Skip to main content

Advertisement

SpringerLink
Log in

Dynamic network biomarker to determine the critical point of breast cancer stage progression

  • Original Article
  • Published:
Breast Cancer Aims and scope Submit manuscript

Abstract

Background

The discovery of early warning signs and biomarkers in patients with early breast cancer is crucial for the prevention and treatment of breast cancer. Dynamic Network Biomarker (DNB) is an approach based on nonlinear dynamics theory, which we exploited to identify a set of DNB members and their key genes as early warning signals during breast cancer staging progression.

Methods

First, based on the gene expression profile of breast cancer in the TCGA database, the DNB algorithm was used to calculate the composite index (CI) of each gene cluster in the process of breast cancer anatomical staging. Then we calculated gene modules associated with the clinical phenotype stage based on weighted gene co-expression network analysis (WGCNA), combined with DNB membership to identify key genes in the network.

Results

We identified a set of gene clusters with the highest CI in Stage II as DNBs, whose roles in related pathways indicate the emergence of a tipping point and impact on breast cancer development. In addition, analysis of the key gene GPRIN1 showed that high expression of GPRIN1 predicts poor prognosis, and related immune analysis showed that GPRIN1 is involved in the development of breast cancer through immune aspects.

Conclusion

The discovery of DNBs and the key gene GPRIN1 can provide potential biomarkers and therapeutic targets for breast cancer.

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

Similar content being viewed by others

Data availability

All datasets analyzed in this study can be found in the TCGA database (https://portal.gdc.cancer.gov/), UCSC Xena database (http://xena.ucsc.edu/), GEO database (https://www.ncbi.nlm.nih.gov/gds), GTEx database (https://www.gtexportal.org) and TIMER2.0 database (http://timer.cistrome.org/).

Abbreviations

DNB:

Dynamic network biomarker

TCGA:

The Cancer Genome Atlas

GEO:

Gene Expression Omnibus

WGCNA:

Weighted gene co-expression network analysis

CI:

Composite index

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

FDR:

False discovery rate

GSEA:

Gene Set Enrichment Analysis

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 Countries. CA Cancer J Clin. 2021;71:209–49.

    Article  PubMed  Google Scholar 

  2. Gradishar WJ, Moran MS, Abraham J, Aft R, Agnese D, Allison KH, et al. NCCN Guidelines(R) Insights: Breast Cancer, Version 4.2021. J Natl Compr Canc Netw. 2021;19:484–93.

    Article  PubMed  Google Scholar 

  3. Li G, Hu J, Hu G. Biomarker studies in early detection and prognosis of breast cancer. Adv Exp Med Biol. 2017;1026:27–39.

    Article  CAS  PubMed  Google Scholar 

  4. Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA. 2003;100:8418–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Hall JM, Lee MK, Newman B, Morrow JE, Anderson LA, Huey B, et al. Linkage of early-onset familial breast cancer to chromosome 17q21. Science (New York, NY). 1990;250:1684–9.

    Article  CAS  Google Scholar 

  6. Marsh DJ, Kum JB, Lunetta KL, Bennett MJ, Gorlin RJ, Ahmed SF, et al. PTEN mutation spectrum and genotype-phenotype correlations in Bannayan-Riley-Ruvalcaba syndrome suggest a single entity with Cowden syndrome. Hum Mol Genet. 1999;8:1461–72.

    Article  CAS  PubMed  Google Scholar 

  7. Liu X, Wang Y, Ji H, Aihara K, Chen L. Personalized characterization of diseases using sample-specific networks. Nucleic Acids Res. 2016;44: e164.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Zhang W, Zeng T, Liu X, Chen L. Diagnosing phenotypes of single-sample individuals by edge biomarkers. J Mol Cell Biol. 2015;7:231–41.

    Article  CAS  PubMed  Google Scholar 

  9. Liu X, Liu ZP, Zhao XM, Chen L. Identifying disease genes and module biomarkers by differential interactions. J Am Med Inform Assoc. 2012;19:241–8.

    Article  PubMed  Google Scholar 

  10. Chen L, Liu R, Liu ZP, Li M, Aihara K. Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Sci Rep. 2012;2:342.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Aihara K, Liu R, Koizumi K, Liu X, Chen L. Dynamical network biomarkers: theory and applications. Gene. 2022;808: 145997.

