Skip to main content
Download PDF

Preoperative platelet distribution width predicts bone metastasis in patients with breast cancer

BMC Cancer volume 24, Article number: 1066 (2024) Cite this article

Abstract

Purpose

Bone metastases occur in 50-70% of patients with breast cancer (BC) and result in high mortality. Platelet distribution width (PDW), a commonly used parameter of activated platelets, has been associated with a poor prognosis in BC. We aim to investigate the prognostic role of PDW for bone metastasis in BC patients.

Methods

515 patients who received BC surgery in the Harbin Medical University Cancer Hospital from July 1, 2016, to December 31, 2017, were reviewed. Patients’ characteristics and platelet indices upon enrollment in this study were collected. The Kaplan-Meier method was used to estimate the 5-year bone metastasis incidence. The univariate and multivariate Cox regression analyses were utilized to identify risk factors associated with bone metastasis.

Results

The patients with bone metastases exhibited lower PDW levels than the patients without bone metastases. Moreover, decreased PDW was significantly correlated with histologic type, multifocal disease, and lymph node status. In addition, the patients with reduced PDW levels were more likely to develop bone metastasis. Multivariate analysis showed that PDW was an independent predictor for bone metastasis.

Conclusion

PDW is an independent predictor of bone metastasis in BC. Further research is warranted.

Peer Review reports

Introduction

The most prevalent form of female malignant tumor is breast cancer (BC) [1, 2]. Despite the advancement of treatment options, BC still accounts for the majority of cancer-related deaths in females. Distant metastases are the primary cause of BC fatalities [3]. In between 50% and 70% of BC patients, metastasis has been observed to most frequently occur in the bone [4]. Patients with bone metastasis are accompanied by excessive, osteoclast-mediated bone destruction and have an overall 5-year survival rate of 22.8% [5, 6]. Therefore, the identification of predictive markers for bone metastasis is urgently needed.

Numerous studies have shown how important platelet activation and interactions with cancer cells are for metastasis. A worse prognosis is associated with thrombocytosis in several malignancies, including ovarian, pancreatic, colorectal, and endometrial cancer [7,8,9,10,11]. A normal platelet count, however, may mask the existence of highly hypercoagulative and pro-inflammatory cancer phenotypes due to the availability of effective compensatory mechanisms [12].

Commonly used parameters of platelet activation include mean platelet volume (MPV) and platelet distribution width (PDW) in clinical practice. MPV reflects platelet size, and PDW indicates variation in platelet size. There have been reports of altered MPV levels in breast, lung, stomach, colon, and ovarian cancer [13,14,15,16]. Moreover, higher PDW levels are associated with poor prognosis in a number of tumor types, such as melanoma, laryngeal cancer, BC, non-small cell lung cancer, gastric cancer, and hepatocellular carcinoma [13,14,15,16,17]. Previous research from our group has established associations between platelet indices and overall survival in BC patients [18, 19]. Moreover, a recent study revealed that cancer cells are reprogrammed to a metastatic state through the acquisition of platelet mitochondria [20]. Blockade of platelet cysteinyl leukotriene receptor 1 counteracts platelet protumoral action and inhibits metastasis of cancer cells to the bone in BC [21]. Nevertheless, applying PDW to predict bone metastasis has not been investigated. In this study, we aim to examine the predictive role of PDW for bone metastasis in BC patients.

Methods

Study population

515 consecutive female patients with BC at Harbin Medical University Cancer Hospital from January 1, 2016, to December 31, 2017, were reviewed in this study. The eligibility criteria were as follows: (1) age at diagnosis > 18 years old; (2) all patients had a post-operative pathological diagnosis of BC; (3) no distant metastasis before surgery; and (4) complete clinical and follow-up information. The exclusion criteria were: (1) a history of antitumor treatments; (2) a history of malignancy; (3) insufficient chest computed tomography (CT) images; and (4) failure to follow up. Bone metastases are first defined by emission-computed tomography scans and then confirmed by CT scans. Bone metastasis-free survival was defined as the time interval from surgery to bone metastasis or to the last follow-up visit. The last follow-up time was December 31, 2022.

