Skip to main content
Springer Nature Link
Log in

Causal Bayesian gene networks associated with bone, brain and lung metastasis of breast cancer

  • Research Paper
  • Published:
Clinical & Experimental Metastasis Aims and scope Submit manuscript

Abstract

Using a machine learning method, this study aimed to identify unique causal networks of genes associated with bone, brain, and lung metastasis of breast cancer. Bayesian network analysis identified differentially expressed genes in primary breast cancer tissues, in bone, brain, and lung breast cancer metastatic tissues, and the clinicopathological features of patients obtained from the Gene Expression Omnibus microarray datasets. We evaluated the causal Bayesian networks of breast metastasis to distant sites (bone, brain, or lung) by (i) measuring how well the structures of each specific type of breast cancer metastasis fit the data, (ii) comparing the structures with known experimental evidence, and (iii) reporting predictive capabilities of the structures. We report for the first time that the molecular gene signatures are specific to the different types of breast cancer metastasis. Several genes, including CHPF, ARC, ANGPTL4, NR2E1, SH2D1A, CTSW, POLR2J4, SPTLC1, ILK, ALDH3B1, PDE6A, SCTR, ADM, HEY1, KCNF1, and UVRAG, were found to be predictors of the risk for site-specific metastasis of breast cancer. Expression of POLR2JA, SPTLC1, ILK, ALDH3B1, and the estrogen receptor was significantly associated with breast cancer bone metastasis. Expression of PDE6A and NR2E1 was causally linked to breast cancer brain metastasis. Expression of HEY1, KCNF1, UVRAG, and the estrogen and progesterone receptors was strongly associated with breast cancer lung metastasis. The causal Bayesian network structures of these genes identify potential interactions among the genes in distant metastases of breast cancer, including to the bone, brain, and lung, and may serve as target candidates for treatment of breast cancer metastasis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

References

  1. Cosphiadi I, Atmakusumah TD, Siregar NC, Muthalib A, Harahap A, Mansyur M (2018) Bone metastasis in advanced breast cancer: analysis of gene expression microarray. Clin Breast Cancer 18:e1117–e1122. https://doi.org/10.1016/j.clbc.2018.03.001

    Article  CAS  PubMed  Google Scholar 

  2. Jin X, Mu P (2015) Targeting breast cancer. Metastasis Breast Cancer (Auckl) 9:23–34. https://doi.org/10.4137/BCBCR.S25460

    Article  Google Scholar 

  3. Carioli G, Malvezzi M, Rodriguez T, Bertuccio P, Negri E, La Vecchia C (2018) Trends and predictions to 2020 in breast cancer mortality: Americas and Australasia. Breast 37:163–169. https://doi.org/10.1016/j.breast.2017.12.004

    Article  PubMed  Google Scholar 

  4. Waks AG, Winer EP (2019) Breast cancer treatment: a review. JAMA 321:288–300. https://doi.org/10.1001/jama.2018.19323

    Article  CAS  PubMed  Google Scholar 

  5. Patanaphan V, Salazar OM, Risco R (1988) Breast cancer: metastatic patterns and their prognosis. South Med J 81:1109–1112

    Article  CAS  Google Scholar 

  6. Gupta GP, Massague J (2006) Cancer metastasis: building a framework. Cell 127:679–695. https://doi.org/10.1016/j.cell.2006.11.001

    Article  CAS  PubMed  Google Scholar 

  7. Randall RL (2014) A promise to our patients with metastatic bone disease. Ann Surg Oncol 21:4049–4050. https://doi.org/10.1245/s10434-014-4010-1

    Article  PubMed  Google Scholar 

  8. Chavez-MacGregor M, Mittendorf EA, Clarke CA, Lichtensztajn DY, Hunt KK, Giordano SH (2017) Incorporating tumor characteristics to the American Joint Committee on Cancer Breast Cancer Staging System. Oncologist 22:1292–1300. https://doi.org/10.1634/theoncologist.2017-0116

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Bardia A, Mayer IA, Diamond JR, Moroose RL, Isakoff SJ, Starodub AN, Shah NC, O’Shaughnessy J, Kalinsky K, Guarino M, Abramson V, Juric D, Tolaney SM, Berlin J, Messersmith WA, Ocean AJ, Wegener WA, Maliakal P, Sharkey RM, Govindan SV, Goldenberg DM, Vahdat LT (2017) Efficacy and safety of Anti-Trop-2 antibody drug conjugate sacituzumab govitecan (IMMU-132) in heavily pretreated patients with metastatic triple-negative breast cancer. J Clin Oncol 35:2141–2148. https://doi.org/10.1200/JCO.2016.70.8297

