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A novel immune-related prognostic index for predicting breast cancer overall survival

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Abstract

Purpose

To find immune-related genes with prognostic value in breast cancer, and construct a prognostic risk assessment model to make a more accurate assessment. Moreover, looking for potential immune markers for breast cancer immunotherapy.

Methods

The breast cancer (BC) data were retrieved from The Cancer Genome Atlas (TCGA) database as a training set. Through the Weighted gene co-expression network analysis (WGCNA), Kaplan–Meier (KM) analysis, lasso regression analysis and stepwise backward Cox regression analysis, screening for prognosis-related immune genes, a prognostic index was built, and external validation with two data sets of Gene Expression Omnibus (GEO) database was performed. Transcription factor (TF) regulatory network was constructed to identify key transcription factors that regulate prognostic immune genes. Gene set enrichment analysis (GSEA) was used to explore the signal pathways differences between high and low-risk groups, estimate package and TIMER database were used to evaluate the relationship between risk score and tumor immune microenvironment.

Results

We obtained 10 prognosis-related immune genes, and the index showed accurate prognostic value. We also identified 7 prognostic transcription factors. Multiple signaling pathways that inhibit tumor progression were enriched in the low-risk group, and risk score was significantly negatively related to the degree of immune infiltration and the expression level of immune checkpoint genes.

Conclusion

We successfully constructed an independent prognostic index, which not only has a stronger predictive ability than the tumor pathological stage, but also can reflect the immune infiltration of breast cancer patients.

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Abbreviations

BC:

Breast cancer

TCGA:

The cancer genome atlas

GEO:

Gene expression omnibus

WGCNA:

Weighted gene co-expression network analysis

GSEA:

Gene set enrichment analysis

TME:

Tumor microenvironment

IRGs:

Immune-related genes

TF:

Transcription factor

FPKM:

Fragments per kilobase million

TPM:

Transcripts per million

GS:

Gene significance

MS:

Module significance

KM:

Kaplan–Meier

RS:

Risk score

OS:

Overall survival

HR:

Hazard ratio

References

  1. Bray F, Ferlay J, Soerjomataram I et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. 2018; 68:394–424.

  2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA. 70:7–30.

  3. Yeo SK, Guan JL. Breast Cancer: Multiple Subtypes within a Tumor? Trends Cancer. 2017;3:753–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Emens LA, Ascierto PA, Darcy PK et al. Cancer immunotherapy: opportunities and challenges in the rapidly evolving clinical landscape. European journal of cancer (Oxford, England: 1990) 2017; 81:116–129.

  5. Kalbasi A, Ribas A. Tumour-intrinsic resistance to immune checkpoint blockade. Nat Rev Immunol. 2020;20:25–39.

    Article  CAS  PubMed  Google Scholar 

  6. Tray N, Taff J, Adams S. Therapeutic landscape of metaplastic breast cancer. Cancer Treat Rev. 2019;79:101888.

    Article  CAS  PubMed  Google Scholar 

  7. Emens LA. Breast Cancer Immunotherapy: Facts and Hopes. Clin Cancer Res. 2018;24:511–20.

    Article  CAS  PubMed  Google Scholar 

  8. Mittal S, Brown NJ, Holen I. The breast tumor microenvironment: role in cancer development, progression and response to therapy. Expert Rev Mol Diagn. 2018;18:227–43.

    Article  CAS  PubMed  Google Scholar 

  9. Savas P, Salgado R, Denkert C, et al. Clinical relevance of host immunity in breast cancer: from TILs to the clinic. Nat Rev Clinical Oncol. 2016;13:228–41.

    Article  CAS  Google Scholar 

  10. Ernst B, Anderson KS. Immunotherapy for the treatment of breast cancer. Curr Oncol Rep. 2015;17:5.

    Article  PubMed  CAS  Google Scholar 

  11. Kroemer G, Senovilla L, Galluzzi L, et al. Natural and therapy-induced immunosurveillance in breast cancer. Nat Med. 2015;21:1128–38.

