Preoperative u-PAR Gene Expression in Bone Marrow Indicates the Potential Power of Recurrence in Breast Cancer Cases

  • Koshi Mimori
  • Akemi Kataoka
  • Hiroshi Yamaguchi
  • Norikazu Masuda
  • Yoshimasa Kosaka
  • Hideshi Ishii
  • Shinji Ohno
  • Masaki Mori
Translational Research and Biomarkers



The clinical significance of isolated tumor cells (ITC) in peripheral blood (PB) and bone marrow (BM) as predictive markers in the recurrence or metastasis of breast cancer has not yet been determined. In the current study, we focused on the urokinase plasminogen activator receptor (u-PAR) gene as a powerful indicator of the potential to relapse after surgery.

Patients and Methods

We examined CK-7 and CK19 as an ITC marker and u-PAR as a candidate indicator for metastasis in PB and BM from 800 cases of breast cancer by quantitative real-time reverse-transcription polymerase chain reaction (RT-PCR). Serum tumor markers, carcinoembryonic antigen (CEA) and cancer antigen 15-3 (CA15-3), were compared with u-PAR or CK status.


CK7 in PB was positive in 262 cases that showed a poorer disease-free survival (DFS) than 478 CK7(–) cases (P < 0.05). The 153 cases of u-PAR(+) in BM showed significantly poorer DFS and overall survival (OS) than did the 579 cases of u-PAR(–) in BM (P < 0001 and P < 0.0001, respectively). In PB, a significant difference was also observed between 330 cases of u-PAR(+) and 437 cases of u-PAR(–) (P < 0.0001). The hazard ratio (HR) for prediction of recurrence was significantly higher in u-PAR (P < 0.0001; HR 0.0519) than the level of three serum tumor markers.


u-PAR expresses in cancer cells during the dormant phase. The current findings revealed that the expression levels of u-PAR in PB and BM evaluated preoperatively indicate the potential to relapse or metastasize after surgery.


Overall Survival Breast Cancer Case Isolate Tumor Cell Serum Tumor Marker Disseminate Tumor Cell 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the following grant sponsors: CREST, Japan Science and Technology Agency (JST); Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research, grant numbers 17109013, 17591411, 17591413, 18390367, 18590333, 18659384, and 18790964; The Ministry of Education, Culture, Sports, Science, and Technology (MEXT) Grant-in-Aid for Scientific Research on Priority Areas, grant number 18015039; and the Third Term Comprehensive Ten-Year Strategy for Cancer Control, grant number 16271201.


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Copyright information

© Society of Surgical Oncology 2009

Authors and Affiliations

  • Koshi Mimori
    • 1
  • Akemi Kataoka
    • 1
  • Hiroshi Yamaguchi
    • 2
  • Norikazu Masuda
    • 3
  • Yoshimasa Kosaka
    • 1
  • Hideshi Ishii
    • 1
  • Shinji Ohno
    • 2
  • Masaki Mori
    • 1
  1. 1.Department of Surgical Oncology, Medical Institute of BioregulationKyushu UniversityBeppuJapan
  2. 2.Department of Breast OncologyKyushu Cancer CenterFukuokaJapan
  3. 3.Osaka National HospitalOsakaJapan

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