, GDPD5 and PIK3R1, using LASSO and multivariate Cox regression analysis. Interestingly, GDPD5 was also included in the Nomogram constructed by Xia et al. [37]. However, the AUC values of our risk score ROC curves at 1, 3 and 5 years were 0.84, 0.90 and 0.91, respectively, which were higher than those of Xia et al. (0.754, 0.815, 0.795). This means that our three-gene risk score has better prognostic power.
GDPD5 maps to chromosome 11q13 and frequently shows loss of heterozygosity in high-risk NB [45], often associated with later stages and worse outcomes in NB [46]. GDPD5 induces NB cell differentiation and inhibits its motility through multiple mechanisms [19]. In this study, based on bioinformatics predictions, we identified hsa-miR-592 as a potential target miRNA of GDPD5. Our biological experiments showed that overexpression of GDPD5 would inhibit the expression of Acetyl-CoA Carboxylase Alpha (ACC, a key rate-limiting enzyme in lipid synthesis) in the human NB cell line SH-SY5Y, without affecting the expression of glucose-metabolism-related genes. In addition, overexpression of GDPD5 also inhibited the migration and proliferation of SH-SY5Y. Based on these results, GDPD5 will qualify as a potential tumor-suppressor gene. However, this study also has several shortcomings. First, the constructed risk scoring formula was not validated with a large sample. Second, the regulation of GDPD5 by hsa-miR-592 was not verified by biological experiments. Collectively, the findings of this study provide new insights into the occurrence and development of NB from the perspective of lipid metabolism. A prognostic model based on three lipid-metabolism-related genes can effectively predict the prognosis of NB patients. In addition, simple biological experiments suggest that GDPD5 affects the lipid metabolism, migration and proliferation of NB and is a potential biomarker for high-risk NB.

4. Materials and Methods

4.1. Data Extraction from Online Databases

The gene expression data and clinical information of GSE49710 and GSE121513 were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/ (accessed on 23 May 2022)), based on the GPL16876 and GPL25696 platforms, respectively. GSE49710 has a total of 498 NB samples, including RNA-seq and clinical information (sex, age, International Neuroblastoma Staging System (INSS) stage, MYCN amplification status and overall survival) [47]. GSE121513 contains 117 samples with COG risk stratification and overall survival. Ensemble IDs were converted to corresponding gene symbols using the clusterProfiler R package. These gene expression matrices were then quantile normalized and log2 transformed for subsequent analysis.

4.2. Screening of Differentially Expressed miRNAs and Lipid Metabolism Genes

Differential expression analysis was performed on the gene expression matrix after GSE121513 and GSE49710 treatment to screen genes for differentially expressed miRNAs (DEMs) and lipid metabolism genes between high-risk and non-high-risk groups. The “limma” [48] R package was utilized and genes were identified as significant DEGs if |log2FC| value > 1 and false-discovery rate (FDR) < 0.05. Next, univariate Cox regression analysis was applied to identify DEGs associated with prognosis in NB patients.

4.3. Functional Enrichment Analysis and Protein–Protein Interaction Network Construction

This study used David (v.6.8, https://david.ncifcrf.gov/ (accessed on 23 May 2022)) to complete the enrichment analysis of DEGs, aiming to initially understand the biological functions of these genes. p < 0.05 and FDR < 25% were determined to be significant. The STRING (v 11.0, https://string-db.org (accessed on 23 May 2022)) database was used to construct protein–protein interaction (PPI) networks. Next, the networks were visualized using Cytoscape software (version 3.8.2).

4.4. Construction of a Prognostic Score for Lipid-metabolism-related Genes

LASSO is a shrinkage estimation method whose basic premise is to obtain a shrinkage subset by limiting the coefficients of some features to zero to minimize the residual sum of squares under the constraints. In this study, the LASSO Cox regression model in the “glmnet” R package was applied to identify DEGs associated with overall survival. Next, we obtain the beta coefficient risk score of each gene at the optimal λ value. The risk score is equal to the sum of the expression levels of each gene multiplied by the corresponding beta coefficients. Patients were divided into high-risk and low-risk groups according to the median cut-off value of risk score. The prognostic power of risk score was assessed by Kaplan–Meier analysis and ROC curve.

4.5. Gene Set Enrichment Analysis (GSEA) and miRNA Prediction

We performed GSEA on gene expression profiles based on high- and low-GDPD5 groups (https://www.gsea-msigdb.org/gsea (accessed on 23 May 2022)) [49]. First, we download a subset of c2.cp.kegg.v7.4.symbols.gmt to evaluate relevant pathways and molecular mechanisms. Then, we ranked by normalized enrichment score (NES) and visualize the results. p < 0.01, FDR < 0.25 was considered significant. MiRSystem (http://mirsystem.cgm.ntu.edu.tw/ (accessed on 23 May 2022)) was used to predict target miRNAs of GDPD5 [50].

