Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Increased platelet distribution width predicts poor prognosis in melanoma patients

Increased platelet distribution width predicts poor prognosis in melanoma patients www.nature.com/scientificreports OPEN Increased platelet distribution width predicts poor prognosis in melanoma patients Received: 16 February 2017 1 2 3 4 1,5 6 1 Na Li , Zhiyong Diao , Xiaoyi Huang , Y e Niu , Tiemin Liu , Zhi-ping Liu , Rui-tao Wang & Accepted: 25 April 2017 Kai-jiang Yu Published: xx xx xxxx Activated platelets promote cancer progression and metastasis. Nevertheless, the prognostic value of platelet indices in melanoma had been rarely reported. The aim of this study was to investigate the predictive significance of platelet indices in melanoma. A total of 220 consecutive patients with melanoma were retrospectively enrolled between January 2009 and December 2009. The relationship between PDW and clinicopathological characteristics were analyzed. Kaplan-Meier method and Cox regression were used to evaluate the prognostic impact of PDW. Of the 220 patients, high platelet distribution width (PDW) levels were observed in 63 (28.6%) patients. Increased PDW was associated with tumor subtype (P < 0.001). Survival curves found that patients with increased PDW had significantly shorter survival time than those with normal PDW (P < 0.001). Cox regression analysis revealed that elevated PDW was an independent prognostic factor for overall survival (hazard ratio, 2.480; 95% confidence interval [CI], 1.386–4.436, P = 0.002). In conclusion, PDW is easily available in routine blood test. Our findings indicated that PDW is an independent predictor and that it may also be a potential parameter for targeted therapy in melanoma. Malignant melanoma is an aggressive form of cancer with an increasing incidence and mortality worldwide. Despite multiple and aggressive therapeutic interventions, some patients still recur ae ft r treatment. Therefore, it is of great importance to look for appropriate and effective prognostic markers in melanoma. Platelets play an essential role in cancer development, progression and metastasis though their direct interac- tion with tumor cell . Platelet actions trigger autocrine and paracrine activation processes that cause phenotypic changes in stromal cells which contribute to the development of cancer . Increased platelets were associated with poor prognosis in patients with a wide spectrum of malignancies, such as pancreatic cancer, gastric cancer, 3–7 colorectal cancer, endometrial cancer, and ovarian cancer . However, platelet count is determined by the balance between the rate of production and consumption of platelets. A normal platelet count could conceal the presence of highly hypercoagulative and pro-inflammatory cancer phenotypes in the presence of efficient compensatory mechanisms . Mean platelet volume (MPV), the most commonly used measure of platelet size, is an index of platelet acti- vation and is available in clinical practice . Platelet distribution width (PDW), another platelet index, indicates variation in platelet size . Altered MPV levels were reported in gastric cancer, ovarian cancer, lung cancer, colon cancer, and breast cancer. However, the clinical implications of PDW have not been well defined. In the current study, therefore, we aimed to evaluate the prognostic roles of MPV and PDW in patients with melanoma. Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang, 150081, China. Department of Plastic Surgery, the First Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, 150001, China. Biotherapy Center, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang, 150081, China. Department of Geriatrics, the Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, 150086, China. Division of Hypothalamic Research, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, 75390, USA. Departments of Internal Medicine and Molecular Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA. Department of Intensive Care Unit, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang, 150081, China. Na Li and Zhiyong Diao contributed equally to this work. Correspondence and requests for materials should be addressed to R.-t.W. (email: ruitaowang@126.com) or K.-j.Y. (email: kaijiang_yu@yeah.net) Scientific Repo R ts | 7: 2970 | DOI:10.1038/s41598-017-03212-y 1 www.nature.com/scientificreports/ Figure 1. Optimized cut-off was determined for PDW using standard ROC curve analysis. Results Between Jan, 2009 and Dec, 2009, a total of 220 patients were enrolled in this study. Among the 220 patients, 116 (52.7%) were women and 104 (47.3%) were men, and the median age was 56.3 ± 12.4 years (range 21–86). In terms of the staging system, 36 cases were categorized as stage I and stage II, 129 as stage III and stage IV. A ROC curve for OS prediction was plotted to verify the optimal cut-off value for PDW, which was 17.2 (Fig. 1). It demonstrated that PDW predicts cancer prognosis with a sensitivity of 51.1% and a specificity of 68.3% (AUC = 0.683, 95% CI: 0.618–0.744, p < 0.0001). Then, patients were divided into 2 groups: patients with PDW ≤ 17.2% and patients with PDW > 17.2%. There were 157 (71.4%) patients with PDW ≤ 17.2% and 63 (28.6%) patients with PDW > 17.2%. e r Th elationships between PDW and clinical characteristics were shown in Tables  1 and 