Sage Journals: Discover world-class research

Abstract

Objective

Peripheral systemic inflammatory, nutritional, and coagulation biomarkers have prognostic and predictive value in various malignancies. We evaluated the prognostic and predictive roles of systemic inflammatory, nutritional, and coagulation biomarkers in the circulating blood of patients with advanced cervical cancer.

Methods

A retrospective study of 795 patients with cervical cancer who received concurrent chemoradiation therapy was performed. Overall survival was evaluated by the Kaplan–Meier estimator. Univariate and multivariate Cox regression models were used to determine prognostic factors associated with overall survival.

Results

The median follow-up time was 76 months. In the univariate analysis, overall survival showed positive prognostic value in patients with a platelet-to-lymphocyte ratio (PLR) <164.29 (P = 0.010), and a plasma fibrinogen (FIB) level <4 g/L(P = 0.012). In the multivariate analysis, the PLR (P = 0.036), and FIB level (P = 0.047) maintained their significance for overall survival. Therefore, the PLR and FIB levels are independent prognostic factors in patients with advanced cervical cancer.

Conclusions

Systemic inflammatory and coagulation biomarkers could help to understand survival differences in the clinical treatment of advanced cervical cancer. The PLR and FIB levels are independent prognostic factors of poor survival in patients with advanced cervical cancer.

Keywords

Advanced cervical cancer fibrinogen overall survival platelet-to-lymphocyte ratio

Introduction

Cervical cancer is the fourth most common female malignant tumor in the world.¹ According to recent worldwide research in 2018, approximately 570,000 women are diagnosed with cervical cancer, which causes 311,000 deaths worldwide.² The established standard of care for advanced cervical cancer is concurrent chemoradiation.³ At present, the International Federation of Gynecology and Obstetrics (FIGO) staging system has been proven to be able to predict overall survival (OS) in cervical cancer patients. Advanced cervical cancer has a worse prognosis than stage IA–IIA cervical cancer. Because of the poor prognosis of advanced cervical cancer, the identification of prognostic biomarkers remains a challenge.

Systemic inflammation, nutrition, and coagulation have emerged as potential prognostic factors for patient survival because they play roles in tumor promotion and progression.^4,5 In recent years, several inflammation-based scores, nutritional statuses, and coagulation indices have been confirmed as significant predictors in cervical cancer and other tumors.^5–9 Several prognostic indices have been proposed, such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), body mass index (BMI), and serum albumin (ALB) level. All these biomarkers can be acquired easily by peripheral blood biochemical tests and have great advantages for clinical applications.

However, few studies have focused on the correlation between these indices and survival in patients with advanced cervical cancer. Thus, we aimed to evaluate the prognosis of patients with advanced cervical cancer using inflammatory, nutritional, and coagulation indices.

Methods

Patients

We conducted a retrospective analysis of patients who were pathologically diagnosed with cervical cancer at Shandong Cancer Hospital between October 2009 and December 2014. We retrospectively collected clinical characteristics, hematological examination results, and survival data. We excluded (a) patients who underwent hysterectomy or cervical resection surgery; (b) who had other tumors, hematological diseases or severe liver or renal dysfunction; or (c) who did not undergo peripheral blood tests at diagnosis.

One point to be emphasized is that the tumor stage of all patients was redefined according to the FIGO 2018 criteria for cervical cancer. We evaluated the lymph node status though imaging methods such as computed tomography (CT) scans and positron emission tomography-CT scans. Among these patients, 6 were previously diagnosed with stage IB disease, 12 with stage IIA disease, and 85 with stage IIB disease but were restaged with stage III disease. A total of 795 patients with stage IIB–IVA disease were included in this study.

This study was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants. This retrospective study was approved by the Ethics Committee of Shandong Cancer Hospital.

Study design and data collection

A total of 795 patients who had received concurrent chemoradiation therapy were included in this study. The clinical and pathological data collected included age, BMI, tumor size, histotype, grade, FIGO stage, and hematological parameters including the neutrophil count, monocyte count, lymphocyte count, platelet count, plasma fibrinogen (FIB) level, lactate dehydrogenase (LDH) level and ALB level. In this study, the NLR, PLR, MLR, systemic inﬂammation response index (SIRI), and aggregate index of systemic inflammation (AISI) were calculated as follows: NLR = neutrophil count/lymphocyte count; PLR = platelet count/lymphocyte count; MLR = monocyte count/lymphocyte count; SIRI = neutrophil count ∗ monocyte count/lymphocyte count; and AISI = neutrophil count ∗ monocyte count ∗ platelet count/lymphocyte count. We obtained all the raw values at the same time in each patient.