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Swain SM, Baselga J, Kim SB, Ro J, Semiglazov V, Campone M, Ciruelos E, Ferrero JM, Schneeweiss A, Heeson S, Clark E, Ross G, Benyunes MC, Cortes J, CLEOPATRA Study Group (2015) Pertuzumab, trastuzumab, and docetaxel in HER2-positive metastatic breast cancer. N Engl J Med 372:724–734. https://doi.org/10.1056/NEJMoa1413513

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Aktas B, Kasimir-Bauer S, Muller V, Janni W, Fehm T, Wallwiener D, Pantel K, Tewes M, CLEOPATRA Study Group (2016) Comparison of the HER2, estrogen and progesterone receptor expression profile of primary tumor, metastases and circulating tumor cells in metastatic breast cancer patients. BMC Cancer 16:522. https://doi.org/10.1186/s12885-016-2587-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Cardoso F, Bedard PL, Winer EP, Pagani O, Senkus-Konefka E, Fallowfield LJ, Kyriakides S, Costa A, Cufer T, Albain KS, Force E-MT (2009) International guidelines for management of metastatic breast cancer: combination vs sequential single-agent chemotherapy. J Natl Cancer Inst 101:1174–1181. https://doi.org/10.1093/jnci/djp235

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Robson M, Im SA, Senkus E, Xu B, Domchek SM, Masuda N, Delaloge S, Li W, Tung N, Armstrong A, Wu W, Goessl C, Runswick S, Conte P (2017) Olaparib for metastatic breast cancer in patients with a germline BRCA mutation. N Engl J Med 377:523–533. https://doi.org/10.1056/NEJMoa1706450

    Article  CAS  PubMed  Google Scholar 

  14. Fribbens C, O’Leary B, Kilburn L, Hrebien S, Garcia-Murillas I, Beaney M, Cristofanilli M, Andre F, Loi S, Loibl S, Jiang J, Bartlett CH, Koehler M, Dowsett M, Bliss JM, Johnston SR, Turner NC (2016) Plasma ESR1 mutations and the treatment of estrogen receptor-positive advanced breast cancer. J Clin Oncol 34:2961–2968. https://doi.org/10.1200/JCO.2016.67.3061

    Article  CAS  PubMed  Google Scholar 

  15. Mittempergher L, Saghatchian M, Wolf DM, Michiels S, Canisius S, Dessen P, Delaloge S, Lazar V, Benz SC, Tursz T, Bernards R, van’t Veer LJ (2013) A gene signature for late distant metastasis in breast cancer identifies a potential mechanism of late recurrences. Mol Oncol 7:987–999. https://doi.org/10.1016/j.molonc.2013.07.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, Talantov D, Timmermans M, Meijer-van Gelder ME, Yu J, Jatkoe T, Berns EM, Atkins D, Foekens JA (2005) Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365:671–679. https://doi.org/10.1016/S0140-6736(05)17947-1

    Article  CAS  PubMed  Google Scholar 

  17. Fazilaty H, Mehdipour P (2014) Genetics of breast cancer bone metastasis: a sequential multistep pattern. Clin Exp Metastasis 31:595–612. https://doi.org/10.1007/s10585-014-9642-9

    Article  CAS  PubMed  Google Scholar 

  18. Su G, Morris JH, Demchak B, Bader GD (2014) Biological network exploration with Cytoscape 3. Curr Protoc Bioinformatics 47:8.13.1-8.13.24. https://doi.org/10.1002/0471250953.bi0813s47

    Article  Google Scholar 

  19. Deo RC, Nallamothu BK (2016) Learning about machine learning: the promise and pitfalls of big data and the electronic health record. Circ Cardiovasc Qual 9:618–620. https://doi.org/10.1161/Circoutcomes.116.003308

