Skip to main content
SpringerLink
Log in

Identify potential miRNA-mRNA regulatory networks contributing to high-risk neuroblastoma

  • CLINICAL TRIAL METHODOLOGY
  • Published:
Investigational New Drugs Aims and scope Submit manuscript

Summary

Neuroblastoma (NB) is a common tumor in children, usually in the retroperitoneum. After various treatments, low- and intermediate-risk patients have achieved good results, but the prognosis of high-risk patients is still very poor. Therefore, it is necessary to find new effective targets for the treatment of high-risk patients. In this study, comprehensive bioinformatics analysis was used to identify the differentially expressed genes (DEG and DEM) between high-risk patients and non-high-risk patients, and it was identified that ADRB2 may affect the survival status of high-risk patients due to miR -30a-5p regulation. The GSE49710, GSE73517, and GSE121513 datasets were downloaded from the Gene Expression Synthesis (GEO) database, and DEG and DEM 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 TARGET data set containing information on overall survival days were used for the prognostic analysis of central genes. We identified a total of 255 DEGs from GSE49710 and GSE73517, and 193 DEMs from GSE121513. We identified the 5 most important central genes from the PPI network, performed a prognostic analysis in the target data set, and verified their expression using RT-qPCR to select the most important ADRB2 gene to predict miRNA. Integrating the differential miRNA predicted by miRDB and miRSystem and GSE121513 between the targeted miRNA and the prognosis, miR-30a-5p was finally identified as the targeted miRNA of ADRB2.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Data availability

The datasets analyzed during the current study are available in the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) and Therapeutically Applicable Research To Generate Effective Treatments (https://ocg.cancer.gov/programs/target) repositories.

References

  1. Kholodenko IV, Kalinovsky DV, Doronin II, Deyev SM, Kholodenko RV (2018) Neuroblastoma origin and therapeutic targets for immunotherapy. J Immunol Res 2018:7394268. https://doi.org/10.1155/2018/7394268

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. 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 

  3. 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-tumor heterogeneity. J Extracell Vesicles 8(1):1597614. https://doi.org/10.1080/20013078.2019.1597614

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 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(12):2663–2675. https://doi.org/10.1158/0008-5472.CAN-19-3068

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Strother DR, London WB, Schmidt ML, Brodeur GM, Shimada H, Thorner P, Collins MH, Tagge E, Adkins S, Reynolds CP, Murray K, Lavey RS, Matthay KK, Castleberry R, Maris JM, Cohn SL (2012) Outcome after surgery alone or with restricted use of chemotherapy for patients with low-risk neuroblastoma: results of Children's Oncology group study P9641. J Clin Oncol 30(15):1842–1848. https://doi.org/10.1200/JCO.2011.37.9990

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Baker DL, Schmidt ML, Cohn SL, Maris JM, London WB, Buxton A, Stram D, Castleberry RP, Shimada H, Sandler A, Shamberger RC, Look AT, Reynolds CP, Seeger RC, Matthay KK, Children's Oncology G (2010) Outcome after reduced chemotherapy for intermediate-risk neuroblastoma. N Engl J Med 363(14):1313–1323. https://doi.org/10.1056/NEJMoa1001527

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Pearson AD, Pinkerton CR, Lewis IJ, Imeson J, Ellershaw C, Machin D, European Neuroblastoma Study G, Children's C, Leukaemia G (2008) High-dose rapid and standard induction chemotherapy for patients aged over 1 year with stage 4 neuroblastoma: a randomized trial. Lancet Oncol 9(3):247–256. https://doi.org/10.1016/S1470-2045(08)70069-X

    Article  CAS  PubMed  Google Scholar 

  8. Matthay KK, Reynolds CP, Seeger RC, Shimada H, Adkins ES, Haas-Kogan D, Gerbing RB, London WB, Villablanca JG (2009) Long-term results for children with high-risk neuroblastoma treated on a randomized trial of myeloablative therapy followed by 13-cis-retinoic acid: a children's oncology group study. J Clin Oncol 27(7):1007–1013. https://doi.org/10.1200/JCO.2007.13.8925

