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中华妇幼临床医学杂志(电子版) ›› 2019, Vol. 15 ›› Issue (03) : 268 -274. doi: 10.3877/cma.j.issn.1673-5250.2019.03.006

所属专题: 文献

论著

血小板与淋巴细胞比值对卵巢癌患者国际妇产科联盟临床分期的预测价值
唐英1, 李均1, 胡辉权1,(), 徐凡1, 陈明星2   
  1. 1. 南充市中心医院妇科,四川 637000
    2. 川北医学院,四川南充 637000
  • 收稿日期:2019-01-08 修回日期:2019-05-04 出版日期:2019-06-01
  • 通信作者: 胡辉权

Predictive value of plate-to-lymphocyte ratio for International Federation of Obstetrics and Gynecology staging of ovarian cancer patients

Ying Tang1, Jun Li1, Huiquan Hu1,(), Fan Xu1, Mingxing Chen2   

  1. 1. Department of Gynecology, Nanchong Central Hospital, Nanchong 637000, Sichuan Province, China
    2. North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
  • Received:2019-01-08 Revised:2019-05-04 Published:2019-06-01
  • Corresponding author: Huiquan Hu
  • About author:
    Corresponding author: Hu Huiquan, Email:
  • Supported by:
    Science & Technology Pillar Program by Science and Technology Department of Sichuan Province(2015JY0056); Project of Bureau of Science & Technology and Intellectual Property Nanchong City(17YFZJ0005, 18YFZJ0012)
引用本文:

唐英, 李均, 胡辉权, 徐凡, 陈明星. 血小板与淋巴细胞比值对卵巢癌患者国际妇产科联盟临床分期的预测价值[J/OL]. 中华妇幼临床医学杂志(电子版), 2019, 15(03): 268-274.

Ying Tang, Jun Li, Huiquan Hu, Fan Xu, Mingxing Chen. Predictive value of plate-to-lymphocyte ratio for International Federation of Obstetrics and Gynecology staging of ovarian cancer patients[J/OL]. Chinese Journal of Obstetrics & Gynecology and Pediatrics(Electronic Edition), 2019, 15(03): 268-274.

目的

探讨血小板与淋巴细胞比值(PLR)对卵巢癌患者国际妇产科联盟(FIGO)临床分期的预测价值。

方法

选择2005年1月至2017年1月,于南充市中心医院就诊的225例初治卵巢癌患者为研究对象。采取回顾性分析方法,根据FIGO分期,将其分为早期卵巢癌组(n=85,FIGO分期为Ⅰ~Ⅱ期)和晚期卵巢癌组(n=140,FIGO分期为Ⅲ~Ⅳ期)。采用Wilcoxon秩和检验,对2组患者的年龄、人体质量指数(BMI)、白细胞计数、血红蛋白(Hb)水平、PLR和中性粒细胞与淋巴细胞比值(NLR)进行统计学分析。采用χ2检验,对2组患者的组织病理学类型、合并恶性腹水患者所占比例、血清CA125水平>35 U/mL所占比例等进行统计学分析。绘制受试者工作特征(ROC)曲线,对PLR预测卵巢癌患者FIGO分期进行分析,并计算ROC曲线下面积(ROC-AUC)。采用多因素非条件logistic回归分析,判断PLR预测卵巢癌患者FIGO临床分期的准确性。本研究遵循的程序符合2013年修订的《世界医学协会赫尔辛基宣言》要求。

结果

①2组患者的年龄、BMI、Hb水平和白细胞计数比较,差异均无统计学意义(P>0.05);而其组织病理学类型、是否合并恶性腹水、血清CA125水平>35 U/mL、PLR和NLR比较,则差异均有统计学意义(χ2=16.897、76.187、11.817、-5.252、-3.790, P均<0.05)。②组织病理学类型(OR=2.460,95%CI=1.246~4.856,P=0.009),是否合并恶性腹水(OR=6.649,95%CI=3.437~12.862,P<0.001)和PLR(OR=1.005,95%CI=1.001~1.009,P=0.027),均为预测卵巢癌患者FIGO分期的独立危险因素。③PLR对预测卵巢癌患者FIGO分期的ROC-AUC为0.709(95%CI:0.645~0.767,P<0.001)。根据约登指数最大原则,PLR预测卵巢癌患者FIGO临床分期的最佳临界值为157.14。其中,本研究225例初治卵巢癌患者中,PLR>157.14者为134例,PLR≤157.14者为91例。此时,PLR对预测卵巢癌患者FIGO分期的敏感度为72.9%,特异度为63.5%。