4.6. Immune Infiltration and Immune Microenvironment Assessment

CIBERSORT (https://cibersortx.stanford.edu/ (accessed on 23 May 2022)) was used to assess the relationship between GDPD5 and the immune microenvironment [51]. ESTIMATE (https://sourceforge.net/projects/estimateproject/ (accessed on 23 May 2022)) was used to evaluate the relationship between GDPD5 and tumor immune infiltration [52]. EPIC (Immune Cell http://epic.gfellerlab.org/ (accessed on 23 May 2022)) was used to evaluate the relationship between GDPD5 and tumor immune cell content [53].

4.7. Plasmids Construction and Cell Culture Transfection

The GDPD5 (NM_001351167.2) coding sequences (CDS) were amplified with polymerase chain reaction (PCR) in vitro from the cDNA of HEK293T cells and were cloned into the CMV-flag vector by using restriction enzyme EcoRI and XhoI. The CMV-flag-GDPD5 plasmid was sequenced and aligned to confirm the DNA sequence was completely cloned (BGI, Chongqing, China).
The SH-SY5Y cell was grown in Dulbecco’s Modified Eagle Medium (DMEM) (Gibco, Carlsbad, CA, USA) supplemented with 10% fetal bovine serum (Gibco, Carlsbad, CA, USA) and 1% penicillin/streptomycin (PS) (Invitrogen, Grand Island, NY, USA). The cell was maintained at 37 °C in a 5% CO2 incubator. The Flag-CTL or Flag-GDPD5 plasmids were transfected into SH-SY5Y cells with Neofect™ DNA transfection reagent (Neofect biotech, Beijing, China), according to manufacturer’s protocol. Briefly, culture the SH-SY5Y cells with complete medium in 35 mm dish, dilute 2 μg DNA in Opti-MEM, add 2 μL Neofect™ gentle mixing, then incubate 25 min at RT and, finally, add transfection mix to plate and incubate at 37 °C in a 5% CO2.

4.8. Western Blot Analysis

The Flag-CTL and Flag-GDPD5 plasmids were transfected into SH-SY5Y cells with Neofect™ DNA transfection reagent. After cells were transfected for 48 h, the cells were lysed by 1% SDS lysing buffer containing Protease Inhibitor Cocktail and Phosphatase Inhibitor Cocktail (Apexbio, Houston, TX, USA) for Western blot analysis. The protein concentration was determined by a BCA protein assay reagent kit (Thermo Scientific, Waltham, MA, USA). All blots were, respectively, incubated with primary anti-bodies anti-flag (1:1000, ABclonal, Wuhan, China), anti-p-ACC, anti-ACC, Anti-p-ACLY, anti-ACLY, anti-PFKFB3, anti-HK2 and anti-LDHA (1:1000, Cell Signaling Technology, MA, USA), anti-ALDOA, anti-ENO2, anti-HADH and anti-PPARA (1:1000, proteintech, Wuhan, China), as well as anti-β-Tubulin (1:5000, TRANSGEN, Beijing, China). Bands were visualized with ECL Reagents (Smart-Lifesciences, Changzhou, China).

4.9. Transwell Assay

To confirm the effect of GDPD5 on SH-SY5Y cell migration, the Flag-CTL and Flag-GDPD5 plasmids were transfected into SH-SY5Y cells with Neofect™ DNA transfection reagent. After the cells were transfected for 48 h, a total of 1 × 104 cells diluted in DMEM medium without FBS was plated into transwell chamber (Corning, NYS, USA), then the chamber was plated in 24-well plate supplemented with complete medium. After 24 h, the membrane was fixed with 4% paraformaldehyde for 30 min, washed with PBS for 5 min × 3, then the membrane was stained with 0.1% crystal violet (Sangon Biotech, Shanghai, China). Finally, the eight fields were captured randomly under a microscope (DMI8, Leica, Wetzlar, Germany). The quantitative analysis for migration was analyzed by Image J software (National Institutes of Health, Bethesda, MD, USA).

4.10. Cell Viability Assay

After cells were transfected for 48 h, the cell counting kit-8 (CCK-8) assay kits (MedChem Express, NJ, USA) were used for cell viability analyze. The Flag-CTL or Flag-GDPD5 cells were seeded in 96-well plates at a density of 2 × 104 cells per well and cultured for 0 h, 6 h and 12 h. Then the DMEM medium (100 μL) and CCK8 solution (10 μL) were added to each well and incubated for 1 h. Finally, the absorbance of each well was measured at 450 nm using a microplate reader (Thermo Scientific, Waltham, MA, USA). A two-tailed paired or unpaired t-test statistical analysis was performed using GraphPad Prism 8 software (GraphPad, San Diego, CA, USA).

4.11. Statistical Analysis

R 4.0.2 was used for statistical analysis. The relationship between risk score or GDPD5 expression level and immune score, immune infiltration and immune cells was calculated using Spearman or Pearson correlation analysis. Kaplan–Meier analysis can compare survival status between groups. ROC curve analysis can test the predictive ability of the model. Unless otherwise stated, p < 0.05 was considered statistically significant.