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Holmes K, Potschger U, ADJ P, Sarnacki S, Cecchetto G, Gomez-Chacon J, Squire R, Freud E, Bysiek A, Matthyssens LE, Metzelder M, Monclair T, Stenman J, Rygl M, Rasmussen L, Joseph JM, Irtan S, Avanzini S, Godzinski J, Bjornland K, Elliott M, Luksch R, Castel V, Ash S, Balwierz W, Laureys G, Ruud E, Papadakis V, Malis J, Owens C, Schroeder H, Beck-Popovic M, Trahair T, Forjaz de Lacerda A, Ambros PF, Gaze MN, McHugh K, Valteau-Couanet D, Ladenstein RL, International Society of Paediatric Oncology Europe Neuroblastoma G (2020) Influence of surgical excision on the survival of patients with stage 4 high-risk neuroblastoma: a report from the HR-NBL1/SIOPEN study. J Clin Oncol 38(25):2902–2915. https://doi.org/10.1200/JCO.19.03117

    Article  PubMed  Google Scholar 

  10. 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 

  11. Matthay KK, Maris JM, Schleiermacher G, Nakagawara A, Mackall CL, Diller L, Weiss WA (2016) Neuroblastoma. Nat Rev Dis Primers 2:16078. https://doi.org/10.1038/nrdp.2016.78

    Article  PubMed  Google Scholar 

  12. Park JR, Bagatell R, London WB, Maris JM, Cohn SL, Mattay KK, Hogarty M, Committee COGN (2013) Children's Oncology Group's 2013 blueprint for research: neuroblastoma. Pediatr Blood Cancer 60(6):985–993. https://doi.org/10.1002/pbc.24433

    Article  PubMed  Google Scholar 

  13. 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 

  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. Chen B, Ding P, Hua Z, Qin X, Li Z (2020) Analysis and identification of novel biomarkers involved in neuroblastoma via integrated bioinformatics. Investig New Drugs. https://doi.org/10.1007/s10637-020-00980-9

  17. Wang H, Wang X, Xu L, Zhang J, Cao H (2020) Prognostic significance of MYCN related genes in pediatric neuroblastoma: a study based on TARGET and GEO datasets. BMC Pediatr 20(1):314. https://doi.org/10.1186/s12887-020-02219-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhong X, Liu Y, Liu H, Zhang Y, Wang L, Zhang H (2018) Identification of potential prognostic genes for neuroblastoma. Front Genet 9:589. https://doi.org/10.3389/fgene.2018.00589

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 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 

  20. Consortium SM-I (2014) A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the sequencing quality control consortium. Nat Biotechnol 32(9):903–914. https://doi.org/10.1038/nbt.2957

    Article  CAS  Google Scholar 

  21. Henrich KO, Bender S, Saadati M, Dreidax D, Gartlgruber M, Shao C, Herrmann C, Wiesenfarth M, Parzonka M, Wehrmann L, Fischer M, Duffy DJ, Bell E, Torkov A, Schmezer P, Plass C, Hofer T, Benner A, Pfister SM, Westermann F (2016) Integrative genome-scale analysis identifies epigenetic mechanisms of transcriptional deregulation in unfavorable neuroblastomas. Cancer Res 76(18):5523–5537. https://doi.org/10.1158/0008-5472.CAN-15-2507

    Article  CAS  PubMed  Google Scholar 

  22. 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 

  23. 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 

  24. 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 

  25. 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 

  26. 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  Google Scholar 

  27. 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 

  28. 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 

  29. Sherman BT, Huang d 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 

  30. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ, von Mering C (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43(Database issue):D447–D452. https://doi.org/10.1093/nar/gku1003

    Article  CAS  PubMed  Google Scholar 

  31. 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 

  32. Park JA, Cheung NV (2020) Targets and antibody formats for immunotherapy of neuroblastoma. J Clin Oncol 38(16):1836–1848. https://doi.org/10.1200/JCO.19.01410

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 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 

  34. 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 

  35. 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 

  36. 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 

  37. Svensmark JH, Brakebusch C (2019) Rho GTPases in cancer: friend or foe? Oncogene 38(50):7447–7456. https://doi.org/10.1038/s41388-019-0963-7

    Article  CAS  PubMed  Google Scholar 

  38. Schwab M, Ellison J, Busch M, Rosenau W, Varmus HE, Bishop JM (1984) Enhanced expression of the human gene N-myc consequent to amplification of DNA may contribute to malignant progression of neuroblastoma. Proc Natl Acad Sci U S A 81(15):4940–4944. https://doi.org/10.1073/pnas.81.15.4940

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Tajbakhsh A, Pasdar A, Rezaee M, Fazeli M, Soleimanpour S, Hassanian SM, FarshchiyanYazdi Z, Younesi Rad T, Ferns GA, Avan A (2018) The current status and perspectives regarding the clinical implication of intracellular calcium in breast cancer. J Cell Physiol 233(8):5623–5641. https://doi.org/10.1002/jcp.26277