We collected the following information from the hospital information system: age, menstrual status, tumor size, lymph node metastasis, histopathological type, proliferation index expression, lymphovascular invasion, molecular classification, clinical stage, and postoperative treatment. The blood testing was performed one week before surgery. White blood cell, hemoglobin, and platelet indices were detected using an autoanalyzer (Sysmex XE-2100, Kobe, Japan). The inter- and intra-assay coefficients of variation of all these assays were below 5%. Estrogen receptor (ER) and progesterone receptor (PR) status were defined based on immunohistochemistry (IHC) results. Human epidermal growth factor receptor-2 (HER-2) positivity was defined as IHC 3 + or fluorescence in situ hybridization (FISH) positive of the primary tumor.

This study was approved by the ethics committees of the Harbin Medical University Cancer Hospital (KY2022-10).

Statistical analysis

Continuous variables were presented as mean ± standard deviation (SD), and categorical variables were expressed as percentages of the number. Continuous variables with normal distribution were compared by the Student’s t test, and categorical variables were compared by the Chi-square test. Bone metastasis incidence curves were drawn by the Kaplan-Meier method and compared using the log-rank test. Univariate and multivariate Cox proportional hazard regression models were used to examine the potential predictors of bone metastasis. Variables associated with p < 0.05 in the univariate analysis were included in the multivariate Cox regression analysis. The optimal cutoff value of PDW was defined by analyzing the receiver operating characteristic curve in terms of bone metastasis incidence after surgery. The statistics were analyzed using SPSS 26.0 (SPSS Inc., Chicago, IL, USA) and MedCalc 15.0. All analyses were two-sided, and p < 0.05 was considered significant.

Results

A total of 515 BC patients who underwent complete surgical resection were included in this study. The median age was 46 years (range from 27 to 73 years). 239 participants had no lymph node metastasis, whereas 276 people were present. 432 (83.9%) and 83 (16.1%) patients were classified as stages I-II, and III, respectively.

The clinicopathological characteristics between bone metastasis and non-bone metastasis groups are summarized in Tables 1 and 2. Platelet count, mean platelet volume, PDW, hemoglobin, multifocal disease, tumor size, lymph node status, PR status, histologic type, clinical stage, and adjuvant hormonal therapy were significantly associated with bone metastasis. No significant associations were found between bone metastasis and other clinical features.

Table 1 Baseline characteristics of BC patients according to bone metastasis status
Full size table
Table 2 Baseline characteristics of BC patients according to bone metastasis status
Full size table

The ROC curve was used to calculate the AUC and evaluate the predictive ability of PDW for bone metastasis. The AUC for predicting bone metastasis by preoperative PDW was 0.650 (0.607–0.692), the best cut-off value was 15.0, the sensitivity was 67.6%, and the specificity was 59.3% (Fig. 1). The patients were divided into two groups according to the optimal cut-off value of PDW. 286 cases (55.5%) had PDW > 15.0%, and 229 (44.5%) had PDW ≤ 15.0%.

Fig. 1
figure 1

An optimized cut-off value was determined for PDW using ROC curve analysis

Full size image

Table 3 summarizes the relationships between PDW and patients’ clinicopathological characteristics. There was a strong correlation between PDW and histologic type, multifocal disease, and lymph node status. Nevertheless, no significant correlations between PDW and other clinical characteristics were found.

Table 3 Baseline clinico-pathological parameters of BC patients according to PDW levels
Full size table

The median follow-up time was 65 months (interquartile range, 62–69 months). There were a total of 74 events of bone metastasis that occurred during the follow-up period. Patients with lower PDW had a greater risk of bone metastasis incidence than those with higher PDW (21.8% vs. 8.4%, respectively; p < 0.001). The Kaplan-Meier curve of PDW identified a significant difference between PDW > 15.0 and ≤ 15.0 in BC (Fig. 2).

Fig. 2
figure 2

Incidence of bone metastasis based on PDW levels

Full size image

Cox univariate and multivariate regression analyses were performed to identify the predictors for bone metastasis in BC patients. On univariate analysis, age, platelet count, mean platelet volume, PDW, multifocal disease, tumor size, lymph node status, PR status, histologic type, clinical stage, and adjuvant hormonal therapy were associated with bone metastasis. On multivariate analysis, age, PDW, multifocal disease, tumor size, histologic type, and adjuvant hormonal therapy were the independent predictors for bone metastasis (Table 4). Patients with reduced PDW had a hazard ratio (HR) of 0.835 (95% CI: 0.745–0.937, p = 0.002) for bone metastasis.