    Article  Google Scholar 

  20. Nemzek JA, Hodges AP, He Y (2015) Bayesian network analysis of multi-compartmentalized immune responses in a murine model of sepsis and direct lung injury. BMC Res Notes 8:516. https://doi.org/10.1186/s13104-015-1488-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Park SB, Chung CK, Gonzalez E, Yoo C (2018) Causal inference network of genes related with bone metastasis of breast cancer and osteoblasts using causal Bayesian networks. J Bone Metab 25:251–266. https://doi.org/10.11005/jbm.2018.25.4.251

    Article  PubMed  PubMed Central  Google Scholar 

  22. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A (2013) NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res 41:D991-995. https://doi.org/10.1093/nar/gks1193

    Article  CAS  PubMed  Google Scholar 

  23. Lipton A, Theriault RL, Hortobagyi GN, Simeone J, Knight RD, Mellars K, Reitsma DJ, Heffernan M, Seaman JJ (2000) Pamidronate prevents skeletal complications and is effective palliative treatment in women with breast carcinoma and osteolytic bone metastases: long term follow-up of two randomized, placebo-controlled trials. Cancer 88:1082–1090

    Article  CAS  Google Scholar 

  24. Friedman N, Koller D (2003) Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks. Mach Learn 50:95–125. https://doi.org/10.1023/A:1020249912095

    Article  Google Scholar 

  25. Agostinho NB, Machado KS, Werhli AV (2015) Inference of regulatory networks with a convergence improved MCMC sampler. BMC Bioinformatics 16:306. https://doi.org/10.1186/s12859-015-0734-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Scutari MDJ (2014) Bayesian networks with examples in R. Chapman and Hall, Boca Raton, p 16

    Book  Google Scholar 

  27. Charniak E (1991) Bayesian networks without tears. Ai Mag 12:50–63

    Google Scholar 

  28. Siclari VA, Mohammad KS, Tompkins DR, Davis H, McKenna CR, Peng X, Wessner LL, Niewolna M, Guise TA, Suvannasankha A, Chirgwin JM (2014) Tumor-expressed adrenomedullin accelerates breast cancer bone metastasis. Breast Cancer Res 16:458. https://doi.org/10.1186/s13058-014-0458-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ferrero H, Larrayoz IM, Gil-Bea FJ, Martinez A, Ramirez MJ (2018) Adrenomedullin, a novel target for neurodegenerative diseases. Mol Neurobiol 55:8799–8814. https://doi.org/10.1007/s12035-018-1031-y

    Article  CAS  PubMed  Google Scholar 

  30. Shafik NM, Mohamed DA, Bedder AE, El-Gendy AM (2015) Significance of tissue expression and serum levels of angiopoietin-like protein 4 in breast cancer progression: link to NF-kappaB /P65 activity and pro-inflammatory cytokines. Asian Pac J Cancer Prev 16:8579–8587

    Article  Google Scholar 

  31. Sobhan PK, Funa K (2017) TLX—its emerging role for neurogenesis in health and disease. Mol Neurobiol 54:272–280. https://doi.org/10.1007/s12035-015-9608-1

    Article  CAS  PubMed  Google Scholar 

  32. Lin ML, Patel H, Remenyi J, Banerji CR, Lai CF, Periyasamy M, Lombardo Y, Busonero C, Ottaviani S, Passey A, Quinlan PR, Purdie CA, Jordan LB, Thompson AM, Finn RS, Rueda OM, Caldas C, Gil J, Coombes RC, Fuller-Pace FV, Teschendorff AE, Buluwela L, Ali S (2015) Expression profiling of nuclear receptors in breast cancer identifies TLX as a mediator of growth and invasion in triple-negative breast cancer. Oncotarget 6:21685–21703. https://doi.org/10.18632/oncotarget.3942

    Article  PubMed  PubMed Central  Google Scholar 

  33. Tan GJ, Peng ZK, Lu JP, Tang FQ (2013) Cathepsins mediate tumor metastasis. World J Biol Chem 4:91–101. https://doi.org/10.4331/wjbc.v4.i4.91

    Article  PubMed  PubMed Central  Google Scholar 

  34. Winslow S, Leandersson K, Edsjo A, Larsson C (2015) Prognostic stromal gene signatures in breast cancer. Breast Cancer Res 17:23. https://doi.org/10.1186/s13058-015-0530-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hou XM, Zhang T, Da Z, Wu XA (2019) CHPF promotes lung adenocarcinoma proliferation and anti-apoptosis via the MAPK pathway. Pathol Res Pract. https://doi.org/10.1016/j.prp.2019.02.005

