Skip to main content
Log in

Analysis and identification of novel biomarkers involved in neuroblastoma via integrated bioinformatics

  • PRECLINICAL STUDIES
  • Published:
Investigational New Drugs Aims and scope Submit manuscript

Summary

Neuroblastoma (NB) is the most common extracranial solid tumor in children. Under various treatments, some patients still have a poor prognosis. Hence, it is necessary to find new valid targets for NB therapy. In this study, a comprehensive bioinformatic analysis was used to identify differentially expressed genes (DEGs) between NB and control cells, and to select hub genes associated with NB. GSE66586 and GSE78061 datasets were downloaded from the Gene Expression Omnibus (GEO) database and DEGs were selected. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied to the selected DEGs. The STRING database and Cytoscape software were used to construct protein-protein interaction (PPI) networks and perform modular analysis of the DEGs. The R2 database was used for prognostic analysis. We identified a total of 238 DEGs from two microarray databases. GO enrichment analysis shows that these DEGs are mainly concentrated in the regulation of cell growth, cell migration, cell fate determination, and cell maturation. KEGG pathway analysis showed that these DEGs are mainly involved in focal adhesion, the TNF signaling pathway, cancer-related pathways, and signaling pathways regulating stem cell pluripotency. We identified the 15 most closely related DEGs from the PPI network, and performed R2 database prognostic analysis to select five hub genes – CTGF, EDN1, GATA2, LOX, and SERPINE1. This study distinguished hub genes and related signaling pathways that can potentially serve as diagnostic indicators and therapeutic biomarkers for NB, thereby improving understanding of the molecular mechanisms involved in NB.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

All data is available under reasonable request.

References

  1. Maris JM, Hogarty MD, Bagatell R, Cohn SL (2007) Neuroblastoma. Lancet 369(9579):2106–2120. https://doi.org/10.1016/S0140-6736(07)60983-0

    Article  CAS  PubMed  Google Scholar 

  2. Fonseka P, Liem M, Ozcitti C, Adda CG, Ang CS, Mathivanan S (2019) Exosomes from N-Myc amplified neuroblastoma cells induce migration and confer chemoresistance to non-N-Myc amplified cells: implications of intra-tumour heterogeneity. J Extracell Vesicles 8(1):1597614. https://doi.org/10.1080/20013078.2019.1597614

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Brodeur GM (2003) Neuroblastoma: biological insights into a clinical enigma. Nat Rev Cancer 3(3):203–216. https://doi.org/10.1038/nrc1014

    Article  CAS  PubMed  Google Scholar 

  4. Scheer M, Bork K, Simon F, Nagasundaram M, Horstkorte R, Gnanapragassam VS (2020) Glycation leads to increased Polysialylation and promotes the metastatic potential of Neuroblastoma cells. Cells 9(4). https://doi.org/10.3390/cells9040868

  5. Depuydt P, Boeva V, Hocking TD, Cannoodt R, Ambros IM, Ambros PF, Asgharzadeh S, Attiyeh EF, Combaret V, Defferrari R, Fischer M, Hero B, Hogarty MD, Irwin MS, Koster J, Kreissman S, Ladenstein R, Lapouble E, Laureys G, London WB, Mazzocco K, Nakagawara A, Noguera R, Ohira M, Park JR, Potschger U, Theissen J, Tonini GP, Valteau-Couanet D, Varesio L, Versteeg R, Speleman F, Maris JM, Schleiermacher G, De Preter K (2018) Genomic amplifications and distal 6q loss: novel markers for poor survival in high-risk Neuroblastoma patients. J Natl Cancer Inst 110(10):1084–1093. https://doi.org/10.1093/jnci/djy022

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Koneru B, Lopez G, Farooqi A, Conkrite KL, Nguyen TH, Macha SJ, Modi A, Rokita JL, Urias E, Hindle A, Davidson H, McCoy K, Nance J, Yazdani V, Irwin MS, Yang S, Wheeler DA, Maris JM, Diskin SJ, Reynolds CP (2020) Telomere maintenance mechanisms define clinical outcome in high-risk neuroblastoma. Cancer Res 80:2663–2675. https://doi.org/10.1158/0008-5472.CAN-19-3068

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Pinto NR, Applebaum MA, Volchenboum SL, Matthay KK, London WB, Ambros PF, Nakagawara A, Berthold F, Schleiermacher G, Park JR, Valteau-Couanet D, Pearson AD, Cohn SL (2015) Advances in risk classification and treatment strategies for Neuroblastoma. J Clin Oncol 33(27):3008–3017. https://doi.org/10.1200/JCO.2014.59.4648