结论

PLR值可预测卵巢癌FIGO分期。当PLR>157.14时,卵巢癌患者FIGO临床分期为晚期的可能性较大。

Objective

To explore the predictive value of platelet-to-lymphocyte ratio (PLR) for International Federation of Obstetrics and Gynecology (FIGO) staging of ovarian cancer patients.

Methods

From January 2005 to January 2017, a total of 225 patients with primary ovarian cancer who were treated at Nanchong Central Hospital were selected into this study. According to different FIGO stagings, they were divided into early ovarian cancer group (n=85, FIGO stage Ⅰ-Ⅱ) and advanced ovarian cancer group (n=140, FIGO stage Ⅲ-Ⅳ). Wilcoxon rank sum test was used to statistically compare age, body mass index (BMI), white blood cell count, hemoglobin (Hb) level, PLR and neutrophil-to-lymphocyte ratio (NLR) between two groups. Chi-square test was used to statistically compare histopathological types, proportion of patients with ascites and proportion of serum CA125 level >35 U/mL. Then receiver operator characteristic (ROC) curve of PLR for predicting the FIGO staging of ovarian caner patients was drawn, and the area under ROC curve (ROC-AUC) was calculated. The optimal critical value of PLR for predicting the clincial staging of ovarian caner patients was obtained when the Youden index reaching the maximum value. And its sensitivity and specificity were calculated. This study met the requirements of the World Medical Association Declaration of Helsinki revised in 2013. Informed consent was obtairned from each participates.

Results

①There were significant differences in histopathological types, malignant ascites or not, serum CA125 level > 35 U/mL, PLR and NLR between two groups (χ2=16.897, 76.187, 11.817, -5.252, -3.790; P all<0.05), while there were no significant differences in age, BMI, Hb level and WBC count between two groups (P>0.05). ②Histopathological type (OR=2.460, 95%CI=1.246-4.856, P=0.009), malignant ascites (OR=6.649, 95%CI=3.437-12.862, P<0.001) and PLR (OR=1.005, 95%CI=1.001-1.009, P=0.027) were independent risk factors for ovarian cancer patients with different FIGO staging. ③ROC curve analysis showed that PLR for predicting FIGO staging of ovarian cancer patients was 0.709 (95%CI: 0.645-0.767, P<0.001). According to the principle of maximum Youden index, the optimal critical value for PLR to predict FIGO staging of ovarian cancer patients was 157.14, of which 134 patients with PLR > 157.14 and 91 patients with PLR≤157.14. At this time, the sensitivity was 72.9%, and the specificity was 63.5%.

Conclusions

PLR value can predict FIGO staging of ovarian cancer. When PLR>157.14, the FIGO staging of ovarian cancer patients is more likely to be advanced.

表1 225例卵巢癌患者的一般临床资料
表2 2组卵巢癌患者的临床特征比较
表3 卵巢癌患者FIGO分期影响因素的多因素非条件logistic回归分析变量含义及赋值情况
表4 卵巢癌患者FIGO分期影响因素的多因素非条件logistic回归分析结果
图1 PLR预测卵巢癌患者FIGO分期的ROC曲线
[1]
Ahmed N, Abubaker K, Findlay JK. Ovarian cancer stem cells: molecular concepts and relevance as therapeutic targets[J]. Mol Aspects Med, 2014, 39: 110-125.
[2]
Teng Z Han R, Huang X, et al.Increase of incidence and mortality of ovarian cancer during 2003-2012 in Jiangsu Province, China[J]. Front Public Health, 2016, 4: 146.
[3]
Torre LA, Trabert B, DeSantis CE, et al. Ovarian cancer statistics, 2018[J]. CA Cancer J Clin, 2018, 68(4): 284-296.
[4]
Morgan RJ Jr, Armstrong DK, Alvarez RD, et al. Ovarian cancer, version 1. 2016, NCCN clinical practice guidelines in oncology[J]. J Natl Compr Canc Netw, 2016, 14(9): 1134-1163.
[5]
Daly MB, Pilarski R2, Berry M, et al.NCCN guidelines insights: genetic/familial high-risk assessment: breast and ovarian, version 2. 2017[J]. J Natl Compr Canc Netw, 2017, 15(1): 9-20.
[6]
Wright AA, Bohlke K, Armstrong DK, et al. Neoadjuvant chemotherapy for newly diagnosed, advanced ovarian cancer: Society of Gynecologic Oncology and American Society of clinical oncology clinical practice guideline[J]. Gynecol Oncol, 2016, 143(1): 3-15.
[7]
Timmermans M, van der Aa MA, Lalisang RI, et al. Interval between debulking surgery and adjuvant chemotherapy is associated with overall survival inpatients with advanced ovarian cancer[J]. Gynecol Oncol, 2018, 150(3): 446-450
[8]
Elinav E, Nowarski R, Thaiss CA, et al. Inflammation induced cancer: crosstalk between tumours, immune cells and microorganisms[J]. Nat Rev Cancer, 2013, 13(11): 759-771.
[9]
Ceran MU, Tasdemir U, Colak E, et al. Can complete blood count inflammatory parameters in epithelial ovarian cancer contribute to prognosis? A survival analysis[J]. J Ovarian Res, 2019, 12(1): 16.
[10]
Zhang WW, Liu KJ, Hu GL, et al. Preoperative platelet/lymphocyte ratio is a superior prognostic factor compared to other systemic inflammatory response markers in ovarian cancer patients[J]. Tumour Biol, 2015, 36(11): 8831-8837.
[11]
Ma XM, Sun X, Yang GW, et al. The platelet-to-lymphocyte ratio as a predictor of patient outcomes in ovarian cancer: a Meta-analysis[J]. Climacteric, 2017, 20(5): 448-455.
[12]
Zhu Y, Zhou S, Liu Y, et al. Prognostic value of systemic inflammatory markers in ovarian cancer: a PRISMA-compliant Meta-analysis and systematic review[J]. BMC Cancer, 2018, 18(1): 443.
[13]
Balkwill F, Mantovani A. Inflammation and cancer: back to Virchow? [J]. Lancet, 2001, 357(9255): 539-545.
[14]
李秋丽,张月香. 血小板增多症在卵巢癌中的研究进展[J]. 现代妇产科进展,2014, 23(10): 840-841.
[15]
Rao AK, Rao DA. Platelets signal and tumors take off[J]. Blood, 2012, 120(24): 4667-4668.
[16]
Stone RL, Nick AM, McNeish IA, et al. Paraneoplastic thrombocytosis in ovarian cancer[J]. N Engl J Med, 2012, 366(7): 610-618.
[17]
唐英,徐凡,罗祥力,等. 血小板/淋巴细胞计数比对卵巢癌临床预测价值的研究进展[J]. 西部医学,2017, 29(5): 725-728.
[18]
Fu BH, Fu ZZ, Meng W, et al. Platelet VEGF and serum TGF-β1 levels predict chemotherapy response in non-small cell lung cancer patients[J]. Tumour Biol, 2015, 36(8): 6477-6483.
[19]
Dunn GP, Old LJ, Schreiber RD. The immunobiology of cancer immunosurveillance and immunoediting[J]. Immunity, 2004, 21(2): 137-148.
[20]
Rosenberg SA. Progress in human tumour immunology and immunotherapy[J]. Nature, 2001, 411(6835): 380-384.
[21]
Shen GH, Ghazizadeh M, Kawanami O, et al. Prognostic significance of vascular endothelial growth factor expression in human ovarian carcinoma[J]. Br J Cancer, 2000, 83(2): 196-203.
