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Integration of clinical characteristics and molecular signatures of the tumor microenvironment to predict the prognosis of neuroblastoma

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Abstract

This study aimed to analyze the clinical characteristics, cell types, and molecular characteristics of the tumor microenvironment to better predict the prognosis of neuroblastoma (NB). The gene expression data and corresponding clinical information of 498 NB patients were obtained from the Gene Expression Omnibus (GEO: GSE62564) and ArrayExpress (accession: E-MTAB-8248). The relative cell abundances were estimated using single-sample gene set enrichment analysis (ssGSEA) with the R gene set variation analysis (GSVA) package. We performed Cox regression analyses to identify marker genes indicating cell subsets and combined these with prognostically relevant clinical factors to develop a new prognostic model. Data from the E-MTAB-8248 cohort verified the predictive accuracy of the prognostic model. Single-cell RNA-seq data were analyzed by using the R Seurat package. Multivariate survival analysis for each gene, using clinical characteristics as cofactors, identified 34 prognostic genes that showed a significant correlation with both event-free survival (EFS) and overall survival (OS) (log-rank test, P value < 0.05). The pathway enrichment analysis revealed that these prognostic genes were highly enriched in the marker genes of NB cells with mesenchymal features and protein translation. Ultimately, USP39, RPL8, IL1RAPL1, MAST4, CSRP2, ATP5E, International Neuroblastoma Staging System (INSS) stage, age, and MYCN status were selected to build an optimized Cox model for NB risk stratification. These samples were divided into two groups using the median of the risk score as a cutoff. The prognosis of samples in the poor prognosis group (PP) was significantly worse than that of samples in the good prognosis group (GP) (log-rank test, P value < 0.0001, median EFS: 640.5 vs. 2247 days, median OS: 1279.5 vs. 2519 days). The risk model was also regarded as a prognostic indicator independent of MYCN status, age, and stage. Finally, through scRNA-seq data, we found that as an important prognostic marker, USP39 might participate in the regulation of RNA splicing in NB. Our study established a multivariate Cox model based on gene signatures and clinical characteristics to better predict the prognosis of NB and revealed that mesenchymal signature genes of NB cells, especially USP39, were more abundant in patients with a poor prognosis than in those with a good prognosis.

Key messages

  • Our study established a multivariate Cox model based on gene signatures and clinical characteristics to better predict the prognosis of NB and revealed that mesenchymal signature genes of NB cells, especially USP39, were more abundant in patients with a poor prognosis than in those with a good prognosis.

  • USP39, RPL8, IL1RAPL1, MAST4, CSRP2, ATP5E, International Neuroblastoma Staging System (INSS) stage, age, and MYCN status were selected to build an optimized Cox model for NB risk stratification.

  • These samples were divided into two groups using the median of the risk score as a cutoff. The prognosis of samples in the poor prognosis group (PP) was significantly worse than that of samples in the good prognosis group (GP).

  • Finally, through scRNA-seq data, we found that as an important prognostic marker, USP39 might participate in the regulation of RNA splicing in NB.

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Availability of data and materials

The high-throughput data used and/or analyzed during this study were obtained from public databases, including NCBI-GEO (https://www.ncbi.nlm.nih.gov/gds) and ArrayExpress (https://www.ebi.ac.uk/arrayexpress/), which have been cited in Materials and methods.

References

  1. London WB, Castleberry RP, Matthay KK, Look AT, Seeger RC, Shimada H, Thorner P, Brodeur G, Maris JM, Reynolds CP et al (2005) Evidence for an age cutoff greater than 365 days for neuroblastoma risk group stratification in the Children’s Oncology Group. J Clin Oncol 23(27):6459–6465

    Article  CAS  PubMed  Google Scholar 

  2. Pinto NR, Applebaum MA, Volchenboum SL, Matthay KK, London WB, Ambros PF, Nakagawara A, Berthold F, Schleiermacher G, Park JR et al (2015) Advances in risk classification and treatment strategies for neuroblastoma. J Clin Oncol 33(27):3008–3017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Bosse KR, Maris JM (2016) Advances in the translational genomics of neuroblastoma: from improving risk stratification and revealing novel biology to identifying actionable genomic alterations. Cancer 122(1):20–33

    Article  CAS  PubMed  Google Scholar 

  4. Depuydt P, Boeva V, Hocking TD, Cannoodt R, Ambros IM, Ambros PF, Asgharzadeh S, Attiyeh EF, Combaret V, Defferrari R et al (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