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Upton K, Modi A, Patel K, Kendsersky NM, Conkrite KL, Sussman RT, Way GP, Adams RN, Sacks GI, Fortina P, Diskin SJ, Maris JM, Rokita JL (2020) Epigenomic profiling of neuroblastoma cell lines. Sci Data 7(1):116. https://doi.org/10.1038/s41597-020-0458-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Almstedt E, Elgendy R, Hekmati N, Rosen E, Warn C, Olsen TK, Dyberg C, Doroszko M, Larsson I, Sundstrom A, Arsenian Henriksson M, Pahlman S, Bexell D, Vanlandewijck M, Kogner P, Jornsten R, Krona C, Nelander S (2020) Integrative discovery of treatments for high-risk neuroblastoma. Nat Commun 11(1):71. https://doi.org/10.1038/s41467-019-13817-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Berthold F, Faldum A, Ernst A, Boos J, Dilloo D, Eggert A, Fischer M, Fruhwald M, Henze G, Klingebiel T, Kratz C, Kremens B, Krug B, Leuschner I, Schmidt M, Schmidt R, Schumacher-Kuckelkorn R, von Schweinitz D, Schilling FH, Theissen J, Volland R, Hero B, Simon T (2020) Extended induction chemotherapy does not improve the outcome for high-risk neuroblastoma patients: results of the randomized open-label GPOH trial NB2004-HR. Ann Oncol 31(3):422–429. https://doi.org/10.1016/j.annonc.2019.11.011

    Article  CAS  PubMed  Google Scholar 

  11. Pstrag N, Ziemnicka K, Bluyssen H, Wesoly J (2018) Thyroid cancers of follicular origin in a genomic light: in-depth overview of common and unique molecular marker candidates. Mol Cancer 17(1):116. https://doi.org/10.1186/s12943-018-0866-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Gao Y, Huo W, Zhang L, Lian J, Tao W, Song C, Tang J, Shi S, Gao Y (2019) Multiplex measurement of twelve tumor markers using a GMR multi-biomarker immunoassay biosensor. Biosens Bioelectron 123:204–210. https://doi.org/10.1016/j.bios.2018.08.060

    Article  CAS  PubMed  Google Scholar 

  13. Coyle R, Jia J, Mei Y (2016) Polymer microarray technology for stem cell engineering. Acta Biomater 34:60–72. https://doi.org/10.1016/j.actbio.2015.10.030

    Article  CAS  PubMed  Google Scholar 

  14. Toro-Dominguez D, Martorell-Marugan J, Lopez-Dominguez R, Garcia-Moreno A, Gonzalez-Rumayor V, Alarcon-Riquelme ME, Carmona-Saez P (2019) ImaGEO: integrative gene expression meta-analysis from GEO database. Bioinformatics 35(5):880–882. https://doi.org/10.1093/bioinformatics/bty721

    Article  CAS  PubMed  Google Scholar 

  15. Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Edgar R (2007) NCBI GEO: mining tens of millions of expression profiles--database and tools update. Nucleic Acids Res 35(Database issue):D760–D765. https://doi.org/10.1093/nar/gkl887

    Article  CAS  PubMed  Google Scholar 

  16. Hart LS, Rader J, Raman P, Batra V, Russell MR, Tsang M, Gagliardi M, Chen L, Martinez D, Li Y, Wood A, Kim S, Parasuraman S, Delach S, Cole KA, Krupa S, Boehm M, Peters M, Caponigro G, Maris JM (2017) Preclinical therapeutic synergy of MEK1/2 and CDK4/6 inhibition in Neuroblastoma. Clin Cancer Res 23(7):1785–1796. https://doi.org/10.1158/1078-0432.CCR-16-1131

    Article  CAS  PubMed  Google Scholar 

  17. Gu L, Chu P, Lingeman R, McDaniel H, Kechichian S, Hickey RJ, Liu Z, Yuan YC, Sandoval JA, Fields GB, Malkas LH (2015) The mechanism by which MYCN amplification confers an enhanced sensitivity to a PCNA-derived cell permeable peptide in Neuroblastoma cells. EBioMedicine 2(12):1923–1931. https://doi.org/10.1016/j.ebiom.2015.11.016

    Article  PubMed  PubMed Central  Google Scholar 

  18. Clough E, Barrett T (2016) The gene expression omnibus database. Methods Mol Biol 1418:93–110. https://doi.org/10.1007/978-1-4939-3578-9_5