    Article  CAS  PubMed  Google Scholar 

  40. 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 

  41. Ohashi K, Wang Z, Yang YM, Billet S, Tu W, Pimienta M, Cassel SL, Pandol SJ, Lu SC, Sutterwala FS, Bhowmick N, Seki E (2019) NOD-like receptor C4 Inflammasome regulates the growth of Colon Cancer liver metastasis in NAFLD. Hepatology 70(5):1582–1599. https://doi.org/10.1002/hep.30693

    Article  CAS  PubMed  Google Scholar 

  42. Coll RC, Robertson AA, Chae JJ, Higgins SC, Munoz-Planillo R, Inserra MC, Vetter I, Dungan LS, Monks BG, Stutz A, Croker DE, Butler MS, Haneklaus M, Sutton CE, Nunez G, Latz E, Kastner DL, Mills KH, Masters SL, Schroder K, Cooper MA, O'Neill LA (2015) A small-molecule inhibitor of the NLRP3 inflammasome for the treatment of inflammatory diseases. Nat Med 21(3):248–255. https://doi.org/10.1038/nm.3806

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Philipp M, Hein L (2004) Adrenergic receptor knockout mice: distinct functions of 9 receptor subtypes. Pharmacol Ther 101(1):65–74. https://doi.org/10.1016/j.pharmthera.2003.10.004

    Article  CAS  PubMed  Google Scholar 

  44. Sassone-Corsi P (2012) The cyclic AMP pathway. Cold Spring Harb Perspect Biol 4(12). https://doi.org/10.1101/cshperspect.a011148

  45. Sapio L, Gallo M, Illiano M, Chiosi E, Naviglio D, Spina A, Naviglio S (2017) The natural cAMP elevating compound Forskolin in Cancer therapy: is it time? J Cell Physiol 232(5):922–927. https://doi.org/10.1002/jcp.25650

    Article  CAS  PubMed  Google Scholar 

  46. Bergantin LB (2019) Diabetes and cancer: debating the link through ca(2+)/cAMP signalling. Cancer Lett 448:128–131. https://doi.org/10.1016/j.canlet.2019.02.017

    Article  CAS  PubMed  Google Scholar 

  47. Zhang X, Zhang Y, He Z, Yin K, Li B, Zhang L, Xu Z (2019) Chronic stress promotes gastric cancer progression and metastasis: an essential role for ADRB2. Cell Death Dis 10(11):788. https://doi.org/10.1038/s41419-019-2030-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Renz BW, Takahashi R, Tanaka T, Macchini M, Hayakawa Y, Dantes Z, Maurer HC, Chen X, Jiang Z, Westphalen CB, Ilmer M, Valenti G, Mohanta SK, Habenicht AJR, Middelhoff M, Chu T, Nagar K, Tailor Y, Casadei R, Di Marco M, Kleespies A, Friedman RA, Remotti H, Reichert M, Worthley DL, Neumann J, Werner J, Iuga AC, Olive KP, Wang TC (2018) beta2 adrenergic-Neurotrophin feedforward loop promotes pancreatic Cancer. Cancer Cell 33 (1):75–90 e77. doi:https://doi.org/10.1016/j.ccell.2017.11.007

  49. Thaker PH, Han LY, Kamat AA, Arevalo JM, Takahashi R, Lu C, Jennings NB, Armaiz-Pena G, Bankson JA, Ravoori M, Merritt WM, Lin YG, Mangala LS, Kim TJ, Coleman RL, Landen CN, Li Y, Felix E, Sanguino AM, Newman RA, Lloyd M, Gershenson DM, Kundra V, Lopez-Berestein G, Lutgendorf SK, Cole SW, Sood AK (2006) Chronic stress promotes tumor growth and angiogenesis in a mouse model of ovarian carcinoma. Nat Med 12(8):939–944. https://doi.org/10.1038/nm1447

    Article  CAS  PubMed  Google Scholar 

  50. Braadland PR, Ramberg H, Grytli HH, Urbanucci A, Nielsen HK, Guldvik IJ, Engedal A, Ketola K, Wang W, Svindland A, Mills IG, Bjartell A, Tasken KA (2019) The beta2-adrenergic receptor is a molecular switch for neuroendocrine Transdifferentiation of prostate Cancer cells. Mol Cancer Res 17(11):2154–2168. https://doi.org/10.1158/1541-7786.MCR-18-0605