5. Conclusions

Through various bioinformatic analyses of high-throughput sequencing datasets, we systematically assessed lipid-metabolism-related molecular features and prognostic value in NB and constructed a risk score composed of lipid-metabolism-related genes that independently predicted the prognosis of NB. It is biologically proved that GDPD5 is involved in lipid metabolism, migration and proliferation of NB, providing preliminary evidence for the complex biological function and immune regulation of GDPD5 involved in lipid metabolism in neuroblastoma. Our findings will help reveal the pathogenesis of NB and identify novel biomarkers and provide a basis for developing therapeutic strategies targeting lipid metabolism.

Supplementary Materials

The supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms232213740/s1.

Author Contributions

T.L. and J.P. performed bioinformatics analysis, Q.L. and Y.Z. were in charge of data collection, Y.H. and L.X. performed software analysis, T.L. and J.P. performed cell biology experiments, G.Y. and D.T. were in charge of data curation, Q.Z. and Y.T. conceived and designed this article, were responsible for syntax modification and article revision. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Scientific and Technological Research Program of Chongqing Municipal Education Commission, Grant/Award Number: KJQN202100411, Chongqing Medical University Science Foundation, Grant/Award Number: R16003.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data used in this study can be downloaded from Gene Expression Omnibus (GEO), NCBI’s publicly available genomics database (https://www.ncbi.nlm.nih.gov/geo/ (accessed on 23 May 2022)).

Acknowledgments

Thanks to all the staff who contributed to the GEO databases. Thanks to the reviewers and editors for their sincere comments.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

NBNeuroblastoma
DEGsDifferentially Expressed Genes
PPIProtein–protein interaction
FCFold Change
GEOGene Expression Omnibus
GOGene Ontology
KEGGKyoto Encyclopedia of Genes and Genomes
DAVIDDatabase for Annotation, Visualization and Integrated Discovery
STRINGSearch Tool for The Retrieval of Interaction Genes
OSOverall Survival
DFIDisease free interval
DFSDisease free survival

Appendix A

Ijms 23 13740 g0a1 550
Figure A1. (A) Gene expression heatmap of Top–100 (up– and down–regulated) differentially expressed genes. (B) Prognostic survival analysis of 58 genes.
Figure A1. (A) Gene expression heatmap of Top–100 (up– and down–regulated) differentially expressed genes. (B) Prognostic survival analysis of 58 genes.
Ijms 23 13740 g0a1
Ijms 23 13740 g0a2 550
Figure A2. (A–F) is Kaplan–Meier analysis of ACHE, CROT, GDPD5, HSD17B3, PIK3R1, PRKACB in order.
Figure A2. (A–F) is Kaplan–Meier analysis of ACHE, CROT, GDPD5, HSD17B3, PIK3R1, PRKACB in order.
Ijms 23 13740 g0a2
Ijms 23 13740 g0a3 550
Figure A3. (A–H) is the relationship between the expression of GDPD5 and the infiltration of 8 kinds of immune cells. The horizontal axis is the expression of GDPD5 and the vertical axis is immune cells.
Figure A3. (A–H) is the relationship between the expression of GDPD5 and the infiltration of 8 kinds of immune cells. The horizontal axis is the expression of GDPD5 and the vertical axis is immune cells.
Ijms 23 13740 g0a3