    Article  PubMed  Google Scholar 

  36. Toth C, Meinrath J, Herpel E, Derix J, Fries J, Buettner R, Schirmacher P, Heikaus S (2016) Expression of the apoptosis repressor with caspase recruitment domain (ARC) in liver metastasis of colorectal cancer and its correlation with DNA mismatch repair proteins and p53. J Cancer Res Clin Oncol 142:927–935. https://doi.org/10.1007/s00432-015-2102-3

    Article  CAS  PubMed  Google Scholar 

  37. Gu JX, Zhang X, Miao RC, Xiang XH, Fu YN, Zhang JY, Liu C, Qu K (2019) Six-long non-coding RNA signature predicts recurrence-free survival in hepatocellular carcinoma. World J Gastroenterol 25:220–232. https://doi.org/10.3748/wjg.v25.i2.220

    Article  PubMed  PubMed Central  Google Scholar 

  38. Oloumi A, Maidan M, Lock FE, Tearle H, McKinney S, Muller WJ, Aparicio SA, Dedhar S (2010) Cooperative signaling between Wnt1 and integrin-linked kinase induces accelerated breast tumor development. Breast Cancer Res 12:R38. https://doi.org/10.1186/bcr2592

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Acconcia F, Manavathi B, Mascarenhas J, Talukder AH, Mills G, Kumar R (2006) An inherent role of integrin-linked kinase-estrogen receptor alpha interaction in cell migration. Cancer Res 66:11030–11038. https://doi.org/10.1158/0008-5472.CAN-06-2676

    Article  CAS  PubMed  Google Scholar 

  40. Kang S, Kim B, Kang HS, Jeong G, Bae H, Lee H, Lee S, Kim SJ (2015) SCTR regulates cell cycle-related genes toward anti-proliferation in normal breast cells while having pro-proliferation activity in breast cancer cells. Int J Oncol 47:1923–1931. https://doi.org/10.3892/ijo.2015.3164

    Article  CAS  PubMed  Google Scholar 

  41. Wu T, Li Y, Gong L, Lu JG, Du XL, Zhang WD, He XL, Wang JQ (2012) Multi-step process of human breast carcinogenesis: a role for BRCA1, BECN1, CCND1, PTEN and UVRAG. Mol Med Rep 5:305–312. https://doi.org/10.3892/mmr.2011.634

    Article  CAS  PubMed  Google Scholar 

  42. McFall T, McKnight B, Rosati R, Kim S, Huang Y, Viola-Villegas N, Ratnam M (2018) Progesterone receptor A promotes invasiveness and metastasis of luminal breast cancer by suppressing regulation of critical microRNAs by estrogen. J Biol Chem 293:1163–1177. https://doi.org/10.1074/jbc.M117.812438

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Author notes

  1. Deodutta Roy and Changwon Yoo contributed equally to this work.

Authors and Affiliations

  1. Department of Neurosurgery, Seoul National University Boramae Medical Center, Seoul, Korea

    Sung Bae Park

  2. Department of Surgery, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Korea

    Ki-Tae Hwang

  3. Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Korea

    Chun Kee Chung

  4. Department of Neurosurgery, Clinical Research Institute, Seoul National University Hospital, Seoul, Korea

    Chun Kee Chung

  5. Department of Environmental Health Sciences, Stempel College of Public Health and Social Work, Florida International University, Miami, FL, USA

    Deodutta Roy

  6. Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, 11200 SW 8th Street AHC5, Miami, FL, 33199, USA

    Changwon Yoo

Authors
  1. Sung Bae Park
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ki-Tae Hwang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Chun Kee Chung
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Deodutta Roy
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Changwon Yoo
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

SBP has contribution to the design of the work and acquisition, analysis and interpretation of data. And he has drafted the work. KH has drafted the work. CKC has contribution to the design of the work. DR and CY have contribution to the conception of the work, interpretation of data and revised the draft.

Corresponding authors

Correspondence to Deodutta Roy or Changwon Yoo.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Park, S.B., Hwang, KT., Chung, C.K. et al. Causal Bayesian gene networks associated with bone, brain and lung metastasis of breast cancer. Clin Exp Metastasis 37, 657–674 (2020). https://doi.org/10.1007/s10585-020-10060-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10585-020-10060-0

Keywords