2. Our study revealed that PDW was associated with tumor subtype (P < 0.001). However, no significant differences were observed between the groups with regard to age, gender, tumor location, ulceration, tumor size, lymph node metastasis, distant metastasis, and clinical stage. With a median follow up of 60 months, 47 (21.4%) patients had death events. Patients with PDW ≤ 17.2% had a significantly better 5-year OS than patients with PDW > 17.2% (85.4% vs. 61.9%, P < 0.001). The Kaplan-Meier OS curves of the normal versus elevated PDW showed a significant separation (Fig.  2). In univariate analysis, lymph node metastasis, PDW (categorical variable), albumin, and clinical stage were significant predictors of OS (Table  3). Age (categorical variable) (p = 0.093), lymphocytes (p = 0.071), and tumor subtype (p = 0.075) showed weak associations. Other parameters were not found to be in correlation with OS. Next, all the factors with a P value less than 0.05 in univariate analysis were included in multivariate analysis (Table 4). In multivariate analyses, we demonstrated that PDW was an independent prognostic factor in patients with melanoma. Patients with PDW > 17.2% had a hazard ratio (HR) of 2.480 [95% confidence interval (CI): 1.386–4.436, P = 0.002] for OS. Discussion This study showed that PDW is associated with patient’s survival and is an independent risk factor for prognosis in melanoma. Platelets facilitate cancer progression and metastasis by inducing tumor growth, epithelial-mesenchymal tran- sition, and invasion . An increasingly body of evidence have identified the involvement of activated platelets in melanoma. Platelet-derived growth factor (PDGF) secreted by melanoma cell could stimulate the development of tumor stroma and new blood vessels . Moreover, Boukerche H et al. showed platelet-melanoma cell interaction is mediated by the glycoprotein IIb-IIIa complex . Kolber DL et al. confirmed that recombinant platelet factor 4, a known angiogenesis inhibitor, could effectively suppress tumor-induced neovascularization in mice . In accordance with the studies above, the current study indirectly confirmed the findings using a simple platelet Scientific Repo R ts | 7: 2970 | DOI:10.1038/s41598-017-03212-y 2 www.nature.com/scientificreports/ Variables Total n (%) PDW ≤ 17.2 n (%) PDW > 17.2 n (%) P value Age (years) 0.849  <60 127 (57.7) 90 (57.3) 37 (58.7)  ≥60 93 (42.3) 67 (42.7) 26 (41.3) Gender 0.119  Male 104 (47.3) 69 (43.9) 35 (55.6)  Female 116 (52.7) 88 (56.1) 28 (44.4) Tumor location 0.728  Sun-exposed (head and neck) 22 (10.0) 15 (9.6) 7 (11.1)  Sun-protected (others) 198 (90.0) 142 (90.4) 56 (88.9) Ulceration 0.091  Negative 138 (62.7) 93 (59.2) 45 (71.4)  Positive 82 (37.3) 64 (40.8) 18 (28.6) Tumor Size 0.187  ≥2 mm 127 (57.7) 95 (60.5) 32 (50.8)  <2 mm 93 (42.3) 62 (39.5) 31 (49.2) Tumor subtype <0.001  ALM 89 (40.5) 66 (42.0) 23 (36.5)  SSM 65 (29.5) 45 (28.7) 20 (31.7)  LMM 26 (11.8) 17 (10.8) 9 (14.3)  NM 29 (13.2) 24 (15.3) 5 (7.9)  Others 11 (5.0) 5 (3.2) 6 (9.5) Lymph node metastasis 0.116  Negative 166 (75.0) 123 (78.3) 43 (68.3)  Positive 54 (25.0) 34 (21.7) 20 (31.7) Distant metastasis 0.366  Absent 192 (87.3) 135 (86.0) 57 (90.5)  Present 28 (12.7) 22 (14.0) 6 (9.5) Clinical stage 0.192  I/II 157 (71.4) 116 (73.9) 44 (65.1)  III/IV 63 (28.6) 41 (26.1) 22 (34.9) Table 1. Baseline characteristics of melanoma patients according to PDW levels. SSM, superficial spreading melanoma; LMM, lentigo maligna melanoma; ALM, acrolentigous melanoma; NM, nodular melanoma; PDW, platelet distribution width. Variables PDW ≤ 17.2 PDW > 17.2 P value Age (years) 56.2 (12.6) 56.5 (12.2) 0.834 Smoker (n, %) 17 (10.8) 13 (20.6) 0.570 Drinking (n, %) 14 (8.9) 16 (25.4) 0.525 BMI (kg/m ) 24.8(3.4) 23.6 (2.9) 0.007 FPG (mmol/L) 5.10 (4.80–5.62) 5.00 (4.70–5.50) 0.444 Albumin (g/L) 43.3 (4.3) 44.6 (4.5) 0.047 WBC (×10 /L) 6.44 (2.05) 5.94 (1.87) 0.095 Neutrophils (×10 /L) 3.84 (1.74) 3.49 (1.65) 0.173 Lymphocytes (×10 /L) 2.07 (1.36) 1.85 (0.58) 0.239 Hemoglobin (g/dl) 138.8 (29.8) 139.3 (17.4) 0.903 Platelet count (×10 /L) 242.2 (67.7) 217.3 (60.5) 0.011 MPV (fL) 8.6 (1.2) 9.2 (1.5) 0.001 NLR 2.12(1.32) 2.15(2.30) 0.904 PLR 133.8 (58.9) 125.4 (46.2) 0.265 Table 2. Baseline characteristics of melanoma patients according to PDW levels. Data are expressed as means (SD) or median (IQR). FPG, fasting plasma glucose; WBC, white blood cell; BMI, body mass index; MPV, mean platelet volume; PDW, platelet distribution width; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to- lymphocyte ratio. Scientific Repo R ts | 7: 2970 | DOI:10.1038/s41598-017-03212-y 3 www.nature.com/scientificreports/ Figure 2. Kaplan–Meier analysis of overall survival in melanoma patients. index. These data are also consistent with the current knowledge that anti-platelet is considered to be a part of cancer adjuvant therapy . In addition, our study can form the basis for further mechanistic studies and ultimately aid in patient-tailored selection of therapeutic strategies. e m Th echanisms to explain the association between PDW and survival are poorly understood. Bone marrow cells (including megakaryocytes) dys-function may contribute to altered PDW. PDW is a measure of platelet heterogeneity caused by heterogeneous demarcation of megakarocytes . Recent reports demonstrated several cytokines, such as interleukin-6 (IL-6), granulocytes