References

  1. Maris, J.; Hogarty, M.; Bagatell, R.; Cohn, S. Neuroblastoma. Lancet 2007, 369, 2106–2120. [Google Scholar] [CrossRef]
  2. Qiu, B.; Matthay, K.K. Advancing therapy for neuroblastoma. Nat. Rev. Clin. Oncol. 2022, 19, 515–533. [Google Scholar] [CrossRef]
  3. Chang, H.; Lu, M.; Yang, Y.; Chou, S.; Lin, D.; Lin, K.; Hsu, W.; Jeng, Y.; Jou, S. The prognostic roles of and correlation between and protein expression in neuroblastoma. J. Clin. Pathol. 2020, 73, 154–161. [Google Scholar] [CrossRef] [PubMed]
  4. Sokol, E.; Desai, A.V.; Applebaum, M.A.; Valteau-Couanet, D.; Park, J.R.; Pearson, A.D.; Schleiermacher, G.; Irwin, M.S.; Hogarty, M.; Naranjo, A.; et al. Age, Diagnostic Category, Tumor Grade, and Mitosis-Karyorrhexis Index Are Independently Prognostic in Neuroblastoma: An INRG Project. J. Clin. Oncol. 2020, 38, 1906–1918. [Google Scholar] [CrossRef] [PubMed]
  5. Zhu, Y.; Lin, X.; Zhou, X.; Prochownik, E.V.; Wang, F.; Li, Y. Posttranslational control of lipogenesis in the tumor microenvironment. J. Hematol. Oncol. 2022, 15, 120. [Google Scholar] [CrossRef]
  6. Agostini, M.; Melino, G.; Habeb, B.; Calandria, J.M.; Bazan, N.G. Targeting lipid metabolism in cancer: Neuroblastoma. Cancer Metastasis Rev. 2022, 41, 255–260. [Google Scholar] [CrossRef]
  7. Yang, F.; Hu, A.; Guo, Y.; Wang, J.; Li, D.; Wang, X.; Jin, S.; Yuan, B.; Cai, S.; Zhou, Y.; et al. p113 isoform encoded by CUX1 circular RNA drives tumor progression via facilitating ZRF1/BRD4 transactivation. Mol. Cancer 2021, 20, 123. [Google Scholar] [CrossRef]
  8. Shulkin, B.L.; Mitchell, D.S.; Ungar, D.R.; Prakash, D.; Dole, M.G.; Castle, V.P.; Hernandez, R.J.; A Koeppe, R.; Hutchinson, R.J. Neoplasms in a pediatric population: 2-[F-18]-fluoro-2-deoxy-D-glucose PET studies. Radiology 1995, 194, 495–500. [Google Scholar] [CrossRef]
  9. Ruiz-Pérez, M.V.; Sainero-Alcolado, L.; Oliynyk, G.; Matuschek, I.; Balboni, N.; Ubhayasekera, S.K.A.; Snaebjornsson, M.T.; Makowski, K.; Aaltonen, K.; Bexell, D.; et al. Inhibition of fatty acid synthesis induces differentiation and reduces tumor burden in childhood neuroblastoma. iScience 2021, 24, 102128. [Google Scholar] [CrossRef]
  10. Rugolo, F.; Bazan, N.G.; Calandria, J.; Jun, B.; Raschellà, G.; Melino, G.; Agostini, M. The expression of ELOVL4, repressed by MYCN, defines neuroblastoma patients with good outcome. Oncogene 2021, 40, 5741–5751. [Google Scholar] [CrossRef]
  11. Wang, Z.; Gerstein, M.; Snyder, M. RNA-Seq: A revolutionary tool for transcriptomics. Nat. Rev. Genet. 2009, 10, 57–63. [Google Scholar] [CrossRef] [PubMed]
  12. McDermaid, A.; Monier, B.; Zhao, J.; Liu, B.; Ma, Q. Interpretation of differential gene expression results of RNA-seq data: Review and integration. Brief. Bioinform. 2019, 20, 2044–2054. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Gallazzini, M.; Ferraris, J.D.; Burg, M.B. GDPD5 is a glycerophosphocholine phosphodiesterase that osmotically regulates the osmoprotective organic osmolyte GPC. Proc. Natl. Acad. Sci. USA 2008, 105, 11026–11031. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Sabharwal, P.; Lee, C.; Park, S.; Rao, M.; Sockanathan, S. GDE2 Regulates Subtype-Specific Motor Neuron Generation through Inhibition of Notch Signaling. Neuron 2011, 71, 1058–1070. [Google Scholar] [CrossRef] [Green Version]
  15. Yanaka, N.; Nogusa, Y.; Fujioka, Y.; Yamashita, Y.; Kato, N. Involvement of membrane protein GDE2 in retinoic acid-induced neurite formation in Neuro2A cells. FEBS Lett. 2007, 581, 712–718. [Google Scholar] [CrossRef] [Green Version]
  16. Choi, B.-R.; Cave, C.; Na, C.H.; Sockanathan, S. GDE2-Dependent Activation of Canonical Wnt Signaling in Neurons Regulates Oligodendrocyte Maturation. Cell Rep. 2020, 31, 107540. [Google Scholar] [CrossRef]
  17. Rao, M.; Sockanathan, S. Transmembrane Protein GDE2 Induces Motor Neuron Differentiation in Vivo. Science 2005, 309, 2212–2215. [Google Scholar] [CrossRef] [Green Version]
  18. Cave, C.; Park, S.; Rodriguez, M.; Nakamura, M.; Hoke, A.; Pletnikov, M.; Sockanathan, S. GDE2 is essential for neuronal survival in the postnatal mammalian spinal cord. Mol. Neurodegener. 2017, 12, 8. [Google Scholar] [CrossRef] [Green Version]
