Skip to main content

Advertisement

SpringerLink
Log in

Identification of biomarkers associated with extracellular vesicles based on an integrative pan-cancer bioinformatics analysis

  • Original Paper
  • Published:
Medical Oncology Aims and scope Submit manuscript

Abstract

Extracellular vesicle (EV) has received increasing attention over the last decade. However, biomarkers and mechanisms underlying remain largely limited. Three microarray profiles, GSE78718 (K562 leukemia cell line), GSE45301 (U87-MG glioblastoma cell line), and GSE9589 (SW480 colon cancer cell line), were analyzed for the overlapped differentially expressed genes (DEGs). SurvExpress was used for the prognostic analysis of hub genes signature. Predicted transcription factors networks were built by NetworkAnalysis. Characterization between hub genes and immune cells was analyzed by the tumor immune estimation resources (TIMER) and single-sample gene set enrichment analysis (ssGSEA). The most significantly enriched pathway was lysosome. Hub genes included lysosomal-associated membrane protein 1 (LAMP1), heat shock protein family A (Hsp70) member 5 (HSPA5), lysosomal-associated membrane protein 2 (LAMP2), integrin subunit alpha V (ITGAV), and transmembrane protein 30A (TMEM30A). Significant prognostic values of hub genes signature were identified in glioblastoma (P-value = 0.006), but not colon cancer. In colon cancer, ITGAV displayed remarkably high correlation with tumor immune infiltrating cells. In glioblastoma, the highest correlation was found between HSPA5 and dendritic cell. Moreover, distinct association of immune cells between cell and EV were identified via ssGSEA. This study identified biomarkers in EV with potential immunological insights and clinical values.

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

Abbreviations

DEGs:

Differentially expressed genes

GEO:

Gene Expression Omnibus

EV:

Extracellular vesicles

GO:

Gene ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

PPI:

Protein–protein interaction

TCGA:

The caner genome atlas

TFs:

Transcription factors

TIICs:

Tumor immune infiltrating cells

TIMER:

Tumor immune estimation resources

SsGSEA:

Single-sample gene set enrichment analysis

LAMP1:

Lysosomal-associated membrane protein 1

HSPA5:

Heat shock protein family A (Hsp70) member 5

LAMP2:

Lysosomal-associated membrane protein 2

ITGAV:

Integrin subunit alpha V

TMEM30A:

Transmembrane protein 30A

References

  1. Raposo G, Stoorvogel W. Extracellular vesicles: exosomes, microvesicles, and friends. J Cell Biol. 2013;200(4):373–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Muralidharan-Chari V, Clancy JW, Sedgwick A, et al. Microvesicles: mediators of extracellular communication during cancer progression. J Cell Sci. 2010;123(10):1603–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Turturici G, Tinnirello R, Sconzo G, et al. Extracellular membrane vesicles as a mechanism of cell-to-cell communication: advantages and disadvantages. Am J Physiol Cell Physiol. 2014;306(7):C621–C633633.

    Article  CAS  PubMed  Google Scholar 

  4. Revenfeld ALS, Bæk R, Nielsen MH, et al. Diagnostic and prognostic potential of extracellular vesicles in peripheral blood. Clin Ther. 2014;36(6):830–46.

    Article  PubMed  Google Scholar 

  5. Christianson HC, Svensson KJ, Belting M. Exosome and microvesicle mediated phene transfer in mammalian cells[C]//Seminars in cancer biology. Academic Press. 2014;28:31–8.

    CAS  Google Scholar 

  6. Cocucci E. Meldolesi J Ectosomes and exosomes: shedding the confusion between extracellular vesicles. Trends Cell Biol. 2015;25(6):364–72.

    Article  CAS  PubMed  Google Scholar 

  7. Valadi H, Ekström K, Bossios A, et al. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol. 2007;9(6):654.

    Article  CAS  PubMed  Google Scholar 

  8. Deregibus MC, Cantaluppi V, Calogero R, et al. Endothelial progenitor cell–derived microvesicles activate an angiogenic program in endothelial cells by a horizontal transfer of mRNA. Blood. 2007;110(7):2440–8.

    Article  CAS  PubMed  Google Scholar 

  9. Pap E, Pallinger E, Pasztoi M, et al. Highlights of a new type of intercellular communication: microvesicle-based information transfer. Inflamm Res. 2009;58(1):1–8.