    Article  PubMed  PubMed Central  Google Scholar 

  5. Oldridge DA, Wood AC, Weichert-Leahey N, Crimmins I, Sussman R, Winter C, Mcdaniel LD, Diamond M, Hart LS, Zhu S et al (2015) Genetic predisposition to neuroblastoma mediated by a LMO1 super-enhancer polymorphism. Nature 528(7582):418–421

  6. Nguyen Le B, Diskin SJ, Capasso M, Wang K, Diamond MA, Glessner J, Kim C, Attiyeh EF, Mosse YP, Cole K et al (2011) Phenotype restricted genome-wide association study using a gene-centric approach identifies three low-risk neuroblastoma susceptibility Loci. PLoS Genet 7(3):e1002026

  7. Russell MR, Penikis A, Oldridge DA, Alvarez-Dominguez JR, Mcdaniel L, Diamond M, Padovan O, Raman P, Li Y, Wei JS et al (2015) CASC15-S is a tumor suppressor lncRNA at the 6p22 neuroblastoma susceptibility locus. Cancer Res 75(15):3155–3166

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Yu AL, Gilman AL, Ozkaynak MF, London WB, Kreissman SG, Chen HX, Smith M, Anderson B, Villablanca JG, Matthay KK et al (2010) Group Children’s Oncology. Anti-GD2 antibody with GM-CSF, interleukin-2, and isotretinoin for neuroblastoma. N Engl J Med 363(14):1324–1334

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Fridman WH, Pages F, Sautes-Fridman C, Galon J (2012) The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer 12(4):298–306

    Article  CAS  PubMed  Google Scholar 

  10. Borriello L, Seeger RC, Asgharzadeh S, Declerck YA (2016) More than the genes, the tumor microenvironment in neuroblastoma. Cancer Lett 380(1):304–314

    Article  CAS  PubMed  Google Scholar 

  11. Su Z, Fang H, Hong H, Shi L, Zhang W, Zhang W, Zhang Y, Dong Z, Lancashire LJ, Bessarabova M, Yang X et al (2014) An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era. Genome Biol 15(12):523

    Article  PubMed  Google Scholar 

  12. Roderwieser A, Sand F, Walter E, Fischer J, Gecht J, Bartenhagen C, Ackermann S, Otte F, Welte A, Kahlert Y et al (2019) Telomerase Is a prognostic marker of poor outcome and a therapeutic target in neuroblastoma. JCO Precis Oncol 3:1–20

    PubMed  Google Scholar 

  13. Ma X, Liu Y, Liu Y, Alexandrov LB, Edmonson MN, Gawad C, Zhou X, Li Y, Rusch MC, Easton J et al (2018) Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours. Nature 555(7696):371–376

  14. Brodeur GM, Pritchard J, Berthold F, Carlsen NL, Castel V, Castelberry RP, De Bernardi B, Evans AE, Favrot M, Hedborg F et al (1993) Revisions of the international criteria for neuroblastoma diagnosis, staging, and response to treatment. J Clin Oncol 11(8):1466–1477

    Article  CAS  PubMed  Google Scholar 

  15. Liang WH, Federico SM, London WB, Naranjo A, Irwin MS, Volchenboum SL, Cohn SL (2020) Tailoring therapy for children with neuroblastoma on the basis of risk group classification: past, present, and future. JCO Clin Cancer Inform 4:895–905

    Article  PubMed  Google Scholar 

  16. Dong R, Yang R, Zhan Y, Lai HD, Ye CJ, Yao XY, Luo WQ, Cheng XM, Miao JJ, Wang JF et al (2020) Single-cell characterization of malignant phenotypes and developmental trajectories of adrenal neuroblastoma. Cancer Cell 38(5):716–733 e716

  17. Harenza JL, Diamond MA, Adams RN, Song MM, Davidson HL, Hart LS, Dent MH, Fortina P, Reynolds CP, Maris JM (2017) Transcriptomic profiling of 39 commonly-used neuroblastoma cell lines. Sci Data 4:170033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hao Y, Hao S, Andersen-Nissen E, Mauck WM, Zheng 3rd S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M et al (2021) Integrated analysis of multimodal single-cell data. Cell 184(13):3573–3587e3529

  19. Senbabaoglu Y, Gejman RS, Winer AG, Liu M, Van Allen EM, De Velasco G, Miao D, Ostrovnaya I, Drill E, Luna A et al (2016) Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biol 17(1):231