  19. Matas-Rico, E.; van Veen, M.; Leyton-Puig, D.; van den Berg, J.; Koster, J.; Kedziora, K.M.; Molenaar, B.; Weerts, M.J.A.; Rink, I.; Medema, R.H.; et al. Glycerophosphodiesterase GDE2 Promotes Neuroblastoma Differentiation through Glypican Release and Is a Marker of Clinical Outcome. Cancer Cell 2016, 30, 548–562. [Google Scholar] [CrossRef]
  20. Zheng, M.; Mullikin, H.; Hester, A.; Czogalla, B.; Heidegger, H.; Vilsmaier, T.; Vattai, A.; Chelariu-Raicu, A.; Jeschke, U.; Trillsch, F.; et al. Development and Validation of a Novel 11-Gene Prognostic Model for Serous Ovarian Carcinomas Based on Lipid Metabolism Expression Profile. Int. J. Mol. Sci. 2020, 21, 9169. [Google Scholar] [CrossRef]
  21. Asgharzadeh, S.; Salo, J.; Ji, L.; Oberthuer, A.; Fischer, M.; Berthold, F.; Hadjidaniel, M.; Liu, C.W.; Metelitsa, L.S.; Pique-Regi, R.; et al. Clinical significance of tumor-associated inflammatory cells in metastatic neuroblastoma. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2012, 30, 3525–3532. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Wienke, J.; Dierselhuis, M.P.; Tytgat, G.A.; Künkele, A.; Nierkens, S.; Molenaar, J.J. The immune landscape of neuroblastoma: Challenges and opportunities for novel therapeutic strategies in pediatric oncology. Eur. J. Cancer 2020, 144, 123–150. [Google Scholar] [CrossRef] [PubMed]
  23. Yenyuwadee, S.; Aliazis, K.; Wang, Q.; Christofides, A.; Shah, R.; Patsoukis, N.; Boussiotis, V.A. Immune cellular components and signaling pathways in the tumor microenvironment. Semin. Cancer Biol. 2022, 86, 187–201. [Google Scholar] [CrossRef] [PubMed]
  24. Fabian, M.R.; Sonenberg, N.; Filipowicz, W. Regulation of mRNA Translation and Stability by microRNAs. Annu. Rev. Biochem. 2010, 79, 351–379. [Google Scholar] [CrossRef] [Green Version]
  25. Campos Cogo, S.; Gradowski Farias da Costa do Nascimento, T.; de Almeida Brehm Pinhatti, F.; de França Junior, N.; Santos Rodrigues, B.; Cavalli, L.R.; Elifio-Esposito, S. An overview of neuroblastoma cell lineage phenotypes and in vitro models. Exp. Biol. Med. 2020, 245, 1637–1647. [Google Scholar] [CrossRef]
  26. Batchuluun, B.; Pinkosky, S.L.; Steinberg, G.R. Lipogenesis inhibitors: Therapeutic opportunities and challenges. Nat. Rev. Drug Discov. 2022, 21, 283–305. [Google Scholar] [CrossRef]
  27. Wang, X.; Song, H.; Liang, J.; Jia, Y.; Zhang, Y. Abnormal expression of HADH, an enzyme of fatty acid oxidation, affects tumor development and prognosis (Review). Mol. Med. Rep. 2022, 26, 355. [Google Scholar] [CrossRef]
  28. Park, S.; Baek, I.J.; Ryu, J.H.; Chun, C.H.; Jin, E.J. PPARalpha-ACOT12 axis is responsible for maintaining cartilage homeostasis through modulating de novo lipogenesis. Nat. Commun. 2022, 13, 3. [Google Scholar] [CrossRef]
  29. Bolanos, J.P.; Almeida, A.; Moncada, S. Glycolysis: A bioenergetic or a survival pathway? Trends Biochem. Sci. 2010, 35, 145–149. [Google Scholar] [CrossRef]
  30. Tarnopolsky, M.A. Myopathies Related to Glycogen Metabolism Disorders. Neurotherapeutics 2018, 15, 915–927. [Google Scholar] [CrossRef]
  31. Qin, J.; Chai, G.; Brewer, J.M.; Lovelace, L.L.; Lebioda, L. Structures of asymmetric complexes of human neuron specific enolase with resolved substrate and product and an analogous complex with two inhibitors indicate subunit interaction and inhibitor cooperativity. J. Inorg. Biochem. 2012, 111, 187–194. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Tan, V.P.; Miyamoto, S. HK2/hexokinase-II integrates glycolysis and autophagy to confer cellular protection. Autophagy 2015, 11, 963–964. [Google Scholar] [CrossRef] [Green Version]
  33. Pouysségur, J.; Marchiq Parks, S.; Durivault, J.; Ždralević, M.; Vucetic, M. ‘Warburg effect’ controls tumor growth, bacterial, viral infections and immunity—Genetic deconstruction and therapeutic perspectives. Semin. Cancer Biol. 2022, 86, 334–346. [Google Scholar] [CrossRef] [PubMed]
  34. Irwin, M.; Naranjo, A.; Zhang, F.; Cohn, S.; London, W.; Gastier-Foster, J.; Ramirez, N.C.; Pfau, R.; Reshmi, S.; Wagner, E.; et al. Revised Neuroblastoma Risk Classification System: A Report from the Children’s Oncology Group. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2021, 39, 3229–3241. [Google Scholar] [CrossRef]