    Article  CAS  PubMed  Google Scholar 

  10. van der Vos KE, Balaj L, Skog J, et al. Brain tumor microvesicles: insights into intercellular communication in the nervous system. Cell Mol Neurobiol. 2011;31(6):949–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Barteneva NS, Maltsev N, Vorobjev IA. Microvesicles and intercellular communication in the context of parasitism. Front Cell Infect Microbiol. 2013;3:49.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Lee Y, El Andaloussi S, Wood MJA. Exosomes and microvesicles: extracellular vesicles for genetic information transfer and gene therapy. Hum Mol Genet. 2012;21(R1):R125–R134134.

    Article  CAS  PubMed  Google Scholar 

  13. Hunter MP, Ismail N, Zhang X, et al. Detection of microRNA expression in human peripheral blood microvesicles. PLoS ONE. 2008;3(11):e3694.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Yang M, Chen J, Su F, et al. Microvesicles secreted by macrophages shuttle invasion-potentiating microRNAs into breast cancer cells. Mol Cancer. 2011;10(1):117.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Castellana D, Toti F, Freyssinet JM. Membrane microvesicles: macromessengers in cancer disease and progression. Thromb Res. 2010;125:S84–S8888.

    Article  PubMed  Google Scholar 

  16. Lee H, Zhang D, Zhu Z, et al. Epithelial cell-derived microvesicles activate macrophages and promote inflammation via microvesicle-containing microRNAs. Sci Rep. 2016;6:35250.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kahlert C, Kalluri R. Exosomes in tumor microenvironment influence cancer progression and metastasis. J Mol Med. 2013;91(4):431–7.

    Article  CAS  PubMed  Google Scholar 

  18. Shiao SL, Chu GCY, Chung LWK. Regulation of prostate cancer progression by the tumor microenvironment. Cancer Lett. 2016;380(1):340–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Milani G, Lana T, Bresolin S, et al. Expression profiling of circulating microvesicles reveals intercellular transmission of oncogenic pathways. Mol Cancer Res. 2017;15(6):683–95.

    Article  CAS  PubMed  Google Scholar 

  20. Kucharzewska P, Christianson HC, Belting M. Global profiling of metabolic adaptation to hypoxic stress in human glioblastoma cells. PLoS ONE. 2015;10(1):e0116740.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Hong BS, Cho JH, Kim H, et al. Colorectal cancer cell-derived microvesicles are enriched in cell cycle-related mRNAs that promote proliferation of endothelial cells. BMC Genomics. 2009;10(1):556.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Davis S, Meltzer PS. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics. 2007;23(14):1846–7.

    Article  PubMed  CAS  Google Scholar 

  24. Barrett T, Edgar R. Gene expression omnibus: microarray data storage, submission, retrieval, and analysis. Methods Enzymol. 2006;411:352–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Barrett T, Troup DB, Wilhite SE, et al. NCBI GEO: mining tens of millions of expression profiles—database and tools update. Nucleic Acids Res. 2006;35(suppl_1):D760–D765765.

    PubMed  PubMed Central  Google Scholar 

  26. Van Deun J, Mestdagh P, Agostinis P, et al. EV-TRACK: transparent reporting and centralizing knowledge in extracellular vesicle research. Nat Methods. 2017;14:228–32.

    Article  PubMed  CAS  Google Scholar 

  27. Yu G, Wang L, Han Y, He Q. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ashburner M, Ball CA, Blake JA, et al. Gene Ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44.

    Article  PubMed  CAS  Google Scholar 

  31. Szklarczyk D, Franceschini A, Wyder S, et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2014;43(D1):D447–D45252.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Aguirre-Gamboa R, Gomez-Rueda H, Martínez-Ledesma E, et al. SurvExpress: an online biomarker validation tool and database for cancer gene expression data using survival analysis. PLoS ONE. 2013;8(9):e74250.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Xia J, Gill EE, Hancock RE. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc. 2015;10(6):823.

    Article  CAS  PubMed  Google Scholar 

  35. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Can Res. 2017;77(21):e108–e110110.

    Article  CAS  Google Scholar 

  36. Barbie DA, Tamayo P, Boehm JS, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature. 2009;462(7269):108–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 2013;14:7.

    Article  Google Scholar 

  38. Charoentong P, Finotello F, Angelova M, et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017;18(1):248–62.

    Article  CAS  PubMed  Google Scholar 

  39. Saftig P, Klumperman J. Lysosome biogenesis and lysosomal membrane proteins: trafficking meets function. Nat Rev Mol Cell Biol. 2009;10(9):623.

    Article  CAS  PubMed  Google Scholar 

  40. Zheng J, Tan J, Miao YY, et al. Extracellular vesicles degradation pathway based autophagy lysosome pathway. Am J Transl Res. 2019;11(3):1170.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Eitan E, Suire C, Zhang S, et al. Impact of lysosome status on extracellular vesicle content and release. Ageing Res Rev. 2016;32:65–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Tancini B, Buratta S, Sagini K, et al. Insight into the role of extracellular vesicles in lysosomal storage disorders. Genes. 2019;10(7):510.