    Article  PubMed  PubMed Central  Google Scholar 

  19. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4(2):249–264. https://doi.org/10.1093/biostatistics/4.2.249

    Article  PubMed  Google Scholar 

  20. Gautier L, Cope L, Bolstad BM, Irizarry RA (2004) Affy--analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20(3):307–315. https://doi.org/10.1093/bioinformatics/btg405

    Article  CAS  PubMed  Google Scholar 

  21. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43(7):e47. https://doi.org/10.1093/nar/gkv007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Diboun I, Wernisch L, Orengo CA, Koltzenburg M (2006) Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genomics 7:252. https://doi.org/10.1186/1471-2164-7-252

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Nie K, Shi L, Wen Y, Pan J, Li P, Zheng Z, Liu F (2019) Identification of hub genes correlated with the pathogenesis and prognosis of gastric cancer via bioinformatics methods. Minerva Med. https://doi.org/10.23736/S0026-4806.19.06166-4

  24. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet 25(1):25–29. https://doi.org/10.1038/75556

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30. https://doi.org/10.1093/nar/28.1.27

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M (1999) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 27(1):29–34. https://doi.org/10.1093/nar/27.1.29

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Sherman BT, da Huang W, Tan Q, Guo Y, Bour S, Liu D, Stephens R, Baseler MW, Lane HC, Lempicki RA (2007) DAVID knowledgebase: a gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis. BMC Bioinformatics 8:426. https://doi.org/10.1186/1471-2105-8-426

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Doncheva NT, Morris JH, Gorodkin J, Jensen LJ (2019) Cytoscape StringApp: network analysis and visualization of proteomics data. J Proteome Res 18(2):623–632. https://doi.org/10.1021/acs.jproteome.8b00702

    Article  CAS  PubMed  Google Scholar 

  29. Park JA, Cheung NV (2020) Targets and antibody formats for immunotherapy of Neuroblastoma. J Clin Oncol:JCO1901410. https://doi.org/10.1200/JCO.19.01410

  30. Xia XQ, Jia Z, Porwollik S, Long F, Hoemme C, Ye K, Muller-Tidow C, McClelland M, Wang Y (2010) Evaluating oligonucleotide properties for DNA microarray probe design. Nucleic Acids Res 38(11):e121. https://doi.org/10.1093/nar/gkq039

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Pounds S, Morris SW (2003) Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values. Bioinformatics 19(10):1236–1242. https://doi.org/10.1093/bioinformatics/btg148

    Article  CAS  PubMed  Google Scholar 

  32. Kupfer P, Guthke R, Pohlers D, Huber R, Koczan D, Kinne RW (2012) Batch correction of microarray data substantially improves the identification of genes differentially expressed in rheumatoid arthritis and osteoarthritis. BMC Med Genet 5:23. https://doi.org/10.1186/1755-8794-5-23

    Article  CAS  Google Scholar 

  33. Karstens KF, Bellon E, Polonski A, Wolters-Eisfeld G, Melling N, Reeh M, Izbicki JR, Tachezy M (2020) Expression and serum levels of the neural cell adhesion molecule L1-like protein (CHL1) in gastrointestinal stroma tumors (GIST) and its prognostic power. Oncotarget 11(13):1131–1140. https://doi.org/10.18632/oncotarget.27525

    Article  PubMed  PubMed Central  Google Scholar 

  34. Beltran-Anaya FO, Romero-Cordoba S, Rebollar-Vega R, Arrieta O, Bautista-Pina V, Dominguez-Reyes C, Villegas-Carlos F, Tenorio-Torres A, Alfaro-Riuz L, Jimenez-Morales S, Cedro-Tanda A, Rios-Romero M, Reyes-Grajeda JP, Tagliabue E, Iorio MV, Hidalgo-Miranda A (2019) Expression of long non-coding RNA ENSG00000226738 (LncKLHDC7B) is enriched in the immunomodulatory triple-negative breast cancer subtype and its alteration promotes cell migration, invasion, and resistance to cell death. Mol Oncol 13(4):909–927. https://doi.org/10.1002/1878-0261.12446

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Villalobo A, Berchtold MW (2020) The role of Calmodulin in tumor cell migration, invasiveness, and metastasis. Int J Mol Sci 21(3). https://doi.org/10.3390/ijms21030765