  35. Pinto, N.; Applebaum, M.; Volchenboum, S.; Matthay, K.; London, W.; Ambros, P.; Nakagawara, A.; Berthold, F.; Schleiermacher, G.; Park, J.R.; et al. Advances in Risk Classification and Treatment Strategies for Neuroblastoma. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2015, 33, 3008–3017. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Shao, F.; Wang, Z.; Wang, S. MYCNIdentification of -Related Gene as a Potential Biomarker for Neuroblastoma Prognostic Model by Integrated Analysis and Quantitative Real-Time PCR. DNA Cell Biol. 2021, 40, 332–347. [Google Scholar] [CrossRef] [PubMed]
  37. Xia, Y.; Li, X.; Tian, X.; Zhao, Q. Identification of a Five-Gene Signature Derived from MYCN Amplification and Establishment of a Nomogram for Predicting the Prognosis of Neuroblastoma. Front. Mol. Biosci. 2021, 8, 1185. [Google Scholar] [CrossRef] [PubMed]
  38. Song, J.; Zhao, Q.; Xu, Y.; Zhu, L. A signature of 29 immune-related genes pairs to predict prognosis in patients with neuroblastoma. Int. Immunopharmacol. 2020, 88, 106994. [Google Scholar] [CrossRef] [PubMed]
  39. Sha, Y.; Han, L.; Sun, B.; Zhao, Q. Identification of a Glycosyltransferase Signature for Predicting Prognosis and Immune Microenvironment in Neuroblastoma. Front. Cell Dev. Biol. 2022, 9, 769580. [Google Scholar] [CrossRef] [PubMed]
  40. Cao, Y. Adipocyte and lipid metabolism in cancer drug resistance. J. Clin. Investig. 2019, 129, 3006–3017. [Google Scholar] [CrossRef]
  41. Broadfield, L.A.; Pane, A.A.; Talebi, A.; Swinnen, J.V.; Fendt, S.-M. Lipid metabolism in cancer: New perspectives and emerging mechanisms. Dev. Cell 2021, 56, 1363–1393. [Google Scholar] [CrossRef] [PubMed]
  42. Luo, X.; Cheng, C.; Tan, Z.; Li, N.; Tang, M.; Yang, L.; Cao, Y. Emerging roles of lipid metabolism in cancer metastasis. Mol. Cancer 2017, 16, 76. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Jiang, C.; Liu, Y.; Wen, S.; Xu, C.; Gu, L. In silico development and clinical validation of novel 8 gene signature based on lipid metabolism related genes in colon adenocarcinoma. Pharmacol. Res. 2021, 169, 105644. [Google Scholar] [CrossRef] [PubMed]
  44. Zhu, M.; Zeng, Q.; Fan, T.; Lei, Y.; Wang, F.; Zheng, S.; Wang, X.; Zeng, H.; Tan, F.; Sun, N.; et al. Clinical Significance and Immunometabolism Landscapes of a Novel Recurrence-Associated Lipid Metabolism Signature in Early-Stage Lung Adenocarcinoma: A Comprehensive Analysis. Front. Immunol. 2022, 13, 783495. [Google Scholar] [CrossRef] [PubMed]
  45. Attiyeh, E.F.; London, W.B.; Mossé, Y.P.; Wang, Q.; Winter, C.; Khazi, D.; McGrady, P.W.; Seeger, R.C.; Look, A.T.; Shimada, H.; et al. Chromosome 1p and 11q Deletions and Outcome in Neuroblastoma. N. Engl. J. Med. 2005, 353, 2243–2253. [Google Scholar] [CrossRef]
  46. Jiang, M.; Stanke, J.; Lahti, J.M. The Connections between Neural Crest Development and Neuroblastoma. Curr. Top. Dev. Biol. 2011, 94, 77–127. [Google Scholar] [CrossRef] [Green Version]
  47. Zhang, W.; Yu, Y.; Hertwig, F.; Thierry-Mieg, J.; Zhang, W.; Thierry-Mieg, D.; Wang, J.; Furlanello, C.; Devanarayan, V.; Cheng, J.; et al. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome Biol. 2015, 16, 133. [Google Scholar] [CrossRef] [Green Version]
  48. Ritchie, M.E.; Belinda, P.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015, 43, e47. [Google Scholar] [CrossRef]
  49. Subramanian, A.; Tamayo, P.; Mootha, V.K.; Mukherjee, S.; Ebert, B.L.; Gillette, M.A.; Paulovich, A.; Pomeroy, S.L.; Golub, T.R.; Lander, E.S.; et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 2005, 102, 15545–15550. [Google Scholar] [CrossRef] [Green Version]
  50. Lu, T.-P.; Lee, C.-Y.; Tsai, M.-H.; Chiu, Y.-C.; Hsiao, C.K.; Lai, L.-C.; Chuang, E.Y. miRSystem: An Integrated System for Characterizing Enriched Functions and Pathways of MicroRNA Targets. PLoS ONE 2012, 7, e42390. [Google Scholar] [CrossRef]
  51. Newman, A.M.; Liu, C.L.; Green, M.R.; Gentles, A.J.; Feng, W.; Xu, Y.; Hoang, C.D.; Diehn, M.; Alizadeh, A.A. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 2015, 12, 453–457. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Yoshihara, K.; Shahmoradgoli, M.; Martínez, E.; Vegesna, R.; Kim, H.; Torres-Garcia, W.; Trevino, V.; Shen, H.; Laird, P.W.; Levine, D.A.; et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 2013, 4, 2612. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Racle, J.; De Jonge, K.; Baumgaertner, P.; Speiser, D.E.; Gfeller, D. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. eLife 2017, 6, e26476. [Google Scholar] [CrossRef] [PubMed]