Because of the lack of validated cut-off values, NLR, MLR, PLR, SIRI, and AISI values were used median value as the cutoff values for OS. The laboratory reference values of PLT, LDH, ALB, and FIB were used as the cut-off values for OS. Patients included in this study were followed-up every 6 months for the first 2 years then annually until death, and all patient follow-ups were ceased in May 2020. It was difficult to accurately acquire the time of recurrence and metastasis. Thus, we ultimately decided to use the OS rate to determine patient prognosis. OS was defined as the time from the initial diagnosis to last contact or death.

Statistical analysis

Statistical analyses were performed using Statistical Product and Service Solutions 22.0 software (IBM Corp., Armonk, NY, USA) and GraphPad prism 8.0 (Windows, GraphPad Software, San Diego, CA, USA; www.graphpad.com). Estimates of OS were calculated using the Kaplan–Meier method. Significant prognostic variables in the univariate and multivariate analyses were entered into a Cox regression model. The variables used in the multivariate analysis were obtained from the univariate analysis. P < 0.05 was considered statistically significant in all analyses.

Results

Patient characteristics

The clinical characteristics of the 795 patients are listed in Table 1. The median age of the patients at diagnosis was 51 years (range: 21–69 years). The median follow- up time was 76 months (range: 2–127 months).

Table 1.

Characteristics of the study population (N = 795).

Variable	Category	Number	Percentage (%)
Age, years	<51	396	49.81
Age, years	≥51	399	50.19
BMI, kg/m²	<18.5	27	3.40
	18.5–22.9	238	29.94
	≥23	530	66.67
Histotype	Squamous	728	91.57
	Non-squamous	55	6.92
	Unknown	12	1.51
Maximum tumor size, cm	≤4	318	40.00
	>4	468	58.87
	Unknown	9	1.13
Tumor grade	G1	39	4.91
	G2	257	32.33
	G3	248	31.19
	Unknown	251	31.57
Tumor stage	IIB	152	19.12
	III	636	80.00
	IVA	7	0.88
Status	Alive	619	77.86
Status	Dead	176	22.14

BMI: body mass index.

Univariate and multivariate analyses for OS

In the univariate risk analysis model, a PLR <164.29 (log-rank test P = 0.010), and an FIB level <4 g/L (P = 0.012) showed significantly positive prognostic value for OS (Figures 1 and 2 and Table 2). Other clinically meaningful parameters such as the histological types (P < 0.001), and early tumor stage (III vs. IIB P = 0.001; IV vs. IIB P = 0.010) were also significant prognostic factors.

Figure 1.

Kaplan–Meier survival curve for overall survival (OS) according to platelet-to-lymphocyte ratio (PLR) values.

Figure 2.

Kaplan–Meier survival curve for overall survival (OS) according to fibrinogen (FIB).

Table 2.

Univariate Cox regression analysis in advanced cervical cancer patients.