    Article  CAS  PubMed Central  Google Scholar 

  43. Fehrenbacher N, Bastholm L, Kirkegaard-Sørensen T, et al. Sensitization to the lysosomal cell death pathway by oncogene-induced down-regulation of lysosome-associated membrane proteins 1 and 2. Can Res. 2008;68(16):6623–33.

    Article  CAS  Google Scholar 

  44. Wang J, Lee J, Liem D, et al. HSPA5 Gene encoding Hsp70 chaperone BiP in the endoplasmic reticulum. Gene. 2017;618:14–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Mambula SS, Calderwood SK. Heat shock protein 70 is secreted from tumor cells by a nonclassical pathway involving lysosomal endosomes. J Immunol. 2006;177(11):7849–57.

    Article  CAS  PubMed  Google Scholar 

  46. Atay S, Gercel-Taylor C, Kesimer M, et al. Morphologic and proteomic characterization of exosomes released by cultured extravillous trophoblast cells. Exp Cell Res. 2011;317(8):1192–202.

    Article  CAS  PubMed  Google Scholar 

  47. Waisberg J, Viana LDS, Junior RJA, et al. Overexpression of the ITGAV gene is associated with progression and spread of colorectal cancer. Anticancer Res. 2014;34(10):5599–607.

    PubMed  Google Scholar 

  48. Luo Z, Li D, Luo X, et al. Decreased expression of miR-548c-3p in osteosarcoma contributes to cell proliferation via targeting ITGAV. Cancer Biother Radiopharm. 2016;31(5):153–8.

    Article  CAS  PubMed  Google Scholar 

  49. Chen R, Brady E, McIntyre TM. Human TMEM30a promotes uptake of antitumor and bioactive choline phospholipids into mammalian cells. J Immunol. 2011;186(5):3215–25.

    Article  CAS  PubMed  Google Scholar 

  50. Li N, Yang Y, Liang C, et al. Tmem30a plays critical roles in ensuring the survival of hematopoietic cells and leukemia cells in mice. Am J Pathol. 2018;188(6):1457–68.

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

This study was not funded.

Author information

Authors and Affiliations

  1. Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200025, People’s Republic of China

    Qiang Wang & Chaoran Yu

  2. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200025, People’s Republic of China

    Qiang Wang & Chaoran Yu

Authors
  1. Qiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chaoran Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

CY and WQ carried out data analysis; CY and WQ drafted the manuscript; CY and WQ participated in study design, data collection, and analysis; CY and WQ revised the manuscript; all authors read and approved the final manuscript.

Corresponding author

Correspondence to Chaoran Yu.

Ethics declarations

Conflict of interest

Both authors declare no conflict of interest.

Research involving human participants or animals

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

Informed consent

No informed consent was needed in the study.

Availability of data and materials

The datasets supporting the conclusion of this article are included within the article.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Figure 1. Workflow of the integrated bioinformatics analysis across GSE78718, GSE9589 and GSE45301

. DEGs: differentially expressed genes; KEGG: Kyoto Encyclopedia of Genes and Genomes; GO: gene ontology; TIICs: tumor immune infiltrating cells (PDF 82 kb)

Supplementary Figure 2. Immune cells scores between cell lines and corresponding EVs in GSE78718 using ssGSEA

. A total of 28 types immune cells were included in this analysis. Markers: ns: p>0.05; *:p<0.05; **:p<0.01; ***:p<0.001; red: cell line; blue: microvesicles. (PDF 730 kb)

Supplementary Figure 3. Immune cells scores between cell lines and corresponding EVs in GSE9589

. A total of 28 types immune cells were included in this analysis. Markers: ns: p>0.05; *:p<0.05; **:p<0.01; ***:p<0.001; red: cell line; blue: microvesicles. (PDF 666 kb)

Supplementary Figure 4. Immune cells scores between cell lines and corresponding EVs in GSE4530

. A total of 28 types immune cells (one type was not identified due to the heterogeneity of gene profile) were included in this analysis. Markers: ns: p>0.05; *:p<0.05; **:p<0.01; ***:p<0.001; red: cell line; blue: exosomes. (PDF 651 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Q., Yu, C. Identification of biomarkers associated with extracellular vesicles based on an integrative pan-cancer bioinformatics analysis. Med Oncol 37, 79 (2020). https://doi.org/10.1007/s12032-020-01404-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12032-020-01404-7

Keywords

Search

Navigation