    Article  PubMed  PubMed Central  Google Scholar 

  20. Xiao Y, Ma D, Zhao S, Suo C, Shi J, Xue MZ, Ruan M, Wang H, Zhao J, Li Q et al (2019) Multi-omics profiling reveals distinct microenvironment characterization and suggests immune escape mechanisms of triple-negative breast cancer. Clin Cancer Res 25(16):5002–5014

    Article  CAS  PubMed  Google Scholar 

  21. Jansky S, Sharma AK, Korber V, Quintero A, Toprak UH, Wecht EM, Gartlgruber M, Greco A, Chomsky E, Grunewald TGP et al (2021) Single-cell transcriptomic analyses provide insights into the developmental origins of neuroblastoma. Nat Genet 53(5):683–693

    Article  CAS  PubMed  Google Scholar 

  22. Hanzelmann S, Castelo R, Guinney J (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14:7

    Article  PubMed  PubMed Central  Google Scholar 

  23. Zhang PB, Huang ZL, Xu YH, Huang J, Huang XY, Huang XY (2019) Systematic analysis of gene expression profiles reveals prognostic stratification and underlying mechanisms for muscle-invasive bladder cancer. Cancer Cell Int 19:337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P (2015) The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst 1(6):417–425

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L et al (2021)  clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb) 2(3):100141

  26. Xing J, Ren L, Xu H, Zhao L, Wang ZH, Hu GD, Wei ZL (2022) Single-cell RNA sequencing reveals cellular and transcriptional changes associated with traumatic brain injury. Front Genet 13:861428

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Mou W, Yang S, Guo R, Fu L, Zhang L, Guo W, Du J, He J, Ren Q, Hao C et al (2021) A novel homozygous TTC7A missense mutation results in familial multiple intestinal atresia and combined immunodeficiency. Front Immunol 12:759308

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Irwin MS, Naranjo A, Zhang FF, Cohn SL, London WB, Gastier-Foster JM, Ramirez NC, Pfau R, Reshmi S, Wagner E et al (2021) Revised neuroblastoma risk classification system: a report from the Children’s Oncology Group. J Clin Oncol 39(29):3229–3241

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Bao R, Spranger S, Hernandez K, Zha Y, Pytel P, Luke JJ, Gajewski TF, Volchenboum SL, Cohn SL, Desai AV (2021) Immunogenomic determinants of tumor microenvironment correlate with superior survival in high-risk neuroblastoma. J Immunother Cancer 9(7)

  30. Schaafsma E, Jiang C, Cheng C (2021) B cell infiltration is highly associated with prognosis and an immune-infiltrated tumor microenvironment in neuroblastoma. J Cancer Metastasis Treat 7(34)

  31. Yu JL, Chan S, Fung MK, Chan GC (2021) Mesenchymal stem cells accelerated growth and metastasis of neuroblastoma and preferentially homed towards both primary and metastatic loci in orthotopic neuroblastoma model. BMC Cancer 21(1):393

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kameneva P, Artemov AV, Kastriti ME, Faure L, Olsen TK, Otte J, Erickson A, Semsch B, Andersson ER, Ratz M et al (2021) Single-cell transcriptomics of human embryos identifies multiple sympathoblast lineages with potential implications for neuroblastoma origin. Nat Genet 53(5):694–706

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Hanemaaijer ES, Margaritis T, Sanders K, Bos FL, Candelli T, Al-Saati H, Van Noesel MM, Meyer-Wentrup FAG, Van De Wetering M, Holstege FCP et al (2021) Single-cell atlas of developing murine adrenal gland reveals relation of Schwann cell precursor signature to neuroblastoma phenotype. Proc Natl Acad Sci USA 118(5)

  34. Wei JS, Kuznetsov IB, Zhang S, Song YK, Asgharzadeh S, Sindiri S, Wen X, Patidar R, Najaraj S, Walton A et al (2018) Clinically relevant cytotoxic immune cell signatures and clonal expansion of T-cell receptors in high-risk MYCN-not-amplified human neuroblastoma. Clin Cancer Res 24(22):5673–5684

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zhong X, Zhang Y, Wang L, Zhang H, Liu H, Liu Y (2019) Cellular components in tumor microenvironment of neuroblastoma and the prognostic value. PeerJ 7:e8017

    Article  PubMed  PubMed Central  Google Scholar 

  36. Liu C, Yao X, Li M, Xi Y, Zhao L (2019) USP39 regulates the cell cycle, survival, and growth of human leukemia cells. Biosci Rep 39(4)

  37. Xu X, Xiong X, Sun Y (2016) The role of ribosomal proteins in the regulation of cell proliferation, tumorigenesis, and genomic integrity. Sci China Life Sci 59(7):656–672

    Article  CAS  PubMed  Google Scholar 

  38. Meng X, Li H, Fang E, Feng J, Zhao X (2020) Comparison of stage 4 and stage 4s neuroblastoma identifies autophagy-related gene and lncRNA signatures associated with prognosis. Front Oncol 10:1411

    Article  PubMed  PubMed Central  Google Scholar 

  39. Westermann F, Muth D, Benner A, Bauer T, Henrich KO, Oberthuer A, Brors B, Beissbarth T, Vandesompele J, Pattyn F et al (2008) Distinct transcriptional MYCN/c-MYC activities are associated with spontaneous regression or malignant progression in neuroblastomas. Genome Biol 9(10):R150

    Article  PubMed  PubMed Central  Google Scholar 

  40. Ding K, Ji J, Zhang X, Huang B, Chen A, Zhang D, Li X, Wang X, Wang J (2019) RNA splicing factor USP39 promotes glioma progression by inducing TAZ mRNA maturation. Oncogene 38(37):6414–6428

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Yan C, Yuan J, Xu J, Zhang G, Li X, Zhang B, Hu T, Huang X, Mao Y, Song G (2019) Ubiquitin-specific peptidase 39 regulates the process of proliferation and migration of human ovarian cancer via p53/p21 pathway and EMT. Med Oncol 36(11):95

    Article  PubMed  Google Scholar 

  42. Dong X, Su H, Jiang F, Li H, Shi G, Fan L (2018) miR-133a, directly targeted USP39, suppresses cell proliferation and predicts prognosis of gastric cancer. Oncol Lett 15(6):8311–8318

    PubMed  PubMed Central  Google Scholar 

  43. Huang Y, Pan XW, Li L, Chen L, Liu X, Lu JL, Zhu XM, Huang H, Yang QW, Ye JQ et al (2016) Overexpression of USP39 predicts poor prognosis and promotes tumorigenesis of prostate cancer via promoting EGFR mRNA maturation and transcription elongation. Oncotarget 7(16):22016–22030

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors would like to acknowledge the support of Dr. Lejian He and Dr. Libing Fu, Department of Pathology, Beijing Children’s Hospital, Capital Medical University, in the IHC analysis.

Funding

This work was supported by the Consulting and Research Project of Chinese Academy of Engineering (2019-XY-34).

Author information

Author notes

  1. Haiyan Cheng and Li Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Surgical Oncology, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, 56 Nanlishi Road, Beijing, 100045, China

    Haiyan Cheng, Shen Yang, Qinghua Ren, Saishuo Chang, Hong Qin, Wei Yang & Huanmin Wang

  2. Shanghai Institute of Precision Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China

    Li Zhang

  3. Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck Surgery, MOE Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, 100045, China

    Yaqiong Jin

  4. Laboratory of Tumor Immunology, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, 100045, China

    Wenjun Mou

  5. Zhengzhou Key Laboratory of Precise Diagnosis and Treatment of Children’s Malignant Tumors, Department of Pediatric Oncology Surgery, Children’s Hospital Affiliated to Zhengzhou University, Henan Children’s Hospital, Zhengzhou Children’s Hospital, Zhengzhou, 450018, China

    Xianwei Zhang & Wancun Zhang

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  1. Haiyan Cheng
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Contributions

Conception and design: Huanmin Wang, Haiyan Cheng, Li Zhang. Administrative support: Hong Qin, Wei Yang. Collection and assembly of data: Qinghua Ren, Saishuo Chang. Data analysis and interpretation: Li Zhang, Shen Yang. The major contributors to manuscript writing: Haiyan Cheng, Li Zhang. Final approval of manuscript: All authors. Accountable for all aspects of the work: All authors.

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Correspondence to Huanmin Wang.

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Cheng, H., Zhang, L., Yang, S. et al. Integration of clinical characteristics and molecular signatures of the tumor microenvironment to predict the prognosis of neuroblastoma. J Mol Med 101, 1421–1436 (2023). https://doi.org/10.1007/s00109-023-02372-x

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  • DOI: https://doi.org/10.1007/s00109-023-02372-x

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