    Article  CAS  PubMed  Google Scholar 

  12. Denkert C, von Minckwitz G, Darb-Esfahani S, et al. Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy. Lancet Oncol. 2018;19:40–50.

    Article  PubMed  Google Scholar 

  13. Burugu S, Asleh-Aburaya K, Nielsen TO. Immune infiltrates in the breast cancer microenvironment: detection, characterization and clinical implication. Breast Cancer. 2017;24:3–15.

    Article  PubMed  Google Scholar 

  14. Stanton SE, Disis ML. Clinical significance of tumor-infiltrating lymphocytes in breast cancer. J Immunotherapy Cancer. 2016;4:59.

    Article  Google Scholar 

  15. Asano Y, Kashiwagi S, Goto W, et al. Tumour-infiltrating CD8 to FOXP3 lymphocyte ratio in predicting treatment responses to neoadjuvant chemotherapy of aggressive breast cancer. Br J Surg. 2016;103:845–54.

    Article  CAS  PubMed  Google Scholar 

  16. Bhattacharya S, Andorf S, Gomes L, et al. ImmPort: disseminating data to the public for the future of immunology. Immunol Res. 2014;58:234–9.

    Article  CAS  PubMed  Google Scholar 

  17. Tang Q, Chen Y, Meyer C, et al. A comprehensive view of nuclear receptor cancer cistromes. Can Res. 2011;71:6940–7.

    Article  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10:1523–1523.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Rizvi AA, Karaesmen E, Morgan M, et al. gwasurvivr: an R package for genome-wide survival analysis. Bioinformatics (Oxford, England). 2019;35:1968–70.

    Article  CAS  Google Scholar 

  21. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:1–22.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Shannon P, Markiel A, Ozier O, 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 

  23. Chin CH, Chen SH, Wu HH, et al. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8(Suppl 4):S11.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Uno H, Cai TX, Tian L, Wei LJ. Evaluating prediction rules for t-year survivors with censored regression models. J Am Stat Assoc. 2007;102:527–37.

    Article  CAS  Google Scholar 

  25. Park SY. Nomogram: an analogue tool to deliver digital knowledge. J Thoracic Cardiovasc Surg. 2018;155:1793.

    Article  Google Scholar 

  26. Subramanian A, Tamayo P, Mootha VK, 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 

  27. Liberzon A, Birger C, Thorvaldsdóttir H, et al. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. 2015;1:417–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Yoshihara K, Shahmoradgoli M, Martínez E, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.

    Article  PubMed  CAS  Google Scholar 

  29. Li T, Fan J, Wang B, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Can Res. 2017;77:e108–10.

    Article  CAS  Google Scholar 

  30. Disis ML, Stanton SE. Immunotherapy in breast cancer: an introduction. Breast (Edinburgh, Scotland). 2018;37:196–9.

    Article  Google Scholar 

  31. Cassetta L, Pollard JW. Repolarizing macrophages improves breast cancer therapy. Cell Res. 2017;27:963–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ban Y, Mai J, Li X, et al. Targeting autocrine CCL5-CCR5 axis reprograms immunosuppressive myeloid cells and reinvigorates antitumor immunity. Can Res. 2017;77:2857–68.

    Article  CAS  Google Scholar 

  33. Dangaj D, Bruand M, Grimm AJ, et al. Cooperation between constitutive and inducible chemokines enables T cell engraftment and immune attack in solid tumors. Cancer Cell. 2019;35(885–900):e810.

    Google Scholar 

  34. Rizeq B, Malki MI. The Role of CCL21/CCR7 chemokine axis in breast cancer progression. cancers (basel). 2020;12:1036.

    Article  CAS  Google Scholar 

  35. Klimczak M, Biecek P, Zylicz A, Zylicz M. Heat shock proteins create a signature to predict the clinical outcome in breast cancer. Sci Rep. 2019;9:7507.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Wu J, Wan F, Sheng H, et al. NR1H3 expression is a prognostic factor of overall survival for patients with muscle-invasive bladder cancer. J Cancer. 2017;8:852–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Yen MC, Huang YC, Kan JY, et al. S100B expression in breast cancer as a predictive marker for cancer metastasis. Int J Oncol. 2018;52:433–40.