  36. Haug BH, Hald OH, Utnes P, Roth SA, Lokke C, Flaegstad T, Einvik C (2015) Exosome-like extracellular vesicles from MYCN-amplified Neuroblastoma cells contain oncogenic miRNAs. Anticancer Res 35(5):2521–2530

    CAS  PubMed  Google Scholar 

  37. Ma W, Chen X, Wu X, Li J, Mei C, Jing W, Teng L, Tu H, Jiang X, Wang G, Chen Y, Wang K, Wang H, Wei Y, Liu Z, Yuan Y (2020) Long noncoding RNA SPRY4-IT1 promotes proliferation and metastasis of hepatocellular carcinoma via mediating TNF signaling pathway. J Cell Physiol. https://doi.org/10.1002/jcp.29438

  38. Li M, Ren CX, Zhang JM, Xin XY, Hua T, Wang HB, Wang HB (2018) The effects of miR-195-5p/MMP14 on proliferation and invasion of cervical carcinoma cells through TNF signaling pathway based on bioinformatics analysis of microarray profiling. Cell Physiol Biochem 50(4):1398–1413. https://doi.org/10.1159/000494602

    Article  CAS  PubMed  Google Scholar 

  39. Song Y, Kim JS, Choi EK, Kim J, Kim KM, Seo HR (2017) TGF-beta-independent CTGF induction regulates cell adhesion mediated drug resistance by increasing collagen I in HCC. Oncotarget 8(13):21650–21662. https://doi.org/10.18632/oncotarget.15521

    Article  PubMed  PubMed Central  Google Scholar 

  40. Waddell JM, Evans J, Jabbour HN, Denison FC (2011) CTGF expression is up-regulated by PROK1 in early pregnancy and influences HTR-8/Svneo cell adhesion and network formation. Hum Reprod 26(1):67–75. https://doi.org/10.1093/humrep/deq294

    Article  CAS  PubMed  Google Scholar 

  41. Ball DK, Rachfal AW, Kemper SA, Brigstock DR (2003) The heparin-binding 10 kDa fragment of connective tissue growth factor (CTGF) containing module 4 alone stimulates cell adhesion. J Endocrinol 176(2):R1–R7. https://doi.org/10.1677/joe.0.176r001

    Article  CAS  PubMed  Google Scholar 

  42. Song ZM, Liu F, Chen YM, Liu YJ, Wang XD, Du SY (2019) CTGF-mediated ERK signaling pathway influences the inflammatory factors and intestinal flora in ulcerative colitis. Biomed Pharmacother 111:1429–1437. https://doi.org/10.1016/j.biopha.2018.12.063

    Article  CAS  PubMed  Google Scholar 

  43. Wang M, Liu Y, Zou J, Yang R, Xuan F, Wang Y, Gao N, Cui H (2015) Transcriptional co-activator TAZ sustains proliferation and tumorigenicity of neuroblastoma by targeting CTGF and PDGF-beta. Oncotarget 6(11):9517–9530. https://doi.org/10.18632/oncotarget.3367

    Article  PubMed  PubMed Central  Google Scholar 

  44. Yuan W, Qian M, Li ZX, Zhao CL, Zhao J, Xiao JR (2019) Endothelin-1 activates the notch signaling pathway and promotes tumorigenesis in Giant cell tumor of the spine. Spine (Phila Pa 1976) 44(17):E1000–E1009. https://doi.org/10.1097/BRS.0000000000003044

    Article  Google Scholar 

  45. Basurto L, Sanchez L, Diaz A, Valle M, Robledo A, Martinez-Murillo C (2019) Differences between metabolically healthy and unhealthy obesity in PAI-1 level: fibrinolysis, body size phenotypes and metabolism. Thromb Res 180:110–114. https://doi.org/10.1016/j.thromres.2019.06.013

    Article  CAS  PubMed  Google Scholar 

  46. Tang W, Dong K, Li K, Dong R, Zheng S (2016) MEG3, HCN3 and linc01105 influence the proliferation and apoptosis of neuroblastoma cells via the HIF-1alpha and p53 pathways. Sci Rep 6:36268. https://doi.org/10.1038/srep36268

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Chen SJ, Hoffman NE, Shanmughapriya S, Bao L, Keefer K, Conrad K, Merali S, Takahashi Y, Abraham T, Hirschler-Laszkiewicz I, Wang J, Zhang XQ, Song J, Barrero C, Shi Y, Kawasawa YI, Bayerl M, Sun T, Barbour M, Wang HG, Madesh M, Cheung JY, Miller BA (2014) A splice variant of the human ion channel TRPM2 modulates neuroblastoma tumor growth through hypoxia-inducible factor (HIF)-1/2alpha. J Biol Chem 289(52):36284–36302. https://doi.org/10.1074/jbc.M114.620922