Ijms 23 13740 g001 550
Figure 1. Discovery of differentially expressed genes and aberrant metabolic pathways in high-risk neuroblastoma. (A) Volcano plots of differentially expressed genes (DEGs) in the GSE49710 dataset. The x-axis represents fold change in gene expression and the y-axis represents FDR. The red and green dots in the graph represent statistically significant up- and down-regulated genes. (B) Venn diagrams of DEG, lipid-metabolism-related genes and genes screened by univariate CCox. (C) Gene ontology (GO) analysis of 58 genes. (D) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of 58 genes. (E) Protein–protein interaction (PPI) network of 58 genes.
Figure 1. Discovery of differentially expressed genes and aberrant metabolic pathways in high-risk neuroblastoma. (A) Volcano plots of differentially expressed genes (DEGs) in the GSE49710 dataset. The x-axis represents fold change in gene expression and the y-axis represents FDR. The red and green dots in the graph represent statistically significant up- and down-regulated genes. (B) Venn diagrams of DEG, lipid-metabolism-related genes and genes screened by univariate CCox. (C) Gene ontology (GO) analysis of 58 genes. (D) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of 58 genes. (E) Protein–protein interaction (PPI) network of 58 genes.
Ijms 23 13740 g001
Ijms 23 13740 g002 550
Figure 2. Development of a prognostic model for lipid metabolism genes. (A) Lasso coefficient profiles of 58 prognosis−related lipid metabolism genes from the Discovery (GSE49710 microarray) dataset. (B) Partial likelihood bias of the variable revealed by the Lasso regression model. The red dots represent the partial likelihood of deviated values, the grey line represents the standard error (SE) and the left and right vertical dashed lines represent the minimum standard and the best value for the 1−SE standard, respectively. (C) Expression heatmap of six signature genes, risk score distribution and survival status of patients. (D) Kaplan−Meier analysis of 6−gene risk scores. (E) ROC curve analysis of 6−gene risk score.
Figure 2. Development of a prognostic model for lipid metabolism genes. (A) Lasso coefficient profiles of 58 prognosis−related lipid metabolism genes from the Discovery (GSE49710 microarray) dataset. (B) Partial likelihood bias of the variable revealed by the Lasso regression model. The red dots represent the partial likelihood of deviated values, the grey line represents the standard error (SE) and the left and right vertical dashed lines represent the minimum standard and the best value for the 1−SE standard, respectively. (C) Expression heatmap of six signature genes, risk score distribution and survival status of patients. (D) Kaplan−Meier analysis of 6−gene risk scores. (E) ROC curve analysis of 6−gene risk score.
Ijms 23 13740 g002
Ijms 23 13740 g003 550
Figure 3. Further optimization of wind direction score. (A) Multivariate Cox regression analysis of 6 genes. (B) Kaplan–Meier analysis of novel 3-gene prognostic risk scores. (C) ROC curve analysis of novel 3-gene prognostic risk scores. (D–F) Kaplan–Meier analysis of ACHE (D), GDPD5 (E) and PIK3R1 (F).
Figure 3. Further optimization of wind direction score. (A) Multivariate Cox regression analysis of 6 genes. (B) Kaplan–Meier analysis of novel 3-gene prognostic risk scores. (C) ROC curve analysis of novel 3-gene prognostic risk scores. (D–F) Kaplan–Meier analysis of ACHE (D), GDPD5 (E) and PIK3R1 (F).
Ijms 23 13740 g003
Ijms 23 13740 g004 550
Figure 4. Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis of GDPD5. (A) Two KEGG pathways enriched in samples with high GDPD5 expression. (B) Two KEGG oncology signatures enriched in samples with high GDPD5 expression. (C–E) The relationship between the expression of GDPD5 and stromal score (C), immune score (D) and estimate score (E). The horizontal axis represents the expression of GDPD5 and the vertical axis represents the score.
Figure 4. Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis of GDPD5. (A) Two KEGG pathways enriched in samples with high GDPD5 expression. (B) Two KEGG oncology signatures enriched in samples with high GDPD5 expression. (C–E) The relationship between the expression of GDPD5 and stromal score (C), immune score (D) and estimate score (E). The horizontal axis represents the expression of GDPD5 and the vertical axis represents the score.