    CAS  PubMed  Google Scholar 

  38. Charmsaz S, Hughes É, Bane FT, et al. S100β as a serum marker in endocrine resistant breast cancer. BMC Med. 2017;15:79.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Saha SK, Yin Y, Chae HS, Cho SG. Opposing Regulation of cancer properties via KRT19-Mediated Differential Modulation Of Wnt/β-catenin/notch signaling in breast and colon cancers. Cancers (Basel) 2019; 11.

  40. Shao L, Hou W, Scharping NE, et al. IRF1 inhibits antitumor immunity through the upregulation of PD-L1 in the tumor cell. Cancer Immunol Res. 2019;7:1258–66.

    Article  CAS  PubMed  Google Scholar 

  41. Kim GC, Kwon HK, Lee CG, et al. Upregulation of Ets1 expression by NFATc2 and NFKB1/RELA promotes breast cancer cell invasiveness. Oncogenesis. 2018;7:91.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Chen N, Zhao G, Yan X, et al. A novel FLI1 exonic circular RNA promotes metastasis in breast cancer by coordinately regulating TET1 and DNMT1. Genome Biol. 2018;19:218.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Zhao X, Liu J, Ge S, et al. Saikosaponin A inhibits breast cancer by regulating Th1/Th2 balance. Front Pharmacol. 2019;10:624.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Peck AR, Witkiewicz AK, Liu C, et al. Low levels of Stat5a protein in breast cancer are associated with tumor progression and unfavorable clinical outcomes. Breast Cancer Res. 2012;14:R130.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Berraondo P, Sanmamed MF, Ochoa MC, et al. Cytokines in clinical cancer immunotherapy. Br J Cancer. 2019;120:6–15.

    Article  CAS  PubMed  Google Scholar 

  46. Braumüller H, Wieder T, Brenner E, et al. T-helper-1-cell cytokines drive cancer into senescence. Nature. 2013;494:361–5.

    Article  PubMed  CAS  Google Scholar 

  47. Müller-Hermelink N, Braumüller H, Pichler B, et al. TNFR1 signaling and IFN-gamma signaling determine whether T cells induce tumor dormancy or promote multistage carcinogenesis. Cancer Cell. 2008;13:507–18.

    Article  PubMed  CAS  Google Scholar 

  48. Pagès F, Galon J, Dieu-Nosjean MC, et al. Immune infiltration in human tumors: a prognostic factor that should not be ignored. Oncogene. 2010;29:1093–102.

    Article  PubMed  CAS  Google Scholar 

  49. Saleh R, Toor SM, Khalaf S, Elkord E. Breast Cancer Cells and PD-1/PD-L1 Blockade Upregulate the Expression of PD-1, CTLA-4, TIM-3 and LAG-3 Immune Checkpoints in CD4(+) T Cells. Vaccines 2019; 7.

  50. Zhu B, Tse LA, Wang D, et al. Immune gene expression profiling reveals heterogeneity in luminal breast tumors. Breast Cancer Res. 2019;21:147.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors would like to thank the TCGA, GEO, ImmPort, cBioPortal, and Cistrome cancer databases for the availability of the data needed for analyses. This work was supported by grants from National Natural Science Foundation of China (81272372 and 30873044) and by Zhongnan Hospital of Wuhan University Science, Technology and Innovation Seed Fund (znpy2016033). The Project-sponsored by SRF for ROCS, SEM.

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Authors and Affiliations

  1. Department of Labortory Medicine, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, 430071, China

    Xiaosi Yu, Juan Guo, Qian Zhou, Wenjie Huang, Chen Xu & Xinghua Long

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Correspondence to Xinghua Long.

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Yu, X., Guo, J., Zhou, Q. et al. A novel immune-related prognostic index for predicting breast cancer overall survival. Breast Cancer 28, 434–447 (2021). https://doi.org/10.1007/s12282-020-01175-z

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