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Wang Q, Xu Z, An Q, Jiang D, Wang L, Liang B, Li Z (2015) TAZ promotes epithelial to mesenchymal transition via the upregulation of connective tissue growth factor expression in neuroblastoma cells. Mol Med Rep 11(2):982–988. https://doi.org/10.3892/mmr.2014.2818

    Article  CAS  PubMed  Google Scholar 

  49. Mendes-de-Almeida DP, Andrade FG, Borges G, Dos Santos-Bueno FV, Vieira IF, da Rocha L, Mendes-da-Cruz DA, Zancope-Oliveira RM, Calado RT, Pombo-de-Oliveira MS (2019) GATA2 mutation in long stand Mycobacterium kansasii infection, myelodysplasia and MonoMAC syndrome: a case-report. BMC Med Genet 20(1):64. https://doi.org/10.1186/s12881-019-0799-6

    Article  PubMed  PubMed Central  Google Scholar 

  50. Hoene V, Fischer M, Ivanova A, Wallach T, Berthold F, Dame C (2009) GATA factors in human neuroblastoma: distinctive expression patterns in clinical subtypes. Br J Cancer 101(8):1481–1489. https://doi.org/10.1038/sj.bjc.6605276

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Wei JS, Johansson P, Chen L, Song YK, Tolman C, Li S, Hurd L, Patidar R, Wen X, Badgett TC, Cheuk AT, Marshall JC, Steeg PS, Vaque Diez JP, Yu Y, Gutkind JS, Khan J (2013) Massively parallel sequencing reveals an accumulation of de novo mutations and an activating mutation of LPAR1 in a patient with metastatic neuroblastoma. PLoS One 8(10):e77731. https://doi.org/10.1371/journal.pone.0077731

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Li Q, Zhu CC, Ni B, Zhang ZZ, Jiang SH, Hu LP, Wang X, Zhang XX, Huang PQ, Yang Q, Li J, Gu JR, Xu J, Luo KQ, Zhao G, Zhang ZG (2019) Lysyl oxidase promotes liver metastasis of gastric cancer via facilitating the reciprocal interactions between tumor cells and cancer associated fibroblasts. EBioMedicine 49:157–171. https://doi.org/10.1016/j.ebiom.2019.10.037

    Article  PubMed  PubMed Central  Google Scholar 

  53. Redova M, Chlapek P, Loja T, Zitterbart K, Hermanova M, Sterba J, Veselska R (2010) Influence of LOX/COX inhibitors on cell differentiation induced by all-trans retinoic acid in neuroblastoma cell lines. Int J Mol Med 25(2):271–280

    CAS  PubMed  Google Scholar 

  54. Chlapek P, Redova M, Zitterbart K, Hermanova M, Sterba J, Veselska R (2010) Enhancement of ATRA-induced differentiation of neuroblastoma cells with LOX/COX inhibitors: an expression profiling study. J Exp Clin Cancer Res 29:45. https://doi.org/10.1186/1756-9966-29-45

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the staff from Medical Research Center of Shengjing Hospital who gave us support throughout the experiments.

Funding

The work was supported by the National Natural Science Foundation of China (No. 81972515, 81,472,359), Key Research and Development Foundation of Liaoning Province (2019JH8/10300024), 2013 Liaoning Climbing Scholar Foundation, and 345 Talent Project of Shengjing Hospital of China Medical University.

Author information

Authors and Affiliations

Authors

Contributions

Bo Chen and Peng Ding had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Both contributed equally to the study and are co-first authors; Zhongyan Hua and Xiuni Qin contributed to collection of data; Zhijie Li contributed to the study design, interpretation of the data, the writing of the manuscript, and the submission of the manuscript for publication.

Corresponding author

Correspondence to Zhijie Li.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

For this type of study, formal consent is not required.

Consent to participate

Not applicable.

Consent for publication

All authors consent to the publication of this study.

Code availability

Not applicable.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, B., Ding, P., Hua, Z. et al. Analysis and identification of novel biomarkers involved in neuroblastoma via integrated bioinformatics. Invest New Drugs 39, 52–65 (2021). https://doi.org/10.1007/s10637-020-00980-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10637-020-00980-9

Keywords

Navigation