    Article  CAS  PubMed  Google Scholar 

  51. Wu FQ, Fang T, Yu LX, Lv GS, Lv HW, Liang D, Li T, Wang CZ, Tan YX, Ding J, Chen Y, Tang L, Guo LN, Tang SH, Yang W, Wang HY (2016) ADRB2 signaling promotes HCC progression and sorafenib resistance by inhibiting autophagic degradation of HIF1alpha. J Hepatol 65(2):314–324. https://doi.org/10.1016/j.jhep.2016.04.019

    Article  CAS  PubMed  Google Scholar 

  52. Xie WY, He RH, Zhang J, He YJ, Wan Z, Zhou CF, Tang YJ, Li Z, McLeod HL, Liu J (2019) Betablockers inhibit the viability of breast cancer cells by regulating the ERK/COX2 signaling pathway and the drug response is affected by ADRB2 singlenucleotide polymorphisms. Oncol Rep 41(1):341–350. https://doi.org/10.3892/or.2018.6830

    Article  CAS  PubMed  Google Scholar 

  53. Kulik G (2019) ADRB2-targeting therapies for prostate Cancer. Cancers (Basel) 11(3). https://doi.org/10.3390/cancers11030358

  54. Li L, Kang L, Zhao W, Feng Y, Liu W, Wang T, Mai H, Huang J, Chen S, Liang Y, Han J, Xu X, Ye Q (2017) miR-30a-5p suppresses breast tumor growth and metastasis through inhibition of LDHA-mediated Warburg effect. Cancer Lett 400:89–98. https://doi.org/10.1016/j.canlet.2017.04.034

    Article  CAS  PubMed  Google Scholar 

  55. Zhou L, Jia S, Ding G, Zhang M, Yu W, Wu Z, Cao L (2019) Down-regulation of miR-30a-5p is associated with poor prognosis and promotes Chemoresistance of gemcitabine in pancreatic ductal adenocarcinoma. J Cancer 10(21):5031–5040. https://doi.org/10.7150/jca.31191

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Tao J, Cong H, Wang H, Zhang D, Liu C, Chu H, Qing Q, Wang K (2018) MiR-30a-5p inhibits osteosarcoma cell proliferation and migration by targeting FOXD1. Biochem Biophys Res Commun 503(2):1092–1097. https://doi.org/10.1016/j.bbrc.2018.06.121

    Article  CAS  PubMed  Google Scholar 

  57. Cheng J, Zhuo H, Xu M, Wang L, Xu H, Peng J, Hou J, Lin L, Cai J (2018) Regulatory network of circRNA-miRNA-mRNA contributes to the histological classification and disease progression in gastric cancer. J Transl Med 16(1):216. https://doi.org/10.1186/s12967-018-1582-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Yang J, Song H, Cao K, Song J, Zhou J (2019) Comprehensive analysis of helicobacter pylori infection-associated diseases based on miRNA-mRNA interaction network. Brief Bioinform 20(4):1492–1501. https://doi.org/10.1093/bib/bby018

    Article  CAS  PubMed  Google Scholar 

  59. Xue J, Zhou D, Poulsen O, Hartley I, Imamura T, Xie EX, Haddad GG (2018) Exploring miRNA-mRNA regulatory network in cardiac pathology in Na(+)/H(+) exchanger isoform 1 transgenic mice. Physiol Genomics 50(10):846–861. https://doi.org/10.1152/physiolgenomics.00048.2018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This work was supported by the Key Project of “Research on Prevention and Control of Major Chronic Non-Communicable Diseases”, Ministry of Science and Technology of the People’s Republic of China (2018YFC1313004), Chongqing Science and Technology Commission (cstc2019jscx-msxmX0220); General Clinical Medical Research Program of Children’s Hospital of Chongqing Medical University (YBXM-2019-003).

Author information

Authors and Affiliations

  1. Children’s Hospital of Chongqing Medical University, Chongqing, People’s Republic of China

    Feng-Ling Shao, Qing-qing Liu & Shan Wang

Authors
  1. Feng-Ling Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qing-qing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Shao performed the research, analyzed the data, and wrote the manuscript, Liu collected the clinical samples, and Wang* contributed to carrying out additional analyses, discussed the results, provided scientific advice, and revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Shan Wang.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

The entire study was approved by the Medical Ethics Committee of Children’s Hospital of Chongqing Medical University and complies with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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.

Supplementary Information

ESM 1

(PDF 270 kb)

ESM 2

(XLSX 141 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shao, FL., Liu, Qq. & Wang, S. Identify potential miRNA-mRNA regulatory networks contributing to high-risk neuroblastoma. Invest New Drugs 39, 901–913 (2021). https://doi.org/10.1007/s10637-021-01064-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10637-021-01064-y

Keywords

Search

Navigation