colony stimulating factor (G-CSF) and macrophage colony stimulating factor (M-CSF), regulate megakaryocytic maturation, platelet production and platelet size . IL-6 pro- motes tumor angiogenesis, metastasis and metabolism . Furthermore, the cytokines G-CSF and M-CSF that be secreted by tumor cells could stimulate megakaryopoiesis and subsequent thrombopoiesis in cancer . However, the clinical value of PDW has not been studied in melanoma. Another possible mechanism is that platelets pro- mote the hypercoagulable state in cancer . Activated platelets create a procoagulant micro-environment that enables the tumor cells to cover themselves with platelets and evade the host immune system . The present study has several limitations. First, this was a single-center retrospective study and additional larger validation studies with multiethnic groups are needed to confirm our results. Second, the mechanisms underlying the involvement of PDW in melanoma remains unclear, to which further investigation should be addressed. Third, the patients were composed of Chinese. The application to other ethnic groups still needs fur - ther investigation. In conclusion, PDW is easily available with routine blood counts. Increased PDW may serve as a marker of adverse prognosis in melanoma. Further studies are warranted to clarify the exact role of PDW in melanoma. Patients and Methods Study population. This study consisted of 220 consecutive melanoma cases (mean age 56.3 ± 17.4 years, range 18–86 years). Cases were admitted to the Third Affiliated Hospital, Harbin Medical University between January 2009 and December 2009. All patients undergone complete surgical resection. The pathologic diagnoses of melanoma were evaluated by pathologists from biopsy reports. None of the patients received preoperative chemotherapy or radiation therapy. Patients were excluded if they had hematological disorders, coronary artery disease, hypertension, diabetes mellitus, and medical treatment with anticoagulant, statins, and acetylic salicylic acid. Standard demographic and clinicopathological data were collected from the patients’ records in hospital. For all the study participants, venous peripheral blood samples were collected at admission. Survival data were obtained through follow-up. Overall survival (OS) was defined as the interval from the date of diagnosis to death or last follow-up. The median follow-up time was 60 months. The platelet-to-lymphocyte ratio (PLR) was calcu- lated as the absolute platelet count measured in ×10 /L divided by the absolute lymphocyte count measured in Scientific Repo R ts | 7: 2970 | DOI:10.1038/s41598-017-03212-y 4 www.nature.com/scientificreports/ Hazard ratio 95% CI P-value Age (years) (≥60 versus <60) 0.591 0.320–1.092 0.093 Gender (male versus female) 0.769 0.433–1.364 0.368 Smoker (yes versus no) 1.060 0.257–4.368 0.936 Drinking (yes versus no) 0.423 0.152–1.180 1.000 BMI (kg/m ) 0.945 0.865–1.033 0.215 FPG (mmol/L) 1.360 0.897–2.063 0.148 Albumin (g/L) 1.075 1.009–1.145 0.026 WBC (×10 /L) 0.894 0.756–1.057 0.189 Neutrophils (×10 /L) 0.931 0.766–1.133 0.477 Lymphocytes (×10 /L) 0.612 0.359–1.042 0.071 NLR 1.105 0.941–1.298 0.224 PLR 1.004 0.978–1.013 0.613 Hemoglobin (g/dl) 0.996 0.983–1.009 0.562 Platelet count (×10 /L) 1.001 0.997–1.005 0.681 MPV (fL) 0.918 0.737–1.143 0.444 PDW (%) (>17.2 versus ≤17.2) 2.857 1.612–5.063 <0.001 Tumor location (Sun-exposed versus 1.721 0.771–3.843 0.185 Sun-protected) Ulceration (Yes versus No) 0.675 0.361–1.261 0.218 Tumor subtype (SSM + NM versus 0.592 0.332–1.055 0.075 ALM + LMM + others) Tumor Size (mm) (≥2.0 versus<2.0) 1.102 0.616–1.974 0.743 Lymph node metastasis (Yes versus No) 2.889 1.625–5.136 <0.001 Distant metastasis (Yes versus No) 1.219 0.546–2.721 0.629 Clinical stage (III/IV versus I/II) 3.386 1.908–6.009 <0.001 Table 3. Univariate analysis of overall survival in melanoma patients. Abbreviations: see to Tables 1 and 2. Hazard ratio 95% CI P-value Albumin (g/L) 1.051 0.985–1.122 0.131 PDW (%) (>17.2 versus ≤17.2) 2.480 1.386–4.436 0.002 Lymph node metastasis (Yes versus No) 0.660 0.223–1.953 0.453 Clinical stage (III/IV versus I/II) 4.311 1.479–12.568 0.007 Table 4. Multivariate analysis of overall survival in melanoma patients. CI, confidence interval. Abbreviations: see to Tables 1 and 2. ×10 /L. e Th neutrophil-to-lymphocyte ratio (NLR) was calculated as the absolute neutrophil count measured in 9 9 ×10 /L divided by the absolute lymphocyte count measured in ×10 /L. rd The Institutional Ethics Review Board of the 3 Affiliated Hospital of Harbin Medical University approved this study prior to commencement of data collection and waived the informed consent requirement because it was a retrospective study. Statistical analysis. All statistical analyses were performed using SPSS Statistics version 22.0 (SPSS Inc., Chicago, IL, USA). The descriptive statistics are presented as means ± SD or medians (interquartile range) for continuous variables and percentages of the number for categorical variables. Inter-group differences in categor - ical variables were assessed for significance using the Chi-square test; differences in continuous variables were assessed using the Mann-Whitney U test or t-test. The optimal cutoff value of PDW was determined by receiver operating characteristic (ROC) curve. We used univariate analysis to narrow down the list of possible prognostic factors. Variables with P value < 0.05 in univariate analysis were brought into multivariate Cox proportional hazard model to determine their independency. Kaplan-Meier curves and log-rank test were used to compare survival differences among groups. All reported p-values are two-sided and statistical significance was assumed as p < 0.05. References 1. Mezouar, S. et al. Role of platelets in cancer and cancer-associated thrombosis: Experimental and clinical evidences. r Th omb. Res. 139, 65–76, doi:10.1016/j.thromres.2016.01.006 (2016). 