Variable	Category	Survival (%)	Death (%)	HR	95% CI
Age, years	< 51	311 (39.12)	85 (10.69)	1
Age, years	≥51	308 (38.74)	91 (11.45)	1.072	0.797, 1.440
BMI, kg/m²	< 18.5	17 (2.14)	10 (1.26)	1
	18.5–22.9	185 (23.27)	53 (6.67)	0.553	0.282, 1.088
	≥23	417 (52.45)	113 (14.21)	0.521	0.273, 0.995
Histotype	Squamous	576 (72.45)	152 (19.12)	1
	Non-squamous	33 (4.15)	22 (2.77)	2.273	1.454, 3.555
	Unknown	10 (1.26)	2 (0.25)
Maximum tumor size, cm	≤4	257 (32.33)	61 (7.67)	1
	>4	356 (44.78)	112 (14.09)	1.290	0.944, 1.762
	Unknown	6 (0.75)	3 (0.38)
Tumor grade	G1	32 (4.03)	7(0.88)	1
	G2	206 (25.91)	51 (6.42)	1.106	0.482−2.341
	G3	194 (24.40)	54 (6.79)	1.200	0.546−2.635
	Unknown	187 (23.52)	64 (8.05)
Tumor stage	IIB	135 (16.98)	17 (2.14)	1
	III	480 (60.38)	156 (19.62)	2.386	1.446, 3.936
	IVA	4 (0.50)	3 (0.38)	5.502	1.480, 17.244
NLR	<2.65	309 (38.87)	87 (10.94)	1
NLR	≥2.65	310 (38.99)	89 (11.19)	1.035	0.770, 1.390
MLR	<0.25	306 (38.49)	84 (10.57)	1
MLR	≥0.25	313 (39.37)	92 (11.57)	1.080	0.803, 1.452
PLR	<164.29	324 (40.75)	73 (9.18)	1
PLR	≥164.29	295 (37.11)	103 (12.96)	1.486	1.101, 2.006
SIRI	<1.07	308 (38.74)	87 (10.94)	1
SIRI	≥1.07	311(39.13)	89 (11.19)	1.026	0.763, 1.378
AISI	<301.33	307 (38.62)	90 (11.32)	1
AISI	≥301.33	312 (39.25)	86 (10.81)	0.953	0.709, 1.281
PLT, × 10 ⁹/L	≤300	419 (52.70)	106 (13.33)	1
PLT, × 10 ⁹/L	>300	200 (25.16)	70 (8.81)	1.337	0.989, 1.808
ALB, g/L	<40	138 (17.36)	48 (6.04)	1
ALB, g/L	≥40	481 (60.50)	128 (16.10)	0.764	0.548, 1.065
LDH, U/L	≤245	602 (75.72)	169 (21.26)	1
LDH, U/L	>245	17 (2.14)	7 (0.88)	1.429	0.671, 3.043
FIB, g/L	<4	476 (59.87)	119 (14.97)	1
FIB, g/L	≥4	143 (17.99)	57 (7.17)	1.499	1.093, 2.055

AISI: aggregate index of systemic inflammation; ALB: albumin; BMI: body mass index; CI: confidence interval; FIB: fibrinogen; HR: hazard ratio; LDH: lactate dehydrogenase; MLR: monocyte-to-lymphocyte ratio; NLR: neutrophil-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; SIRI: systemic inﬂammation response index.

In the multivariate analysis, the tumor histological types, stage, PLR, and FIB levels maintained their significance for OS. Therefore, the tumor histological type, stage, PLR, and FIB level were associated with prognosis and should be used as independent prognostic factors according to our series (Table 3).

Table 3.

Multivariate Cox regression analysis in advanced cervical cancer patients.

Variable	Category	HR	95% CI	P-value
Histotype	Squamous	1		＜0 . 001
Histotype	Non-squamous	2.251	1.438, 3.524
Tumor stage	IIB	1		0.005
	III	2.288	1.383, 3.783	0.001
	IVA	2.851	0.653, 12.444	0.163
PLR	<164.29	1		0.036
PLR	≥164.29	1.385	1.021, 1.878
FIB, g/L	<4	1		0.047
FIB, g/L	≥4	1.385	1.005, 1.910

CI, confidence interval; FIB, fibrinogen; HR, hazard ratio; PLR, platelet-to-lymphocyte ratio.

Discussion

Although the incidence of cervical cancer has tended to decrease, it also continues to constitute a major public health problem.² Exploring the prognostic factors of cervical cancer, and improving clinical treatment measures are of utmost importance. In recent years, many genetic, laboratory, clinical, and pathological studies have examined the prognostic factors of cervical cancer.^10–12 In recent studies, a large number of clinical indicators such as race, age, histological type, grade, and tumor volume seemed to be associated with prognosis.¹³ In our study, age, tumor size, and grade were not correlated with prognosis. Moreover, the histological type and tumor stage were identified as independent prognostic factors of poor survival. We believe these results may be related to the characteristics of advanced tumors. Tumor differentiation grade and tumor volume may be better prognostic factors in women with early stage cervical cancer than in those with advanced-stage cervical cancer.

The current study also demonstrated a rare association of the PLR, NLR, MLR, AISI, and SIRI and PLT, FIB, LDH, and ALB levels with OS in patients with advanced cervical cancer. This study also showed that elevated PLR and FIB levels were useful indicators of a poor prognosis.

A significant amount of evidence shows that inflammatory markers have critical value in evaluating the prognosis of most of the common cancers. Inflammation often contributes to the development and progression of cancer.¹⁴ Inflammatory cells are an important component of the tumor microenvironment and participate in the neoplastic process, fostering proliferation, survival, and migration. In addition, tumor cells have co-opted certain signaling molecules of the immune system, such as selectins, chemokines, and their receptors, which stimulate tumor growth and progression.^15,16

Platelet counts inﬂuence the metastatic potential of tumor cells. Platelets prevent tumor cells from contacting the host's immune system, disturb natural killer cell recognition, and facilitate extravasation of tumor cells.¹⁷ Some studies have reported that increased platelets have a significant effect on patient survival.^18–20 Lymphocytes are a critical part of the immune response, and there is no doubt that a low number of lymphocytes can lead to tumor progression.

To explore simple and effective prognostic indicators for assessing the prognosis of cervical cancer patients and guiding clinical treatment, our study examined and analyzed many indicators based on inflammation. The results showed that a high PLR was signiﬁcantly associated with poor OS in patients with advanced cervical cancer who were treated with concurrent chemoradiation. Li et al.²¹ summarized 33 cohort studies including 8215 patients with advanced cancers—including biliary tract cancer, pancreatic cancer, nasopharyngeal carcinoma, and gastric cancer—and concluded that patients with a high pretreatment PLR had poor OS and progression-free survival (PFS). Regarding cervical cancer, Ma et al.²² researched a total of 12 studies that included 3886 patients and concluded that the pretreatment PLR could serve as a predictive biomarker of a poor prognosis for cervical cancer patients, although most of them were in the early stage. Our result, which was derived mainly from patients with advanced cervical cancer, was the same as that described above and indicates that the PLR is signiﬁcantly associated with poor survival in cervical cancer patients.

Malnutrition is common in patients with malignant tumors. Scientists have suspected for decades that nutrition has an important influence on the risk of developing cancer.²³ Malnutrition, a subacute or chronic state in cancer patients, may impair immune function and increase susceptibility to infection and complications of treatment, thus leading to an increased mortality rate of cancer patients.²⁴ Some studies were conducted to evaluate the association between nutrition and cancer.²⁵ Serum albumin levels and BMI are common nutritional indicators. Some reports showed that a reduction of in BMI and hypoalbuminemia were related to the poor prognosis of malignancy,^26–28 while other reports showed that albumin levels and BMI were not independent prognostic indicators for OS.^29,30 Our results are congruent with the latter results. Therefore, the relationship between cancer and nutrition is puzzling and needs more study for verification.

There are emerging links between malignancy and the hemostatic system.³¹ Fibrinogen plays a critical role in achieving and maintaining hemostasis and is fundamental for effective clot formation.³² Fibrinogen is also a classical positive acute-phase reactant protein.³³ Tumor cells and tumor-derived inflammatory cells may subclinically activate the coagulation cascade,³⁴ and there are relationships between hemostatic factors and advanced tumor stage, large tumor size, and tumor biology.³⁵ Fibrinogen promotes the cellular responses of adhesion, proliferation, and migration during angiogenesis and tumor cell growth by participating in the formation of the extracellular matrix and supporting the binding of growth factors.^36–39 Elevated plasma platelet levels are a common phenomenon in a variety of malignancies.^37,40

In our present study, we retrospectively analyzed 795 patients with advanced cervical cancer whose pretreatment plasma fibrinogen levels were measured at diagnosis. We found that plasma fibrinogen levels are significantly correlated with a poor prognosis. Polterauer et al.⁴¹ reported that plasma fibrinogen levels were an independent parameter in 313 cervical cancer patients, including 141 patients with advanced cervical cancer, which was in accordance with our study. Zhao et al.⁴² concluded that hyperfibrinogenemia may be a valuable parameter for predicting the recurrence of early stage cervical cancer. We verified this finding in a larger number of patients with advanced cervical cancer.

There were some limitations to this study. First, our study lacked data on PFS, which can reflect the status related to cancer. Second, it was a single-center, retrospective study, and its generalization is limited. Third, the detailed concurrent chemoradiation regimen has some heterogeneity and can lead to different clinical results. Third, because of the lack of a unified standard, the cut-off values of biomarkers for prognosis vary in different reports with unknown underlying mechanisms. Further studies are needed to validate the prognostic impact of markers in patients with advanced cervical cancer.

Conclusions

The inflammatory response and coagulating reaction can predict OS in cervical cancer patients. In particular, the PLR and FIB levels evaluated before treatment play a significant prognostic role in advanced cervical cancer. The PLR and FIB levels are inexpensive and readily available biomarkers that could provide additional prognostic information and thus have promising prospects for application. Larger prospective studies with adequate sample sizes are needed to validate this observation.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Disclosure

The authors report no conﬂicts of interest in this work.

Ethics approval and consent to participate

This retrospective study was approved by the Ethics Committee of Shandong Cancer Hospital.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Key Technology Research and Developmental Program of China (Program Nos. 2022YFC2704400 and 2022YFC2704401).

ORCID iD

Baoxia Cui

References

Kamangar

Dores

Anderson

. Patterns of cancer incidence, mortality, and prevalence across five continents: defining priorities to reduce cancer disparities in different geographic regions of the world. J Clin Oncol 2006; 24: 2137–2150.

Arbyn

Weiderpass

Bruni

, et al. Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet Glob Health 2020; 8: e191–e203.

Naga

CH P

Gurram

Chopra

, et al. The management of locally advanced cervical cancer. Curr Opin Oncol 2018; 30: 323–329.

Hanahan

Weinberg

RA.

Hallmarks of cancer: the next generation. Cell 2011; 144: 646–674.

Huang

Liu

Zhu

, et al. Prognostic value of preoperative systemic immune-inflammation Index in patients with cervical cancer. Sci Rep 2019; 9: 3284.

Zhang

Liu

, et al. Pretreatment C-reactive protein/albumin ratio is associated with poor survival in patients with stage IB-IIA cervical cancer. Cancer Med 2018; 7: 105–113.

Cao

Cui

, et al. Fibrinogen/albumin ratio Index is an independent prognosis predictor of recurrence-free survival in patients after surgical resection of gastrointestinal stromal tumors. Front Oncol 2020; 10: 1459.

Huh

Ryu

Chun

, et al. High platelet-to-lymphocyte ratio is associated with poor prognosis in patients with unresectable intrahepatic cholangiocarcinoma receiving gemcitabine plus cisplatin. BMC Cancer 2020; 20: 907.

Zheng

Chen

, et al. Combination of systemic inflammation response Index and platelet-to-lymphocyte ratio as a novel prognostic marker of upper tract urothelial carcinoma after radical nephroureterectomy. Front Oncol 2019; 9: 914.

10.

Ding

Feng

JH.

A three-gene novel predictor for improving the prognosis of cervical cancer. Oncol Lett 2019; 18: 4907–4915.

11.

Wang

Gao

, et al. The prognostic landscape of tumor-infiltrating immune cells in cervical cancer. Biomed Pharmacother 2019; 120: 109444.

12.

Scholl

Beal

de Koning

, et al. Genetic markers and phosphoprotein forms of beta-catenin pβ-Cat552 and pβ-Cat675 are prognostic biomarkers of cervical cancer. EBioMedicine 2020; 61: 103049.

13.

Cohen

Jhingran

Oaknin

, et al. Cervical cancer. Lancet 2019; 393: 169–182.

14.

Singh

Baby

Rajguru

, et al. Inflammation and cancer. Ann Afr Med 2019; 18: 121–126.

15.

Coussens

Werb

Inflammation and cancer. Nature 2002; 420: 860–867.

16.

Grivennikov

Greten

Karin

Immunity, inflammation, and cancer. Cell 2010; 140: 883–899.

17.

Buergy

Wenz

Groden

, et al. Tumor-platelet interaction in solid tumors. Int J Cancer 2012; 130: 2747–2760.

18.

Herold

Lohinszky

, et al. Personalized Indicator Thrombocytosis Shows Connection to Staging and Indicates Shorter Survival in Colorectal Cancer Patients with or without Type 2 Diabetes. Cancers (Basel) 2020; 12: 556.

19.

Menczer

Preoperative elevated platelet count and thrombocytosis in gynecologic malignancies. Arch Gynecol Obstet 2017; 295: 9–15.

20.

Rao

Zhang

, et al. Poor prognostic role of the pretreatment platelet counts in colorectal cancer: a meta-analysis. Medicine (Baltimore) 2018; 97: e10831.

21.

Zhou

Liu

, et al. Platelet-to-lymphocyte ratio in advanced cancer: review and meta-analysis. Clin Chim Acta 2018; 483: 48–56.

22.

Liu

The pretreatment platelet-to-lymphocyte ratio predicts clinical outcomes in patients with cervical cancer: a meta-analysis. Medicine (Baltimore) 2018; 97: e12897.

23.

Key

Bradbury

Perez-Cornago

, et al.

Diet, nutrition, and cancer risk: what do we know and what is the way forward?