[22]
Huang Y, Chen X, Dikov MM, et al. Distinct roles of VEGFR-1 and VEGFR-2 in the aberrant hematopoiesis associated with elevated levels of VEGF[J]. Blood, 2007, 110(2): 624-631.
[23]
刘艳,朱美琪,钱志红. 卵巢良恶性肿瘤患者血浆sIL-2R,IL-6及T细胞亚群变化研究[J]. 苏州医学院学报,2000, 20(1): 25-27.
[24]
Chen L, Zhang F, Sheng XG, et al. Decreased pretreatment lymphocyte/monocyte ratio is associated with poor prognosis in stage Ⅰb1-Ⅱa cervical cancer patients who undergo radical surgery[J]. Onco Targets Ther, 2015, 8: 1355-1362.
[25]
Ceran MU, Tasdemir U, Colak E, et al. Can complete blood count inflammatory parameters in epithelial ovarian cancer contribute to prognosis? A survival analysis[J]. J Ovarian Res, 2019, 12(1): 16.
[26]
Facciabene A, Motz GT, Coukos G. T-regulatory cells: key players in tumor immune escape and angiogenesis[J]. Cancer Res, 2012, 72(9): 2162-2171.
[27]
Wang H, Franco F, Ho PC, et al. Metabolic regulation of Tregs in cancer: opportunities for immunotherapy[J]. Trends Cancer, 2017, 3(8): 583-592.
[28]
Yigit R, Massuger LF, Figdor CG, et al. Ovarian cancer creates a suppressive mic roenvironment to escape immune elimination[J]. Gynecol Oncol, 2010, 117(2): 366-372.
[29]
Kuhn W, Pache L, Schmalfeldt B, et al. Urokinase (uPA) and PAI-1 predict survival in advanced ovarian cancer patients (FIGO Ⅲ) after radical surgery and platinum-based chemotherapy[J]. Gynecol Oncol, 1994, 55(3): 401-409.
[30]
Konecny G, Crohns C, Pegram M, et al. Correlation of drug response with the ATP tumorchemosensitivity assay in primary FIGO stage Ⅲ ovarian cancer.[J]. Gynecologic Oncol, 2000, 77(2): 258-263.
[31]
Wimberger P, Wehling M, Lehmann N, et al. Influence of residual tumor on outcome in ovarian cancer patients with FIGO stage Ⅳ disease[J]. Ann Surg Oncol, 2010, 17(6): 1642-1648.
[32]
Werness BA, Ramus SJ, DiCioccio RA, et al. Histopathology, FIGO stage, and BRCA mutation status of ovarian cancers from the Gilda Radner Familial Ovarian Cancer Registry.[J]. Int J Gynecol Pathol, 2004, 23(1): 29-34.
[33]
Medeiros LR, Rosa DD, Bozzetti MC, et al. Laparoscopy versus laparotomy for FIGO stage Ⅰ ovarian cancer[M]. New York: The Cochrane Library, 2005.
[34]
Kristensen GB, Vergote I, Stuart G, et al. First-line treatment of ovarian cancer FIGO stages Ⅱb-Ⅳ with paclitaxel/epirubicin/carboplatin versus paclitaxel/carboplatin[J]. Int J Gynecol Cancer, 2010, 13(s2): 172-177.
[35]
Lambert HE, Berry RJ. High dose cisplatin compared with high dose cyclophosphamide in the management of advanced epithelial ovarian cancer (FIGO stages Ⅲ and Ⅳ): report from the North Thames Cooperative Group [J]. Br Med J, 1985, 290(6472): 889-893.
[36]
王鑫,张虹. 术前NLR和PLR对上皮性卵巢癌患者预后的评估价值[J]. 现代妇产科进展,2016, 25(6):433-436.
[37]
唐英,李均,徐凡,等. 单核细胞与淋巴细胞比值及其与上皮性卵巢癌患者预后的关系 [J/CD] . 中华妇幼临床医学杂志(电子版),2017,13(5): 532-538.
[38]
王刚,陈扬平. 晚期卵巢癌腹腔镜下肿瘤细胞减灭术的临床相关问题 [J/CD] . 中华妇幼临床医学杂志(电子版), 2016, 12(4): 373-378.
[39]
Polterauer S, Vergote I, Concin N, et al. Prognostic value of residual tumor size in patients with epithelial ovarian cancer FIGO stages ⅡA-Ⅳ: analysis of the OVCAD data.[J]. Int J Gynecol Cancer, 2012, 22(3): 380-385.
[40]
Grabowski J P, Harter P, Buhrmann C, et al. Re-operation outcome in patients referred to a gynecologic oncology center with presumed ovarian cancer FIGO Ⅰ-ⅢA after sub-standard initial surgery[J]. Surg Oncol, 2012, 21(1): 31-35.
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