Ijms 23 13740 g004
Ijms 23 13740 g005 550
Figure 5. Identification of GDPD5-targeted miRNAs. (A) Venn diagram of differentially expressed miRNAs and GDPD5-targeted miRNAs. (B,C) Kaplan–Meier analysis of hsa-miR-592 (B) and hsa-miR-604 (C) in the GSE121513 dataset.
Figure 5. Identification of GDPD5-targeted miRNAs. (A) Venn diagram of differentially expressed miRNAs and GDPD5-targeted miRNAs. (B,C) Kaplan–Meier analysis of hsa-miR-592 (B) and hsa-miR-604 (C) in the GSE121513 dataset.
Ijms 23 13740 g005
Ijms 23 13740 g006 550
Figure 6. Immunoblot analysis. (A) Immunoblot analysis of transfected flag-GDPD5 or transfected flag-ctrl. (B) Western blot analysis of lipid-metabolism-related markers. (C) Western blot analysis of markers related to glucose metabolism. ACC (Acetyl-CoA Carboxylase Alpha), ACLY (ATP Citrate Lyase), HADH (Hydroxyacyl-CoA Dehydrogenase), Fructose-2,6-Biphosphatase 3 (PFKFB3), PPARA (Peroxisome Proliferator Activated Receptor Alpha), ALDOA (Aldolase, Fructose-Bisphosphate A), ENO2 (Enolase 2), HK2 (Hexokinase 2), LDHA (Lactate Dehydrogenase A).
Figure 6. Immunoblot analysis. (A) Immunoblot analysis of transfected flag-GDPD5 or transfected flag-ctrl. (B) Western blot analysis of lipid-metabolism-related markers. (C) Western blot analysis of markers related to glucose metabolism. ACC (Acetyl-CoA Carboxylase Alpha), ACLY (ATP Citrate Lyase), HADH (Hydroxyacyl-CoA Dehydrogenase), Fructose-2,6-Biphosphatase 3 (PFKFB3), PPARA (Peroxisome Proliferator Activated Receptor Alpha), ALDOA (Aldolase, Fructose-Bisphosphate A), ENO2 (Enolase 2), HK2 (Hexokinase 2), LDHA (Lactate Dehydrogenase A).
Ijms 23 13740 g006
Ijms 23 13740 g007 550
Figure 7. GDPD5 affects the migration and proliferation of human neuroblastoma cell line SH-SY5Y. (A,B) GDPD5 effects inhibit the migration (A,B) proliferation of SH-SY5Y. *: p < 0.05, ***: p < 0.001.
Figure 7. GDPD5 affects the migration and proliferation of human neuroblastoma cell line SH-SY5Y. (A,B) GDPD5 effects inhibit the migration (A,B) proliferation of SH-SY5Y. *: p < 0.05, ***: p < 0.001.
Ijms 23 13740 g007
Table
Table 1. Clinical pathological parameters of NB patients in the training set.
Table 1. Clinical pathological parameters of NB patients in the training set.
FeaturesNumber (%)
Sex
  Female211 (42.4%)
  Male287 (57.6%)
Age
  ≤18 months300 (60.2%)
  >18 months198 (39.8%)
MYCN amplification
  No401 (80.5%)
  Unknown5 (1.0%)
  Yes92 (18.5%)
INSS stage
  St1121 (24.3%)
  St278 (15.7%)
  St363 (12.7%)
  St4183 (36.7%)
  St4S53 (10.6%)
Clinical risk
  High176 (35.3%)
  Low322 (64.7%)
Class label
  Favorable181 (36.3%)
  Unfavorable91 (18.3%)
  Unknown226 (45.4%)
Progression
  No315 (63.3%)
  Yes183 (36.7%)
Death from disease
  No393 (78.9%)
  Yes105 (21.1%)
Table
Table 2. Accuracy evaluation of risk score formula grouping (optimal cutoff).
Table 2. Accuracy evaluation of risk score formula grouping (optimal cutoff).
Low Risk ScoreHigh Risk ScoreAccuracy
COG (High)2615085.23%
COG (Low and medium)3012193.48%
Total327171498 (100%)
Table
Table 3. Risk score formula grouping accuracy assessment (median).
Table 3. Risk score formula grouping accuracy assessment (median).
Low Risk ScoreHigh Risk ScoreAccuracy
COG (High)816895.45%
COG (Low and medium)2418174.84%
Total327171498 (100%)
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Luo, T.; Peng, J.; Li, Q.; Zhang, Y.; Huang, Y.; Xu, L.; Yang, G.; Tan, D.; Zhang, Q.; Tan, Y. GDPD5 Related to Lipid Metabolism Is a Potential Prognostic Biomarker in Neuroblastoma. Int. J. Mol. Sci. 2022, 23, 13740. https://doi.org/10.3390/ijms232213740

AMA Style

Luo T, Peng J, Li Q, Zhang Y, Huang Y, Xu L, Yang G, Tan D, Zhang Q, Tan Y. GDPD5 Related to Lipid Metabolism Is a Potential Prognostic Biomarker in Neuroblastoma. International Journal of Molecular Sciences. 2022; 23(22):13740. https://doi.org/10.3390/ijms232213740

Chicago/Turabian Style

Luo, Tengling, Junwei Peng, Qijun Li, Yao Zhang, Yun Huang, Lei Xu, Genling Yang, Dongmei Tan, Qian Zhang, and Yi Tan. 2022. "GDPD5 Related to Lipid Metabolism Is a Potential Prognostic Biomarker in Neuroblastoma" International Journal of Molecular Sciences 23, no. 22: 13740. https://doi.org/10.3390/ijms232213740

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop