Skip to main content
SpringerLink
Log in

A comprehensive prognostic signature for glioblastoma patients based on transcriptomics and single cell sequencing

  • Original Article
  • Published:
Cellular Oncology Aims and scope Submit manuscript

Abstract

Purpose

Glioblastoma (GBM) is the most common and deadly brain tumor. We aimed to reveal potential prognostic GBM marker genes, elaborate their functions, and build an effective a prognostic model for GBM patients.

Methods

Through data mining of The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), we screened for significantly differentially expressed genes (DEGs) to calculate risk scores for individual patients. Published data of somatic mutation and copy number variation profiles were analyzed for distinct genomic alterations associated with risk scores. In addition, single-cell sequencing was used to explore the biological functions of the identified prognostic marker genes. By combining risk scores and other clinical features, we built a comprehensive prognostic GBM model.

Results

Seven DEGs (CLEC5A, HOXC6, HOXA5, CCL2, GPRASP1, BSCL2 and PTX3) were identified as being prognostic for GBM. Expression of these genes was confirmed in different GBM cell lines using real-time PCR. Risk scores calculated from the seven DEGs revealed prognostic value irrespective of other clinical factors, including IDH mutation status, and were negatively correlated with TP53 expression. The prognostic genes were found to be associated with tumor proliferation and progression based on pseudo-time analysis in neoplastic cells. A final prognostic model was developed and validated with a good performance, especially in geriatric GBM patients.

Conclusions

Using genetic profiles, age, IDH mutation status, and chemotherapy and radiotherapy, we constructed a comprehensive prognostic model for GBM patients. The model has a good performance, especially in geriatric GBM patients.

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
Fig. 8

Similar content being viewed by others

Data availability

The datasets generated and analyzed during the current study are available in the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/), TCGA data source (https://xena.ucsc.edu) and CGGA data portal (http://www.cgga.org.cn).

Abbreviations

GBM:

Glioblastoma

TCGA:

The Cancer Genome Atlas

CGGA:

Chinese Glioma Genome Atlas

DEGs:

differentially expressed genes

GSVA:

geneset variation analysis

WHO:

World Health Organization

OS:

overall surviva

ROC:

receiver operating characteristic

AUC:

area under the curve

CNV:

copy number variation

GO:

gene ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

OPC:

oligodendrocyte precursor cell

BP:

biological process

MF:

molecular function

NGS:

next-generation sequencing

References

  1. Y. Yan, Z. Xu, S. Dai, L. Qian, L. Sun, Z. Gong, Targeting autophagy to sensitive glioma to temozolomide treatment. J. Exp. Clin. Cancer Res. 35, 23 (2016). https://doi.org/10.1186/s13046-016-0303-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. T. Jiang, Y. Mao, W. Ma, Q. Mao, Y. You, X. Yang, C. Jiang, C. Kang, X. Li, L. Chen, X. Qiu, W. Wang, W. Li, Y. Yao, S. Li, S. Li, A. Wu, K. Sai, H. Bai, G. Li, B. Chen, K. Yao, X. Wei, X. Liu, Z. Zhang, Y. Dai, S. Lv, L. Wang, Z. Lin, J. Dong, G. Xu, X. Ma, J. Cai, W. Zhang, H. Wang, L. Chen, C. Zhang, P. Yang, W. Yan, Z. Liu, H. Hu, J. Chen, Y. Liu, Y. Yang, Z. Wang, Z. Wang, Y. Wang, G. You, L. Han, Z. Bao, Y. Liu, Y. Wang, X. Fan, S. Liu, X. Liu, Y. Wang, Q. Wang, G. Chinese Glioma Cooperative, CGCG clinical practice guidelines for the management of adult diffuse gliomas. Cancer Lett. 375, 263–273 (2016). https://doi.org/10.1016/j.canlet.2016.01.024

  3. H. Zhang, R. Wang, Y. Yu, J. Liu, T. Luo, F. Fan, Glioblastoma treatment modalities besides surgery. J. Cancer 10, 4793–4806 (2019). https://doi.org/10.7150/jca.32475

  4. X. Chen, L. Zhang, I.Y. Zhang, J. Liang, H. Wang, M. Ouyang, S. Wu, A.C.C. da Fonseca, L. Weng, Y. Yamamoto, H. Yamamoto, R. Natarajan, B. Badie, RAGE expression in tumor-associated macrophages promotes angiogenesis in glioma. Cancer Res. 74, 7285–7297 (2014). https://doi.org/10.1158/0008-5472.CAN-14-1240

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. K. Yang, Y. Liu, Z. Liu, J. Liu, X. Liu, X. Chen, C. Li, Y. Zeng, p38gamma overexpression in gliomas and its role in proliferation and apoptosis. Sci. Rep. 3, 2089 (2013). https://doi.org/10.1038/srep02089

  6. J. Huang, F. Liu, Z. Liu, H. Tang, H. Wu, Q. Gong, J. Chen, Immune checkpoint in glioblastoma: promising and challenging. Front. Pharmacol. 8, 242 (2017). https://doi.org/10.3389/fphar.2017.00242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. W.S. Wen, S.L. Hu, Z. Ai, L. Mou, J.M. Lu, S. Li, Methylated of genes behaving as potential biomarkers in evaluating malignant degree of glioblastoma. J. Cell. Physiol. 232, 3622–3630 (2017). https://doi.org/10.1002/jcp.25831

    Article  CAS  PubMed  Google Scholar 

  8. M.M. Binabaj, A. Bahrami, S. ShahidSales, M. Joodi, M. Joudi Mashhad, S.M. Hassanian, K. Anvari, A. Avan, The prognostic value of MGMT promoter methylation in glioblastoma: A meta-analysis of clinical trials. J. Cell. Physiol. 233, 378–386 (2018). https://doi.org/10.1002/jcp.25896

    Article  CAS  PubMed  Google Scholar 

  9. H. Zhang, Y. Zhou, B. Cui, Z. Liu, H. Shen, Novel insights into astrocyte-mediated signaling of proliferation, invasion and tumor immune microenvironment in glioblastoma. Biomed. Pharmacother. 126, 110086 (2020). https://doi.org/10.1016/j.biopha.2020.110086

    Article  CAS  PubMed  Google Scholar 

  10. Z. Wang, H. Zhang, Q. Cheng, PDIA4, The basic characteristics, functions and its potential connection with cancer. Biomed. Pharmacother. 122, 109688 (2020). https://doi.org/10.1016/j.biopha.2019.109688

    Article  CAS  PubMed  Google Scholar 

  11. H. Zhang, B. Cui, Y. Zhou, X. Wang, W. Wu, Z. Wang, Z. Dai, Q. Cheng, K. Yang, B2M overexpression correlates with malignancy and immune signatures in human gliomas. Sci. Rep. 11, 5045 (2021). https://doi.org/10.1038/s41598-021-84465-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. H. Zhang, J. He, Z. Dai, Z. Wang, X. Liang, F. He, Z. Xia, S. Feng, H. Cao, L. Zhang, Q. Cheng, PDIA5 is Correlated with immune infiltration and predicts poor prognosis in gliomas. Front. Immunol. 12, 628966 (2021). https://doi.org/10.3389/fimmu.2021.628966

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. H. Zhang, Z. Chen, Z. Wang, Z. Dai, Z. Hu, X. Zhang, M. Hu, Z. Liu, Q. Cheng, Correlation between APOBEC3B expression and clinical characterization in lower-grade gliomas. Front. Oncol. 11, 625838 (2021). https://doi.org/10.3389/fonc.2021.625838

    Article  PubMed  PubMed Central  Google Scholar 

  14. N. Zhang, Z. Dai, W. Wu, Z. Wang, H. Cao, Y. Zhang, Z. Wang, H. Zhang, Q. Cheng, The predictive value of monocytes in immune microenvironment and prognosis of glioma patients based on machine learning. Front. Immunol. 12, 656541 (2021). https://doi.org/10.3389/fimmu.2021.656541

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. L. Gautier, L. Cope, B.M. Bolstad, R.A. Irizarry, affy–analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004). https://doi.org/10.1093/bioinformatics/btg405

    Article  CAS  PubMed  Google Scholar 

  16. J.T. Leek, W.E. Johnson, H.S. Parker, A.E. Jaffe, J.D. Storey, The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012). https://doi.org/10.1093/bioinformatics/bts034

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. M.E. Ritchie, B. Phipson, D. Wu, Y. Hu, C.W. Law, W. Shi, G.K. Smyth, limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015). https://doi.org/10.1093/nar/gkv007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. J.J. Goeman, L1 penalized estimation in the Cox proportional hazards model. Biom. J. 52, 70–84 (2010). https://doi.org/10.1002/bimj.200900028

    Article  PubMed  Google Scholar 

  19. S. Hanzelmann, R. Castelo, J. Guinney, GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinf. 14, 7 (2013). https://doi.org/10.1186/1471-2105-14-7

  20. P. Geeleher, N. Cox, R.S. Huang, pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One 9, e107468 (2014). https://doi.org/10.1371/journal.pone.0107468

  21. T. Stuart, A. Butler, P. Hoffman, C. Hafemeister, E. Papalexi, W.M. Mauck 3rd, Y. Hao, M. Stoeckius, P. Smibert, R. Satija, Comprehensive integration of single-cell data. Cell 177, 1888–1902 e1821 (2019). https://doi.org/10.1016/j.cell.2019.05.031

  22. R. Wang, R. Sharma, X. Shen, A.M. Laughney, K. Funato, P.J. Clark, M. Shpokayte, P. Morgenstern, M. Navare, Y. Xu, S. Harbi, I. Masilionis, G. Nanjangud, Y. Yang, G. Duran-Rehbein, M. Hemberg, D. Pe’er, V. Tabar, Adult human glioblastomas harbor radial glia-like cells. Stem Cell Rep. 14, 338–350 (2020). https://doi.org/10.1016/j.stemcr.2020.01.007

    Article  CAS  Google Scholar 

  23. M.A. Tosches, T.M. Yamawaki, R.K. Naumann, A.A. Jacobi, G. Tushev, G. Laurent, Evolution of pallium, hippocampus, and cortical cell types revealed by single-cell transcriptomics in reptiles. Science 360, 881–888 (2018). https://doi.org/10.1126/science.aar4237

    Article  CAS  PubMed  Google Scholar 

  24. X. Han, Z. Zhou, L. Fei, H. Sun, R. Wang, Y. Chen, H. Chen, J. Wang, H. Tang, W. Ge, Y. Zhou, F. Ye, M. Jiang, J. Wu, Y. Xiao, X. Jia, T. Zhang, X. Ma, Q. Zhang, X. Bai, S. Lai, C. Yu, L. Zhu, R. Lin, Y. Gao, M. Wang, Y. Wu, J. Zhang, R. Zhan, S. Zhu, H. Hu, C. Wang, M. Chen, H. Huang, T. Liang, J. Chen, W. Wang, D. Zhang, G. Guo, Construction of a human cell landscape at single-cell level. Nature. 581, 303–309 (2020). https://doi.org/10.1038/s41586-020-2157-4

    Article  CAS  PubMed  Google Scholar 

  25. B. Pang, J. Xu, J. Hu, F. Guo, L. Wan, M. Cheng, L. Pang, Single-cell RNA-seq reveals the invasive trajectory and molecular cascades underlying glioblastoma progression. Mol. Oncol. 13, 2588–2603 (2019). https://doi.org/10.1002/1878-0261.12569

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. X. Robin, N. Turck, A. Hainard, N. Tiberti, F. Lisacek, J.C. Sanchez, M. Muller, pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinf. 12, 77 (2011). https://doi.org/10.1186/1471-2105-12-77

    Article  Google Scholar 

  27. M. Zhang, X. Wang, X. Chen, Q. Zhang, J. Hong, Novel immune-related gene signature for risk stratification and prognosis of survival in lower-grade glioma. Front. Genet. 11, 363 (2020). https://doi.org/10.3389/fgene.2020.00363

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. M. Cao, J. Cai, Y. Yuan, Y. Shi, H. Wu, Q. Liu, Y. Yao, L. Chen, W. Dang, X. Zhang, J. Xiao, K. Yang, Z. He, X. Yao, Y. Cui, X. Zhang, X. Bian, A four-gene signature-derived risk score for glioblastoma: prospects for prognostic and response predictive analyses. Cancer Biol. Med. 16, 595–605 (2019). https://doi.org/10.20892/j.issn.2095-3941.2018.0277

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. H.W. Fan, Q. Ni, Y.N. Fan, Z.X. Ma, Y.B. Li, C-type lectin domain family 5, member A (CLEC5A, MDL-1) promotes brain glioblastoma tumorigenesis by regulating PI3K/Akt signalling. Cell Prolif. e12584 (2019). https://doi.org/10.1111/cpr.12584

  30. T.F. Yan, M.J. Wu, B. Xiao, Q. Hu, Y.H. Fan, X.G. Zhu, Knockdown of HOXC6 inhibits glioma cell proliferation and induces cell cycle arrest by targeting WIF-1 in vitro and vivo. Pathol. Res. Pract. 214, 1818–1824 (2018). https://doi.org/10.1016/j.prp.2018.09.001

    Article  CAS  PubMed  Google Scholar 

  31. G. Arderiu, I. Cuevas, A. Chen, M. Carrio, L. East, N.J. Boudreau, HoxA5 stabilizes adherens junctions via increased Akt1. Cell Adhes. Migr. 1, 185–195 (2007). https://doi.org/10.4161/cam.1.4.5448

    Article  Google Scholar 

  32. P.J. Cimino, Y. Kim, H.J. Wu, J. Alexander, H.G. Wirsching, F. Szulzewsky, K. Pitter, T. Ozawa, J. Wang, J. Vazquez, S. Arora, R. Rabadan, R. Levine, F. Michor, E.C. Holland, Increased HOXA5 expression provides a selective advantage for gain of whole chromosome 7 in IDH wild-type glioblastoma. Genes Dev. 32, 512–523 (2018). https://doi.org/10.1101/gad.312157.118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. B. Lu, Y. Zhou, Z. Su, A. Yan, P. Ding, Effect of CCL2 siRNA on proliferation and apoptosis in the U251 human glioma cell line. Mol. Med. Rep. 16, 3387–3394 (2017). https://doi.org/10.3892/mmr.2017.6995

    Article  CAS  PubMed  Google Scholar 

  34. A.L. Chang, J. Miska, D.A. Wainwright, M. Dey, C.V. Rivetta, D. Yu, D. Kanojia, K.C. Pituch, J. Qiao, P. Pytel, Y. Han, M. Wu, L. Zhang, C.M. Horbinski, A.U. Ahmed, M.S. Lesniak, CCL2 produced by the glioma microenvironment is essential for the recruitment of regulatory T cells and myeloid-derived suppressor cells. Cancer Res. 76, 5671–5682 (2016). https://doi.org/10.1158/0008-5472.CAN-16-0144

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. E. Mori, J. Fujikura, M. Noguchi, K. Nakao, M. Matsubara, M. Sone, D. Taura, T. Kusakabe, K. Ebihara, T. Tanaka, K. Hosoda, K. Takahashi, I. Asaka, N. Inagaki, K. Nakao, Impaired adipogenic capacity in induced pluripotent stem cells from lipodystrophic patients with BSCL2 mutations. Metabolism 65, 543–556 (2016). https://doi.org/10.1016/j.metabol.2015.12.015

    Article  CAS  PubMed  Google Scholar 

  36. L. Liu, Q. Jiang, X. Wang, Y. Zhang, R.C. Lin, S.M. Lam, G. Shui, L. Zhou, P. Li, Y. Wang, X. Cui, M. Gao, L. Zhang, Y. Lv, G. Xu, G. Liu, D. Zhao, H. Yang, Adipose-specific knockout of SEIPIN/BSCL2 results in progressive lipodystrophy. Diabetes 63, 2320–2331 (2014). https://doi.org/10.2337/db13-0729

    Article  PubMed  Google Scholar 

  37. L. Wirestam, H. Enocsson, T. Skogh, M.L. Eloranta, L. Ronnblom, C. Sjowall, J. Wettero, Interferon-alpha coincides with suppressed levels of pentraxin-3 (PTX3) in systemic lupus erythematosus and regulates leucocyte PTX3 in vitro. Clin. Exp. Immunol. 189, 83–91 (2017). https://doi.org/10.1111/cei.12957

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. C. Garlanda, B. Bottazzi, A. Bastone, A. Mantovani, Pentraxins at the crossroads between innate immunity, inflammation, matrix deposition, and female fertility. Annu. Rev. Immunol. 23, 337–366 (2005). https://doi.org/10.1146/annurev.immunol.23.021704.115756

    Article  CAS  PubMed  Google Scholar 

  39. Q. Liu, X.Y. Wang, Y.Y. Qin, X.L. Yan, H.M. Chen, Q.D. Huang, J.K. Chen, J.M. Zheng, SPOCD1 promotes the proliferation and metastasis of glioma cells by up-regulating PTX3. Am. J. Cancer Res. 8, 624–635 (2018)

    CAS  PubMed  PubMed Central  Google Scholar 

  40. M. Locatelli, S. Ferrero, F. Martinelli Boneschi, L. Boiocchi, M. Zavanone, S. Maria Gaini, L. Bello, S. Valentino, E. Barbati, M. Nebuloni, A. Mantovani, C. Garlanda, The long pentraxin PTX3 as a correlate of cancer-related inflammation and prognosis of malignancy in gliomas. J. Neuroimmunol. 260, 99–106 (2013). https://doi.org/10.1016/j.jneuroim.2013.04.009

    Article  CAS  PubMed  Google Scholar 

  41. Z. Chen, X. Feng, C.J. Herting, V.A. Garcia, K. Nie, W.W. Pong, R. Rasmussen, B. Dwivedi, S. Seby, S.A. Wolf, D.H. Gutmann, D. Hambardzumyan, Cellular and molecular identity of tumor-associated macrophages in glioblastoma. Cancer Res. 77, 2266–2278 (2017). https://doi.org/10.1158/0008-5472.CAN-16-2310

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. S. Nakae, T. Kato, K. Murayama, H. Sasaki, M. Abe, M. Kumon, T. Kumai, K. Yamashiro, J. Inamasu, M. Hasegawa, H. Kurahashi, Y. Hirose, Remote intracranial recurrence of IDH mutant gliomas is associated with TP53 mutations and an 8q gain. Oncotarget 8, 84729–84742 (2017). https://doi.org/10.18632/oncotarget.20951

    Article  PubMed  PubMed Central  Google Scholar 

  43. T. Yang, P. Mao, X. Chen, X. Niu, G. Xu, X. Bai, W. Xie, Inflammatory biomarkers in prognostic analysis for patients with glioma and the establishment of a nomogram. Oncol. Lett. 17, 2516–2522 (2019). https://doi.org/10.3892/ol.2018.9870

    Article  CAS  PubMed  Google Scholar 

  44. M. Lacroix, D. Abi-Said, D.R. Fourney, Z.L. Gokaslan, W. Shi, F. DeMonte, F.F. Lang, I.E. McCutcheon, S.J. Hassenbusch, E. Holland, K. Hess, C. Michael, D. Miller, R. Sawaya, A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J. Neurosurg. 95, 190–198 (2001). https://doi.org/10.3171/jns.2001.95.2.0190

    Article  CAS  PubMed  Google Scholar 

  45. Q.T. Ostrom, H. Gittleman, J. Fulop, M. Liu, R. Blanda, C. Kromer, Y. Wolinsky, C. Kruchko, J.S. Barnholtz-Sloan, CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008–2012. Neuro Oncol. 17(4), iv1–iv62 (2015). https://doi.org/10.1093/neuonc/nov189

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank Prof. Bing Jiang and Prof. Zhixiong Liu for their assistance in this research, and Dr. Chris Lou for bioinformatics support.

Funding

This work was supported by the Hunan provincial health and Health Committee Foundation of China (C2019186), the China Postdoctoral Science Foundation (NO.2018M633002), the Hunan Provincial Natural Science Foundation of China (NO.2018JJ3838) and the Science and Technology Department of Hunan Province (NO.2015SK2032-2).

Author information

Author notes

  1. Fan Fan, Hao Zhang and Ziyu Dai contributed equally to this work.

Authors and Affiliations

  1. Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People’s Republic of China

    Fan Fan, Hao Zhang, Ziyu Dai, Kui Yang, Yong Guo & Quan Cheng

  2. Center for Medical Genetics & Hunan Provincial Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China

    Fan Fan

  3. National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Hunan, China

    Fan Fan, Kui Yang & Quan Cheng

  4. One-third Lab, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang, 150000, People’s Republic of China

    Yakun Zhang, Shui Hu & Nan Zhang

  5. Department of Neurology, Hunan Aerospace Hospital, Changsha, China

    Zhiwei Xia

  6. Department of Psychiatry, The Second People’s Hospital of Hunan Province, The Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China

    Hui Cao

  7. Department of Clinical Laboratory Medicine, People’s Hospital of Ningxia Hui Autonomous Region, First Affiliated Hospital of Northwest Minzu University, Yinchuan, Ningxia, China

    Fengqin Ding

Authors
  1. Fan Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ziyu Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yakun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhiwei Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hui Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kui Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Shui Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yong Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Fengqin Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Quan Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Nan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Quan Cheng has made substantial contributions to the conception and design of the study, the acquisition of data and the analysis and interpretation of data. Fan Fan, Hao Zhang, Nan Zhang and Ziyu Dai have been involved in drafting the manuscript and revising it. Yakun Zhang, Zhiwei Xia, Kui Yang, Fengqin Ding and Shui Hu provided technical assistance. Hui Cao and Yong Guo provided writing assistance.

Corresponding authors

Correspondence to Quan Cheng or Nan Zhang.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

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

Supplementary Information

Table S1

(DOCX 16 kb)

Table S2

(DOCX 14 kb)

Table S3

(XLSX 46 kb)

Table S4

(DOCX 19 kb)

Table S5

(DOCX 17 kb)

ESM 1

(DOCX 7457 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, F., Zhang, H., Dai, Z. et al. A comprehensive prognostic signature for glioblastoma patients based on transcriptomics and single cell sequencing. Cell Oncol. 44, 917–935 (2021). https://doi.org/10.1007/s13402-021-00612-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13402-021-00612-1

Keywords

Search

Navigation