    Article  CAS  PubMed  Google Scholar 

  12. Chen P, Liu R, Chen L, Aihara K. Identifying critical differentiation state of MCF-7 cells for breast cancer by dynamical network biomarkers. Front Genet. 2015;6:252.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Yang B, Li M, Tang W, Liu W, Zhang S, Chen L, et al. Dynamic network biomarker indicates pulmonary metastasis at the tipping point of hepatocellular carcinoma. Nat Commun. 2018;9:678.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Tong Y, Song Y, Xia C, Deng S. Theoretical and in silico Analyses Reveal MYC as a Dynamic Network Biomarker in Colon and Rectal Cancer. Front Genet. 2020;11: 555540.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Chen S, Li D, Yu D, Li M, Ye L, Jiang Y, et al. Determination of tipping point in course of PM2.5 organic extracts-induced malignant transformation by dynamic network biomarkers. J Hazard Mater. 2022;426:128089.

    Article  CAS  PubMed  Google Scholar 

  16. Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2016;44:e71.

    Article  PubMed  Google Scholar 

  17. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Kassambara A, Mundt F. Factoextra: extract and visualize the results of multivariate data analyses. R package version. 2017;1:337–54.

    Google Scholar 

  19. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2:100141.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Consortium GT. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45:580–5.

    Article  Google Scholar 

  24. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12:453–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zeng Q, Michael IP, Zhang P, Saghafinia S, Knott G, Jiao W, et al. Synaptic proximity enables NMDAR signalling to promote brain metastasis. Nature. 2019;573:526–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48:W509–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Giaquinto AN, Sung H, Miller KD, Kramer JL, Newman LA, Minihan A, et al. Breast Cancer Statistics, 2022. Cancer J Clin. 2022;72:524–41.

    Article  Google Scholar 

  28. Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer. 2012;12:252–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zhang H, Kong Q, Wang J, Jiang Y, Hua H. Complex roles of cAMP-PKA-CREB signaling in cancer. Exp Hematol Oncol. 2020;9:32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Manerba M, Govoni M, Manet I, Leale A, Comparone A, Di Stefano G. Metabolic activation triggered by cAMP in MCF-7 cells generates lethal vulnerability to combined oxamate/etomoxir. Biochim Biophys Acta Gen Subj. 2019;1863:1177–86.

    Article  CAS  PubMed  Google Scholar 

  31. Pujol CN, Dupuy V, Seveno M, Runtz L, Bockaert J, Marin P, et al. Dynamic interactions of the 5-HT6 receptor with protein partners control dendritic tree morphogenesis. Sci Signal. 2020. https://doi.org/10.1126/scisignal.aax9520.

    Article  PubMed  Google Scholar 

  32. Zhou Q, Li D, Zheng H, He Z, Qian F, Wu X, et al. A novel lncRNA-miRNA-mRNA competing endogenous RNA regulatory network in lung adenocarcinoma and kidney renal papillary cell carcinoma. Thorac Cancer. 2021;12:2526–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Li J, Gong Z, Jiang H, Gao J, Liang J, Chang P, et al. Comprehensive analysis reveals GPRIN1 is a potential biomarker for non-sm all cell lung cancer. Curr Bioinform. 2021;16:130–8.

    Article  CAS  Google Scholar 

  34. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144:646–74.

    Article  CAS  PubMed  Google Scholar 

  35. Schreiber RD, Old LJ, Smyth MJ. Cancer immunoediting: integrating immunity’s roles in cancer suppression and promotion. Science. 2011;331:1565–70.

    Article  CAS  PubMed  Google Scholar 

  36. Sharma P, Allison JP. The future of immune checkpoint therapy. Science. 2015;348:56–61.

    Article  CAS  PubMed  Google Scholar 

  37. Wang X. Role of clinical bioinformatics in the development of network-based Biomarkers. J Clin Bioinform. 2011;1:28.

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (No. 31870932).

Author information

Authors and Affiliations

  1. College of Biomedical Engineering, Taiyuan University of Technology, Jinzhong, 030600, China

    Fa Jiang & Xiong Jiao

  2. College of Information and Computer, Taiyuan University of Technology, Jinzhong, 030600, China

    Lifeng Yang

Authors
  1. Fa Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lifeng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiong Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study’s conception and design. Material preparation, data collection and analysis were performed by FJ. The first draft of the manuscript was written by FJ and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Xiong Jiao.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest.

Ethical approval

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

Informed consent

Not applicable.

Consent for publication

All the authors approve the publication of this manuscript.

Additional information

Publisher's Note

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

Supplementary Information

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, F., Yang, L. & Jiao, X. Dynamic network biomarker to determine the critical point of breast cancer stage progression. Breast Cancer 30, 453–465 (2023). https://doi.org/10.1007/s12282-023-01438-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12282-023-01438-5

Keywords

Search

Navigation