2. Dovizio, M., Alberti, S., Guillem-Llobat, P. & Patrignani, P. Role of platelets in inflammation and cancer: novel therapeutic strategies. Basic Clin. Pharmacol. Toxicol. 114(1), 118–127, doi:10.1111/bcpt.2013.114.issue-1 (2014). Scientific Repo R ts | 7: 2970 | DOI:10.1038/s41598-017-03212-y 5 www.nature.com/scientificreports/ 3. Suzuki, K. et al. Platelets counts closely correlate with the disease-free survival interval of pancreatic cancer patients. Hepatogastroenterology 51(57), 847–853 (2004). 4. Long, Y, Wang, T, Gao, Q, Zhou, C. Prognostic significance of pretreatment elevated platelet count in patients with colorectal cancer: a meta-analysis. Oncotarget (2016). 5. Pietrzyk, L. et al. Diagnostic Power of Blood Parameters as Screening Markers in Gastric Cancer Patients. Asian Pac. J. Cancer Prev. 17(9), 4433–4437 (2016). 6. Ekici, H. et al. Do Leukocyte and Platelet Counts Have Benet fi for \Preoperative Evaluation of Endometrial Cancer. Asian Pac. J. Cancer Prev. 16(13), 5305–5310, doi:10.7314/APJCP.2015.16.13.5305 (2015). 7. Qiu, J. et al. Preoperative plasma b fi rinogen, platelet count and prognosis in epithelial ovarian cancer. J. Obstet. Gynaecol. Res. 38(4), 651–657, doi:10.1111/j.1447-0756.2011.01780.x (2012). 8. Seretis, C., Youssef, H. & Chapman, M. Hypercoagulation in colorectal cancer: what can platelet indices tell us. Platelets 26(2), 114–118, doi:10.3109/09537104.2014.894969 (2015). 9. Gasparyan, A. Y., Ayvazyan, L., Mikhailidis, D. P. & Kitas, G. D. Mean platelet volume: a link between thrombosis and inflammation. Curr. Pharm. Des. 17(1), 47–58, doi:10.2174/138161211795049804 (2011). 10. Kaito, K. et al. Platelet size deviation width, platelet large cell ratio, and mean platelet volume have sufficient sensitivity and specificity in the diagnosis of immune thrombocytopenia. Br. J. Haematol. 128(5), 698–702, doi:10.1111/j.1365-2141.2004.05357.x (2005). 11. Meikle, C. K. et al. Cancer and Thrombosis: The Platelet Perspective. Front Cell Dev Biol 4, 147, doi:10.3389/fcell.2016.00147 (2016). 12. Barnhill, R. L., Xiao, M., Graves, D. & Antoniades, H. N. Expression of platelet-derived growth factor (PDGF)-A, PDGF-B and the PDGF-alpha receptor, but not the PDGF-beta receptor, in human malignant melanoma in vivo. Br. J. Dermatol. 135(6), 898–904, doi:10.1046/j.1365-2133.1996.d01-1092.x (1996). 13. Boukerche, H. et al. Platelet-melanoma cell interaction is mediated by the glycoprotein IIb-IIIa complex. Blood 74(2), 658–663 (1989). 14. Kolber, D. L., Knisely, T. L. & Maione, T. E. Inhibition of development of murine melanoma lung metastases by systemic administration of recombinant platelet factor 4. J. Natl. Cancer Inst. 87(4), 304–309, doi:10.1093/jnci/87.4.304 (1995). 15. Paulus, J. M. Recent advances in the story of megakaryocyte physiology. Pathol. Biol. 29(3), 133–135 (1981). 16. Kaushansky, K. Growth factors and hematopoietic cell fate. A new feature: controversies in hematology. Blood 92(2), 345–344 (1998). 17. Kumari, N., Dwarakanath, B. S., Das, A., Bhatt, A. N. Role of interleukin-6 in cancer progression and therapeutic resistance. Tumour Biol. (2016). 18. Kowanetz, M. et al. Granulocyte-colony stimulating factor promotes lung metastasis through mobilization of Ly6G+Ly6C+ granulocytes. Proc. Natl. Acad. Sci. USA 107(50), 21248–21255, doi:10.1073/pnas.1015855107 (2010). 19. Li, N. Platelets in cancer metastasis: To help the “villain” to do evil. Int. J. Cancer 138(9), 2078–2087, doi:10.1002/ijc.v138.9 (2016). 20. Franco, A. T., Corken, A. & Ware, J. Platelets at the interface of thrombosis, inflammation, and cancer. Blood 126(5), 582–588, doi:10.1182/blood-2014-08-531582 (2015). Acknowledgements This work was supported financially by grants from the Harbin special fund for scientific and technological innovation talents (RC2016XK004068). Author Contributions N.L. and Z.Y.D. participated in manuscript preparation, data analysis and editing. XY.H., and Y.N. participated in data collection and data analysis. T.M.L. and Z.P.L. participated in study design, and manuscript revision. R.T.W. and K.J.Y. participated in study design, data analysis, and manuscript preparation. All authors read and approved the final manuscript. Additional Information Competing Interests: The authors declare that they have no competing interests. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 Cre- ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per- mitted 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 license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2017 Scientific Repo R ts | 7: 2970 | DOI:10.1038/s41598-017-03212-y 6 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Scientific Reports Springer Journals

Increased platelet distribution width predicts poor prognosis in melanoma patients

Loading next page...
1
 
/lp/springer_journal/increased-platelet-distribution-width-predicts-poor-prognosis-in-yyZ3Ye30V3

References (24)

Publisher
Springer Journals
Copyright
Copyright © 2017 by The Author(s)
Subject
Science, Humanities and Social Sciences, multidisciplinary; Science, Humanities and Social Sciences, multidisciplinary; Science, multidisciplinary
eISSN
2045-2322
DOI
10.1038/s41598-017-03212-y
Publisher site
See Article on Publisher Site

Abstract

www.nature.com/scientificreports OPEN Increased platelet distribution width predicts poor prognosis in melanoma patients Received: 16 February 2017 1 2 3 4 1,5 6 1 Na Li , Zhiyong Diao , Xiaoyi Huang , Y e Niu , Tiemin Liu , Zhi-ping Liu , Rui-tao Wang & Accepted: 25 April 2017 Kai-jiang Yu Published: xx xx xxxx Activated platelets promote cancer progression and metastasis. Nevertheless, the prognostic value of platelet indices in melanoma had been rarely reported. The aim of this study was to investigate the predictive significance of platelet indices in melanoma. A total of 220 consecutive patients with melanoma were retrospectively enrolled between January 2009 and December 2009. The relationship between PDW and clinicopathological characteristics were analyzed. Kaplan-Meier method and Cox regression were used to evaluate the prognostic impact of PDW. Of the 220 patients, high platelet distribution width (PDW) levels were observed in 63 (28.6%) patients. Increased PDW was associated with tumor subtype (P < 0.001). Survival curves found that patients with increased PDW had significantly shorter survival time than those with normal PDW (P < 0.001). Cox regression analysis revealed that elevated PDW was an independent prognostic factor for overall survival (hazard ratio, 2.480; 95% confidence interval [CI], 1.386–4.436, P = 0.002). In conclusion, PDW is easily available in routine blood test. Our findings indicated that PDW is an independent predictor and that it may also be a potential parameter for targeted therapy in melanoma. Malignant melanoma is an aggressive form of cancer with an increasing incidence and mortality worldwide. Despite multiple and aggressive therapeutic interventions, some patients still recur ae ft r treatment. Therefore, it is of great importance to look for appropriate and effective prognostic markers in melanoma. Platelets play an essential role in cancer development, progression and metastasis though their direct interac- tion with tumor cell . Platelet actions trigger autocrine and paracrine activation processes that cause phenotypic changes in stromal cells which contribute to the development of cancer . Increased platelets were associated with poor prognosis in patients with a wide spectrum of malignancies, such as pancreatic cancer, gastric cancer, 3–7 colorectal cancer, endometrial cancer, and ovarian cancer . However, platelet count is determined by the balance between the rate of production and consumption of platelets. A normal platelet count could conceal the presence of highly hypercoagulative and pro-inflammatory cancer phenotypes in the presence of efficient compensatory mechanisms . Mean platelet volume (MPV), the most commonly used measure of platelet size, is an index of platelet acti- vation and is available in clinical practice . Platelet distribution width (PDW), another platelet index, indicates variation in platelet size . Altered MPV levels were reported in gastric cancer, ovarian cancer, lung cancer, colon cancer, and breast cancer. However, the clinical implications of PDW have not been well defined. In the current study, therefore, we aimed to evaluate the prognostic roles of MPV and PDW in patients with melanoma. Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang, 150081, China. Department of Plastic Surgery, the First Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, 150001, China. Biotherapy Center, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang, 150081, China. Department of Geriatrics, the Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, 150086, China. Division of Hypothalamic Research, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, 75390, USA. Departments of Internal Medicine and Molecular Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA. Department of Intensive Care Unit, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang, 150081, China. Na Li and Zhiyong Diao contributed equally to this work. Correspondence and requests for materials should be addressed to R.-t.W. (email: ruitaowang@126.com) or K.-j.Y. (email: kaijiang_yu@yeah.net) Scientific Repo R ts | 7: 2970 | DOI:10.1038/s41598-017-03212-y 1 www.nature.com/scientificreports/ Figure 1. Optimized cut-off was determined for PDW using standard ROC curve analysis. Results Between Jan, 2009 and Dec, 2009, a total of 220 patients were enrolled in this study. Among the 220 patients, 116 (52.7%) were women and 104 (47.3%) were men, and the median age was 56.3 ± 12.4 years (range 21–86). In terms of the staging system, 36 cases were categorized as stage I and stage II, 129 as stage III and stage IV. A ROC curve for OS prediction was plotted to verify the optimal cut-off value for PDW, which was 17.2 (Fig. 1). It demonstrated that PDW predicts cancer prognosis with a sensitivity of 51.1% and a specificity of 68.3% (AUC = 0.683, 95% CI: 0.618–0.744, p < 0.0001). Then, patients were divided into 2 groups: patients with PDW ≤ 17.2% and patients with PDW > 17.2%. There were 157 (71.4%) patients with PDW ≤ 17.2% and 63 (28.6%) patients with PDW > 17.2%. e r Th elationships between PDW and clinical characteristics were shown in Tables  1 and 2. Our study revealed that PDW was associated with tumor subtype (P < 0.001). However, no significant differences were observed between the groups with regard to age, gender, tumor location, ulceration, tumor size, lymph node metastasis, distant metastasis, and clinical stage. With a median follow up of 60 months, 47 (21.4%) patients had death events. Patients with PDW ≤ 17.2% had a significantly better 5-year OS than patients with PDW > 17.2% (85.4% vs. 61.9%, P < 0.001). The Kaplan-Meier OS curves of the normal versus elevated PDW showed a significant separation (Fig.  2). In univariate analysis, lymph node metastasis, PDW (categorical variable), albumin, and clinical stage were significant predictors of OS (Table  3). Age (categorical variable) (p = 0.093), lymphocytes (p = 0.071), and tumor subtype (p = 0.075) showed weak associations. Other parameters were not found to be in correlation with OS. Next, all the factors with a P value less than 0.05 in univariate analysis were included in multivariate analysis (Table 4). In multivariate analyses, we demonstrated that PDW was an independent prognostic factor in patients with melanoma. Patients with PDW > 17.2% had a hazard ratio (HR) of 2.480 [95% confidence interval (CI): 1.386–4.436, P = 0.002] for OS. Discussion This study showed that PDW is associated with patient’s survival and is an independent risk factor for prognosis in melanoma. Platelets facilitate cancer progression and metastasis by inducing tumor growth, epithelial-mesenchymal tran- sition, and invasion . An increasingly body of evidence have identified the involvement of activated platelets in melanoma. Platelet-derived growth factor (PDGF) secreted by melanoma cell could stimulate the development of tumor stroma and new blood vessels . Moreover, Boukerche H et al. showed platelet-melanoma cell interaction is mediated by the glycoprotein IIb-IIIa complex . Kolber DL et al. confirmed that recombinant platelet factor 4, a known angiogenesis inhibitor, could effectively suppress tumor-induced neovascularization in mice . In accordance with the studies above, the current study indirectly confirmed the findings using a simple platelet Scientific Repo R ts | 7: 2970 | DOI:10.1038/s41598-017-03212-y 2 www.nature.com/scientificreports/ Variables Total n (%) PDW ≤ 17.2 n (%) PDW > 17.2 n (%) P value Age (years) 0.849  <60 127 (57.7) 90 (57.3) 37 (58.7)  ≥60 93 (42.3) 67 (42.7) 26 (41.3) Gender 0.119  Male 104 (47.3) 69 (43.9) 35 (55.6)  Female 116 (52.7) 88 (56.1) 28 (44.4) Tumor location 0.728  Sun-exposed (head and neck) 22 (10.0) 15 (9.6) 7 (11.1)  Sun-protected (others) 198 (90.0) 142 (90.4) 56 (88.9) Ulceration 0.091  Negative 138 (62.7) 93 (59.2) 45 (71.4)  Positive 82 (37.3) 64 (40.8) 18 (28.6) Tumor Size 0.187  ≥2 mm 127 (57.7) 95 (60.5) 32 (50.8)  <2 mm 93 (42.3) 62 (39.5) 31 (49.2) Tumor subtype <0.001  ALM 89 (40.5) 66 (42.0) 23 (36.5)  SSM 65 (29.5) 45 (28.7) 20 (31.7)  LMM 26 (11.8) 17 (10.8) 9 (14.3)  NM 29 (13.2) 24 (15.3) 5 (7.9)  Others 11 (5.0) 5 (3.2) 6 (9.5) Lymph node metastasis 0.116  Negative 166 (75.0) 123 (78.3) 43 (68.3)  Positive 54 (25.0) 34 (21.7) 20 (31.7) Distant metastasis 0.366  Absent 192 (87.3) 135 (86.0) 57 (90.5)  Present 28 (12.7) 22 (14.0) 6 (9.5) Clinical stage 0.192  I/II 157 (71.4) 116 (73.9) 44 (65.1)  III/IV 63 (28.6) 41 (26.1) 22 (34.9) Table 1. Baseline characteristics of melanoma patients according to PDW levels. SSM, superficial spreading melanoma; LMM, lentigo maligna melanoma; ALM, acrolentigous melanoma; NM, nodular melanoma; PDW, platelet distribution width. Variables PDW ≤ 17.2 PDW > 17.2 P value Age (years) 56.2 (12.6) 56.5 (12.2) 0.834 Smoker (n, %) 17 (10.8) 13 (20.6) 0.570 Drinking (n, %) 14 (8.9) 16 (25.4) 0.525 BMI (kg/m ) 24.8(3.4) 23.6 (2.9) 0.007 FPG (mmol/L) 5.10 (4.80–5.62) 5.00 (4.70–5.50) 0.444 Albumin (g/L) 43.3 (4.3) 44.6 (4.5) 0.047 WBC (×10 /L) 6.44 (2.05) 5.94 (1.87) 0.095 Neutrophils (×10 /L) 3.84 (1.74) 3.49 (1.65) 0.173 Lymphocytes (×10 /L) 2.07 (1.36) 1.85 (0.58) 0.239 Hemoglobin (g/dl) 138.8 (29.8) 139.3 (17.4) 0.903 Platelet count (×10 /L) 242.2 (67.7) 217.3 (60.5) 0.011 MPV (fL) 8.6 (1.2) 9.2 (1.5) 0.001 NLR 2.12(1.32) 2.15(2.30) 0.904 PLR 133.8 (58.9) 125.4 (46.2) 0.265 Table 2. Baseline characteristics of melanoma patients according to PDW levels. Data are expressed as means (SD) or median (IQR). FPG, fasting plasma glucose; WBC, white blood cell; BMI, body mass index; MPV, mean platelet volume; PDW, platelet distribution width; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to- lymphocyte ratio. Scientific Repo R ts | 7: 2970 | DOI:10.1038/s41598-017-03212-y 3 www.nature.com/scientificreports/ Figure 2. Kaplan–Meier analysis of overall survival in melanoma patients. index. These data are also consistent with the current knowledge that anti-platelet is considered to be a part of cancer adjuvant therapy . In addition, our study can form the basis for further mechanistic studies and ultimately aid in patient-tailored selection of therapeutic strategies. e m Th echanisms to explain the association between PDW and survival are poorly understood. Bone marrow cells (including megakaryocytes) dys-function may contribute to altered PDW. PDW is a measure of platelet heterogeneity caused by heterogeneous demarcation of megakarocytes . Recent reports demonstrated several cytokines, such as interleukin-6 (IL-6), granulocytes colony stimulating factor (G-CSF) and macrophage colony stimulating factor (M-CSF), regulate megakaryocytic maturation, platelet production and platelet size . IL-6 pro- motes tumor angiogenesis, metastasis and metabolism . Furthermore, the cytokines G-CSF and M-CSF that be secreted by tumor cells could stimulate megakaryopoiesis and subsequent thrombopoiesis in cancer . However, the clinical value of PDW has not been studied in melanoma. Another possible mechanism is that platelets pro- mote the hypercoagulable state in cancer . Activated platelets create a procoagulant micro-environment that enables the tumor cells to cover themselves with platelets and evade the host immune system . The present study has several limitations. First, this was a single-center retrospective study and additional larger validation studies with multiethnic groups are needed to confirm our results. Second, the mechanisms underlying the involvement of PDW in melanoma remains unclear, to which further investigation should be addressed. Third, the patients were composed of Chinese. The application to other ethnic groups still needs fur - ther investigation. In conclusion, PDW is easily available with routine blood counts. Increased PDW may serve as a marker of adverse prognosis in melanoma. Further studies are warranted to clarify the exact role of PDW in melanoma. Patients and Methods Study population. This study consisted of 220 consecutive melanoma cases (mean age 56.3 ± 17.4 years, range 18–86 years). Cases were admitted to the Third Affiliated Hospital, Harbin Medical University between January 2009 and December 2009. All patients undergone complete surgical resection. The pathologic diagnoses of melanoma were evaluated by pathologists from biopsy reports. None of the patients received preoperative chemotherapy or radiation therapy. Patients were excluded if they had hematological disorders, coronary artery disease, hypertension, diabetes mellitus, and medical treatment with anticoagulant, statins, and acetylic salicylic acid. Standard demographic and clinicopathological data were collected from the patients’ records in hospital. For all the study participants, venous peripheral blood samples were collected at admission. Survival data were obtained through follow-up. Overall survival (OS) was defined as the interval from the date of diagnosis to death or last follow-up. The median follow-up time was 60 months. The platelet-to-lymphocyte ratio (PLR) was calcu- lated as the absolute platelet count measured in ×10 /L divided by the absolute lymphocyte count measured in Scientific Repo R ts | 7: 2970 | DOI:10.1038/s41598-017-03212-y 4 www.nature.com/scientificreports/ Hazard ratio 95% CI P-value Age (years) (≥60 versus <60) 0.591 0.320–1.092 0.093 Gender (male versus female) 0.769 0.433–1.364 0.368 Smoker (yes versus no) 1.060 0.257–4.368 0.936 Drinking (yes versus no) 0.423 0.152–1.180 1.000 BMI (kg/m ) 0.945 0.865–1.033 0.215 FPG (mmol/L) 1.360 0.897–2.063 0.148 Albumin (g/L) 1.075 1.009–1.145 0.026 WBC (×10 /L) 0.894 0.756–1.057 0.189 Neutrophils (×10 /L) 0.931 0.766–1.133 0.477 Lymphocytes (×10 /L) 0.612 0.359–1.042 0.071 NLR 1.105 0.941–1.298 0.224 PLR 1.004 0.978–1.013 0.613 Hemoglobin (g/dl) 0.996 0.983–1.009 0.562 Platelet count (×10 /L) 1.001 0.997–1.005 0.681 MPV (fL) 0.918 0.737–1.143 0.444 PDW (%) (>17.2 versus ≤17.2) 2.857 1.612–5.063 <0.001 Tumor location (Sun-exposed versus 1.721 0.771–3.843 0.185 Sun-protected) Ulceration (Yes versus No) 0.675 0.361–1.261 0.218 Tumor subtype (SSM + NM versus 0.592 0.332–1.055 0.075 ALM + LMM + others) Tumor Size (mm) (≥2.0 versus<2.0) 1.102 0.616–1.974 0.743 Lymph node metastasis (Yes versus No) 2.889 1.625–5.136 <0.001 Distant metastasis (Yes versus No) 1.219 0.546–2.721 0.629 Clinical stage (III/IV versus I/II) 3.386 1.908–6.009 <0.001 Table 3. Univariate analysis of overall survival in melanoma patients. Abbreviations: see to Tables 1 and 2. Hazard ratio 95% CI P-value Albumin (g/L) 1.051 0.985–1.122 0.131 PDW (%) (>17.2 versus ≤17.2) 2.480 1.386–4.436 0.002 Lymph node metastasis (Yes versus No) 0.660 0.223–1.953 0.453 Clinical stage (III/IV versus I/II) 4.311 1.479–12.568 0.007 Table 4. Multivariate analysis of overall survival in melanoma patients. CI, confidence interval. Abbreviations: see to Tables 1 and 2. ×10 /L. e Th neutrophil-to-lymphocyte ratio (NLR) was calculated as the absolute neutrophil count measured in 9 9 ×10 /L divided by the absolute lymphocyte count measured in ×10 /L. rd The Institutional Ethics Review Board of the 3 Affiliated Hospital of Harbin Medical University approved this study prior to commencement of data collection and waived the informed consent requirement because it was a retrospective study. Statistical analysis. All statistical analyses were performed using SPSS Statistics version 22.0 (SPSS Inc., Chicago, IL, USA). The descriptive statistics are presented as means ± SD or medians (interquartile range) for continuous variables and percentages of the number for categorical variables. Inter-group differences in categor - ical variables were assessed for significance using the Chi-square test; differences in continuous variables were assessed using the Mann-Whitney U test or t-test. The optimal cutoff value of PDW was determined by receiver operating characteristic (ROC) curve. We used univariate analysis to narrow down the list of possible prognostic factors. Variables with P value < 0.05 in univariate analysis were brought into multivariate Cox proportional hazard model to determine their independency. Kaplan-Meier curves and log-rank test were used to compare survival differences among groups. All reported p-values are two-sided and statistical significance was assumed as p < 0.05. References 1. Mezouar, S. et al. Role of platelets in cancer and cancer-associated thrombosis: Experimental and clinical evidences. r Th omb. Res. 139, 65–76, doi:10.1016/j.thromres.2016.01.006 (2016). 2. Dovizio, M., Alberti, S., Guillem-Llobat, P. & Patrignani, P. Role of platelets in inflammation and cancer: novel therapeutic strategies. Basic Clin. Pharmacol. Toxicol. 114(1), 118–127, doi:10.1111/bcpt.2013.114.issue-1 (2014). Scientific Repo R ts | 7: 2970 | DOI:10.1038/s41598-017-03212-y 5 www.nature.com/scientificreports/ 3. Suzuki, K. et al. Platelets counts closely correlate with the disease-free survival interval of pancreatic cancer patients. Hepatogastroenterology 51(57), 847–853 (2004). 4. Long, Y, Wang, T, Gao, Q, Zhou, C. Prognostic significance of pretreatment elevated platelet count in patients with colorectal cancer: a meta-analysis. Oncotarget (2016). 5. Pietrzyk, L. et al. Diagnostic Power of Blood Parameters as Screening Markers in Gastric Cancer Patients. Asian Pac. J. Cancer Prev. 17(9), 4433–4437 (2016). 6. Ekici, H. et al. Do Leukocyte and Platelet Counts Have Benet fi for \Preoperative Evaluation of Endometrial Cancer. Asian Pac. J. Cancer Prev. 16(13), 5305–5310, doi:10.7314/APJCP.2015.16.13.5305 (2015). 7. Qiu, J. et al. Preoperative plasma b fi rinogen, platelet count and prognosis in epithelial ovarian cancer. J. Obstet. Gynaecol. Res. 38(4), 651–657, doi:10.1111/j.1447-0756.2011.01780.x (2012). 8. Seretis, C., Youssef, H. & Chapman, M. Hypercoagulation in colorectal cancer: what can platelet indices tell us. Platelets 26(2), 114–118, doi:10.3109/09537104.2014.894969 (2015). 9. Gasparyan, A. Y., Ayvazyan, L., Mikhailidis, D. P. & Kitas, G. D. Mean platelet volume: a link between thrombosis and inflammation. Curr. Pharm. Des. 17(1), 47–58, doi:10.2174/138161211795049804 (2011). 10. Kaito, K. et al. Platelet size deviation width, platelet large cell ratio, and mean platelet volume have sufficient sensitivity and specificity in the diagnosis of immune thrombocytopenia. Br. J. Haematol. 128(5), 698–702, doi:10.1111/j.1365-2141.2004.05357.x (2005). 11. Meikle, C. K. et al. Cancer and Thrombosis: The Platelet Perspective. Front Cell Dev Biol 4, 147, doi:10.3389/fcell.2016.00147 (2016). 12. Barnhill, R. L., Xiao, M., Graves, D. & Antoniades, H. N. Expression of platelet-derived growth factor (PDGF)-A, PDGF-B and the PDGF-alpha receptor, but not the PDGF-beta receptor, in human malignant melanoma in vivo. Br. J. Dermatol. 135(6), 898–904, doi:10.1046/j.1365-2133.1996.d01-1092.x (1996). 13. Boukerche, H. et al. Platelet-melanoma cell interaction is mediated by the glycoprotein IIb-IIIa complex. Blood 74(2), 658–663 (1989). 14. Kolber, D. L., Knisely, T. L. & Maione, T. E. Inhibition of development of murine melanoma lung metastases by systemic administration of recombinant platelet factor 4. J. Natl. Cancer Inst. 87(4), 304–309, doi:10.1093/jnci/87.4.304 (1995). 15. Paulus, J. M. Recent advances in the story of megakaryocyte physiology. Pathol. Biol. 29(3), 133–135 (1981). 16. Kaushansky, K. Growth factors and hematopoietic cell fate. A new feature: controversies in hematology. Blood 92(2), 345–344 (1998). 17. Kumari, N., Dwarakanath, B. S., Das, A., Bhatt, A. N. Role of interleukin-6 in cancer progression and therapeutic resistance. Tumour Biol. (2016). 18. Kowanetz, M. et al. Granulocyte-colony stimulating factor promotes lung metastasis through mobilization of Ly6G+Ly6C+ granulocytes. Proc. Natl. Acad. Sci. USA 107(50), 21248–21255, doi:10.1073/pnas.1015855107 (2010). 19. Li, N. Platelets in cancer metastasis: To help the “villain” to do evil. Int. J. Cancer 138(9), 2078–2087, doi:10.1002/ijc.v138.9 (2016). 20. Franco, A. T., Corken, A. & Ware, J. Platelets at the interface of thrombosis, inflammation, and cancer. Blood 126(5), 582–588, doi:10.1182/blood-2014-08-531582 (2015). Acknowledgements This work was supported financially by grants from the Harbin special fund for scientific and technological innovation talents (RC2016XK004068). Author Contributions N.L. and Z.Y.D. participated in manuscript preparation, data analysis and editing. XY.H., and Y.N. participated in data collection and data analysis. T.M.L. and Z.P.L. participated in study design, and manuscript revision. R.T.W. and K.J.Y. participated in study design, data analysis, and manuscript preparation. All authors read and approved the final manuscript. Additional Information Competing Interests: The authors declare that they have no competing interests. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 Cre- ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per- mitted 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 license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2017 Scientific Repo R ts | 7: 2970 | DOI:10.1038/s41598-017-03212-y 6

Journal

Scientific ReportsSpringer Journals

Published: Jun 7, 2017

There are no references for this article.