Table 4 The predictors of bone metastases in patients with breast cancer
Full size table

Discussion

Our study found that patients with lower PDW were more likely to develop bone metastasis. Moreover, PDW was significantly correlated with histologic type, multifocal disease, and lymph node status. Multivariate Cox regression revealed that PDW was an independent predictor for bone metastasis.

A growing body of literature recognizes the importance of activated platelets in tumor growth and metastasis [22]. Platelets play pivotal roles in cancer progression via direct interactions with cancer cells and indirect interactions mediated by platelet releasates [23]. Platelets can promote the endothelial arrest of tumor cells by directly bridging the endothelium with circulating cancer cells [24]. The interaction between GPIb-IX-V receptors on platelets and von Willebrand factor exposed to vascular endothelium is crucial to this process [25]. Platelets also release chemokine CXC motif ligand 5 (CXCL5), CXCL7, and lysophosphatidic acid to directly recruit granulocytes that promote the transendothelial migration of tumor cells [26, 27]. Moreover, in a BC mouse model with bone metastasis, platelets were observed to secrete lysophosphatidic acid to induce metastatic foci formation [28]. PDW reveals variations in platelet size and indicates platelet activation. It is well known that malignant tumors are accompanied by an inflammatory response throughout the body. Numerous inflammatory cytokines can promote the proliferation of macrophages, further result in platelet activation, and enhance the release of larger platelets [29]. Activated platelets can coat circulating tumor cells, and tumor cells can escape from shear-induced damage, which facilitates and accelerates tumor colonization, tumor growth, angiogenesis, and metastasis [30].

It is unknown how PDW contributes to the pathophysiology of bone metastases. Instead of bone resorption, BC cells’ overexpression of osteoclasts disrupts the dynamic balance between osteoclasts and osteoblasts, leading to BC bone metastases [31]. The epithelial-to-mesenchymal transition (EMT) and bone metastases in BC are facilitated by the transforming growth factor-β (TGF-β) and WNT signaling pathways [32]. Previous studies have demonstrated that the TGF-β1/Smad pathway in cancer cells is activated in a synergistic manner by both platelet-derived TGF-β1 and direct platelet-tumor cell interaction [33]. According to another report, the direct interaction between platelets and BC cells leads to WNT-β-catenin activation and promotes metastasis [34]. Furthermore, TGF-β1 autocrine and BC cell metastasis are accelerated by activated WNT-β-catenin [34].

An increased PDW level indicates a large disparity in platelet volume and can be a sign of activated platelet production. Baseline PDW reflects accelerated platelet turnover and will reduce after treatment in diseases such as sepsis, deep venous thrombosis, atrial fibrillation, and acute myocardial infarction [35,36,37,38,39]. In patients with lung cancer, breast cancer, and colorectal cancer, antitumor therapy is associated with a decrease in PDW levels [40,41,42]. However, changes in PDW before and after treatment had no effect on progression-free survival or overall survival in cervical cancer, lung cancer, breast cancer, or colorectal cancer [40,41,42,43]. To fully understand the impact of various treatment modalities on the kinetics of platelet indices, more research is required.

Our findings might be valuable in the prevention of bone metastasis in BC patients. Patients with reduced PDW may need closer follow-up and more active therapy. PDW may be a useful predictive parameter to identify patients at higher risk for bone metastasis.

PDW is a simple and cheap laboratory parameter and is easy to use in daily practice. Our research has laid a preliminary foundation for further investigation of activated platelets in the occurrence of bone metastasis. However, our study has some limitations. First of all, this was a retrospective study at a single center. Second, the potential mechanistic role of PDW was not investigated in this study. Third, we are not able to extrapolate the results to different ethnic groups because only Chinese participants were included in this study.

In summary, PDW is an independent predictor for bone metastasis in BC. Our findings underscore the importance of PDW in the mechanism of bone metastasis in BC patients.

Data availability

The data are available from the corresponding author upon request.

References

  1. Napolitano LM. Sepsis 2018: definitions and Guideline Changes. Surg Infect. 2018;19(2):117–25.

    Article  Google Scholar 

  2. Acosta CD, Bhattacharya S, Tuffnell D, Kurinczuk JJ, Knight M. Maternal sepsis: a Scottish population-based case-control study. BJOG: Int J Obstet Gynecol. 2012;119(4):474–83.

    Article  CAS  Google Scholar 

  3. Jin X, Mu P. Targeting breast Cancer metastasis. Breast cancer: Basic Clin Res. 2015;9(Suppl 1):23–34.

    Google Scholar 

  4. Yardley DA. Pharmacologic management of bone-related complications and bone metastases in postmenopausal women with hormone receptor-positive breast cancer. Breast cancer (Dove Med Press). 2016;8:73–82.

    CAS  PubMed  Google Scholar 

  5. Haider MT, Ridlmaier N, Smit DJ, Taipaleenmäki H. Interleukins as mediators of the Tumor cell-bone cell crosstalk during the initiation of breast Cancer bone metastasis. Int J Mol Sci 2021, 22(6).

  6. Xiong Z, Deng G, Huang X, Li X, Xie X, Wang J, Shuang Z, Wang X. Bone metastasis pattern in initial metastatic breast cancer: a population-based study. Cancer Manage Res. 2018;10:287–95.

    Article  CAS  Google Scholar 

  7. Williams MD, Braun LA, Cooper LM, Johnston J, Weiss RV, Qualy RL, Linde-Zwirble W. Hospitalized cancer patients with severe sepsis: analysis of incidence, mortality, and associated costs of care. Crit Care (London England). 2004;8(5):R291–298.

    Article  Google Scholar 

  8. Yan S, Zhang P, Xu W, Liu Y, Wang B, Jiang T, Hua C, Wang X, Xu D, Sun B. Serum Uric Acid Increases Risk of Cancer Incidence and Mortality: A Systematic Review and Meta-Analysis. Mediators of inflammation 2015, 2015:764250.

  9. Lin H, Lin HX, Ge N, Wang HZ, Sun R, Hu WH. Plasma uric acid and tumor volume are highly predictive of outcome in nasopharyngeal carcinoma patients receiving intensity modulated radiotherapy. Radiation Oncol (London England). 2013;8:121.

    Article  CAS  Google Scholar 

  10. Stotz M, Szkandera J, Seidel J, Stojakovic T, Samonigg H, Reitz D, Gary T, Kornprat P, Schaberl-Moser R, Hoefler G, et al. Evaluation of uric acid as a prognostic blood-based marker in a large cohort of pancreatic cancer patients. PLoS ONE. 2014;9(8):e104730.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Tanriverdi O, Cokmert S, Oktay E, Pilanci KN, Menekse S, Kocar M, Sen CA, Avci N, Akman T, Ordu C et al. Prognostic significance of the baseline serum uric acid level in non-small cell lung cancer patients treated with first-line chemotherapy: a study of the Turkish Descriptive Oncological Researches Group. Medical oncology (Northwood, London, England) 2014, 31(10):217.

  12. Abou-Mourad NN, Chamberlain BE, Ackerman NB. Poor prognosis of patients with intra-abdominal sepsis and hypouricemia. Surg Gynecol Obstet. 1979;148(3):358–60.

    CAS  PubMed  Google Scholar 

  13. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, et al. The Third International Consensus definitions for Sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Dovell F, Boffetta P. Serum uric acid and cancer mortality and incidence: a systematic review and meta-analysis. Eur J cancer Prevention: Official J Eur Cancer Prev Organisation (ECP). 2018;27(4):399–405.

    Article  CAS  Google Scholar 

  15. Fini MA, Elias A, Johnson RJ, Wright RM. Contribution of uric acid to cancer risk, recurrence, and mortality. Clin Translational Med. 2012;1(1):16.

    Article  Google Scholar 

  16. Netea MG, Kullberg BJ, Blok WL, Netea RT, van der Meer JW. The role of hyperuricemia in the increased cytokine production after lipopolysaccharide challenge in neutropenic mice. Blood. 1997;89(2):577–82.

    Article  CAS  PubMed  Google Scholar 

  17. Hsieh YP, Chang CC, Yang Y, Wen YK, Chiu PF, Lin CC. The role of uric acid in chronic kidney disease patients. Nephrol (Carlton Vic). 2017;22(6):441–8.

    Article  CAS  Google Scholar 

  18. Li N, Lv XH, Wang X, Wang RT, Huang YX. Preoperative mean platelet volume predicts survival in breast cancer patients with type 2 diabetes. Breast cancer (Tokyo Japan). 2019;26(6):712–8.

    Article  PubMed  Google Scholar 

  19. Huang Y, Cui MM, Huang YX, Fu S, Zhang X, Guo H, Wang RT. Preoperative platelet distribution width predicts breast cancer survival. Cancer Biomark A. 2018;23(2):205–11.

    Article  CAS  Google Scholar 

  20. Zhang W, Zhou H, Li H, Mou H, Yinwang E, Xue Y, Wang S, Zhang Y, Wang Z, Chen T, et al. Cancer cells reprogram to metastatic state through the acquisition of platelet mitochondria. Cell Rep. 2023;42(9):113147.

    Article  CAS  PubMed  Google Scholar 

  21. Saier L, Ribeiro J, Daunizeau T, Houssin A, Ichim G, Barette C, Bouazza L, Peyruchaud O. Blockade of platelet CysLT1R receptor with Zafirlukast counteracts platelet Protumoral Action and prevents breast Cancer metastasis to bone and lung. Int J Mol Sci 2022, 23(20).

  22. Yu L, Guo Y, Chang Z, Zhang D, Zhang S, Pei H, Pang J, Zhao ZJ, Chen Y. Bidirectional Interaction between Cancer cells and platelets provides potential strategies for Cancer therapies. Front Oncol. 2021;11:764119.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lazar S, Goldfinger LE. Platelets and extracellular vesicles and their cross talk with cancer. Blood. 2021;137(23):3192–200.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Jain S, Zuka M, Liu J, Russell S, Dent J, Guerrero JA, Forsyth J, Maruszak B, Gartner TK, Felding-Habermann B, et al. Platelet glycoprotein ib alpha supports experimental lung metastasis. Proc Natl Acad Sci U S A. 2007;104(21):9024–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bauer AT, Suckau J, Frank K, Desch A, Goertz L, Wagner AH, Hecker M, Goerge T, Umansky L, Beckhove P, et al. Von Willebrand factor fibers promote cancer-associated platelet aggregation in malignant melanoma of mice and humans. Blood. 2015;125(20):3153–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Takagi S, Sasaki Y, Koike S, Takemoto A, Seto Y, Haraguchi M, Ukaji T, Kawaguchi T, Sugawara M, Saito M, et al. Platelet-derived lysophosphatidic acid mediated LPAR1 activation as a therapeutic target for osteosarcoma metastasis. Oncogene. 2021;40(36):5548–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Labelle M, Begum S, Hynes RO. Platelets guide the formation of early metastatic niches. Proc Natl Acad Sci U S A. 2014;111(30):E3053–3061.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Boucharaba A, Serre CM, Grès S, Saulnier-Blache JS, Bordet JC, Guglielmi J, Clézardin P, Peyruchaud O. Platelet-derived lysophosphatidic acid supports the progression of osteolytic bone metastases in breast cancer. J Clin Invest. 2004;114(12):1714–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kaiser R, Escaig R, Nicolai L. Hemostasis without clot formation: how platelets guard the vasculature in inflammation, infection, and malignancy. Blood. 2023;142(17):1413–25.

    Article  CAS  PubMed  Google Scholar 

  30. Zhang X, Yu S, Li X, Wen X, Liu S, Zu R, Ren H, Li T, Yang C, Luo H. Research progress on the interaction between oxidative stress and platelets: another avenue for cancer? Pharmacol Res. 2023;191:106777.

    Article  CAS  PubMed  Google Scholar 

  31. Song X, Wei C, Li X. The Signaling pathways Associated with breast Cancer bone metastasis. Front Oncol. 2022;12:855609.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Wang L, Zhang K, Feng J, Wang D, Liu J. The progress of platelets in breast Cancer. Cancer Manage Res. 2023;15:811–21.

    Article  CAS  Google Scholar 

  33. Labelle M, Begum S, Hynes RO. Direct signaling between platelets and cancer cells induces an epithelial-mesenchymal-like transition and promotes metastasis. Cancer Cell. 2011;20(5):576–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Zuo XX, Yang Y, Zhang Y, Zhang ZG, Wang XF, Shi YG. Platelets promote breast cancer cell MCF-7 metastasis by direct interaction: surface integrin α2β1-contacting-mediated activation of Wnt-β-catenin pathway. Cell Communication Signaling: CCS. 2019;17(1):142.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Bin Waleed K, Yin X, Yang X, Dai B, Liu Y, Wang Z, Guan X, Gao L, Yves G, Wang L, et al. Short and long-term changes in platelet and inflammatory biomarkers after cryoballoon and radiofrequency ablation. Int J Cardiol. 2019;285:128–32.

    Article  PubMed  Google Scholar 

  36. Sharma DJ, Ganguly S, Batta MR, Paul Majumder A. Utility of platelet indices as a predictive marker in Sepsis: an observational study from North East India. Cureus. 2023;15(4):e38095.

    PubMed  PubMed Central  Google Scholar 

  37. Mangalesh S, Dudani S, Malik A. Platelet indices and their kinetics predict mortality in patients of Sepsis. Indian J Hematol Blood Transfus. 2021;37(4):600–8.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Sevuk U, Altindag R, Bahadir MV, Ay N, Demirtas E, Ayaz F. Value of platelet indices in identifying complete resolution of thrombus in deep venous thrombosis patients. Indian J Hematol Blood Transfus. 2015;31(1):71–6.

    Article  PubMed  Google Scholar 

  39. Yu J, Wang L, Peng Y, Xiong M, Cai X, Luo J, Zhang M. Dynamic monitoring of erythrocyte distribution width (RDW) and platelet distribution width (PDW) in treatment of Acute myocardial infarction. Med Sci Monit. 2017;23:5899–906.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Wang JJ, Wang YL, Ge XX, Xu MD, Chen K, Wu MY, Gong FR, Tao M, Wang WJ, Shou LM, et al. Prognostic values of platelet-Associated indicators in Resectable Lung cancers. Technol Cancer Res Treat. 2019;18:1533033819837261.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lu YJ, Cui MT, Liang ZW, Wang WJ, Jiang M, Xu MD, Wu MY, Shen M, Li W, Gao Y, et al. Prognostic values of platelet-associated indicators in advanced breast cancer. Transl Cancer Res. 2019;8(4):1326–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Qian W, Ge XX, Wu J, Gong FR, Wu MY, Xu MD, Lian L, Wang WJ, Li W, Tao M. Prognostic evaluation of resectable colorectal cancer using platelet-associated indicators. Oncol Lett. 2019;18(1):571–80.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Wang JM, Wang Y, Huang YQ, Wang H, Zhu J, Shi JP, Li YF, Wang JJ, Wang WJ. Prognostic values of platelet-Associated indicators in Resectable Cervical Cancer. Dose Response. 2019;17(3):1559325819874199.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank all the researchers involved in this project.

Funding

This work was supported by the Climbing Program of Harbin Medical University Cancer Hospital (PDTS2024B-01).

Author information

Author notes

  1. Mei-yue Song, Lin Zhao and Wen-juan Huang contributed equally to this work.

Authors and Affiliations

  1. Department of Pathology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, 150086, Heilongjiang, China

    Mei-yue Song

  2. Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, NO.150 Haping ST, Nangang District, Harbin, 150081, Heilongjiang, China

    Lin Zhao, Wen-juan Huang, Ming-ming Cui, Yu-xi Liu, Rui-tao Wang & Xin Zhang

Authors
  1. Mei-yue Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wen-juan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ming-ming Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yu-xi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rui-tao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization and design: MYS, LZ, WJH, RTW; Administrative support and funding acquisition: MYS, LZ, WJH, RTW; Provision of study materials or patients: XZ, MMC, YXL; Collection and assembly of data: XZ, MMC, YXL; Data analysis and interpretation: MYS, LZ, WJH, XZ, MMC, YXL, RTW; Manuscript writing: MYS, LZ, WJH; Final approval of the manuscript: All authors reviewed the manuscript.

Corresponding authors

Correspondence to Rui-tao Wang or Xin Zhang.

Ethics declarations

Ethics approval and consent to participate

This study protocol was approved by the Ethics Committee of the Harbin Medical University Cancer Hospital (KY2022-10). Since it was a retrospective study, informed consent from all participants was exempted by the Ethics Committee of the Harbin Medical University Cancer Hospital.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, My., Zhao, L., Huang, Wj. et al. Preoperative platelet distribution width predicts bone metastasis in patients with breast cancer. BMC Cancer 24, 1066 (2024). https://doi.org/10.1186/s12885-024-12837-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12885-024-12837-y

Keywords

BMC Cancer

ISSN: 1471-2407

Contact us