Br Med J 2020; 368: m511.

24.

Bao

Liu

Lin

, et al. Nutritional assessment and prognosis of oral cancer patients: a large-scale prospective study. BMC Cancer 2020; 20: 146.

25.

Papadimitriou

Muller

van den Brandt

, et al. A nutrient-wide association study for risk of prostate cancer in the European prospective investigation into cancer and nutrition and the Netherlands cohort study. Z Ernahrungswiss 2020; 59: 2929–2937.

26.

Guner

Kim

HI.

ASO Author reflections: parameters for predicting surgical outcomes for gastric cancer patients: simple is better than Complex. Ann Surg Oncol 2018; 25: 699–700.

27.

Man

Chen

YF.

Systemic immune-inflammation index, serum albumin, and fibrinogen impact prognosis in castration-resistant prostate cancer patients treated with first-line docetaxel. Int Urol Nephrol 2019; 51: 2189–2199.

28.

Gui

Wang

Luo

, et al. Platelet to lymphocyte ratio as a prognostic factor in patients with advanced colorectal cancer undergoing palliative treatment. Ann Palliat Med 2020; 9: 3271–3277.

29.

Huang

, et al. Prognostic values of preoperative platelet-to-lymphocyte ratio, albumin and hemoglobin in patients with non-metastatic colon cancer. Cancer Manag Res 2019; 11: 3265–3274.

30.

Liu

Yang

, et al. Preoperative fibrinogen-to-albumin ratio, a potential prognostic factor for patients with stage IB-IIA cervical cancer. BMC Cancer 2020; 20: 691.

31.

Falanga

Marchetti

Vignoli

Coagulation and cancer: biological and clinical aspects. Journal of Thrombosis and Haemostasis : J Thromb Haemost 2013; 11: 223–233.

32.

Levy

Welsby

Goodnough

LT.

Fibrinogen as a therapeutic target for bleeding: a review of critical levels and replacement therapy. Transfusion 2014; 54: 1389–1405; quiz 1388.

33.

Lowe

Rumley

Mackie

IJ.

Plasma fibrinogen. Ann Clin Biochem 2004; 41: 430–440.

34.

Falanga

Schieppati

Russo

Cancer tissue procoagulant mechanisms and the hypercoagulable state of patients with cancer. Semin Thromb Hemost 2015; 41: 756–764.

35.

Cong

Kong

, et al. Fibrinogen is A coagulation marker associated with the prognosis of endometrial cancer. Onco Targets Ther 2019; 12: 9947–9956.

36.

Simpson-Haidaris

Rybarczyk

Tumors and fibrinogen. The role of fibrinogen as an extracellular matrix protein. Ann N Y Acad Sci 2001; 936: 406–425.

37.

Palumbo

Kombrinck

Drew

, et al. Fibrinogen is an important determinant of the metastatic potential of circulating tumor cells. Blood 2000; 96: 3302–3309.

38.

Verheul

van Erp

Homs

, et al. The relationship of vascular endothelial growth factor and coagulation factor (fibrin and fibrinogen) expression in clear cell renal cell carcinoma. Urology 2010; 75: 608–614.

39.

Kwaan

Lindholm

PF.

Fibrin and fibrinolysis in cancer. Semin Thromb Hemost 2019; 45: 413–422.

40.

Peng

Shen

Dong

, et al. Preoperative plasma fibrinogen predicts cervical metastasis in patients with stage I/II carcinoma of the tongue. Int J Oral Maxillofac Surg 2014; 43: 393–398.

41.

Polterauer

Seebacher

Hefler-Frischmuth

, et al. Fibrinogen plasma levels are an independent prognostic parameter in patients with cervical cancer. Am J Obstet Gynecol 2009; 200: 647 e641–647.

42.

Zhao

Deng

Qin

, et al. Prognostic significance of pretreatment plasma fibrinogen and platelet levels in patients with early-stage cervical cancer. Gynecol Obstet Invest 2015; 79: 25–33.

Analysis of systemic inflammatory and coagulation biomarkers in advanced cervical cancer: Prognostic and predictive significance

Abstract

Objective

Methods

Results

Conclusions

Keywords

Introduction

Methods

Patients

Study design and data collection

Statistical analysis

Results

Patient characteristics

Univariate and multivariate analyses for OS

Discussion

Conclusions

Footnotes

Declaration of conflicting interests

Disclosure

Ethics approval and consent to participate

Funding

ORCID iD

References