The Relationship Between Thrombo-Inflammatory Biomarkers and Cellular Indices of Inflammation in Lymphoma Patients

Abstract

Introduction

Thrombo-inflammatory biomarkers play an important role in the pathogenesis of lymphoma. We aimed to characterize the interrelationship of thrombo-inflammatory biomarkers and blood cellular indices in lymphoma patients.

Materials and Methods

Ninety-eight lymphoma patient samples were collected from Lymphoma Center of Clinic of Hematology, University of Belgrade, Serbia. Normal controls (n = 50) represented plasma from healthy individuals. Plasminogen activator inhibitor (PAI-1), D-Dimer, factor XIII, C-reactive protein (CRP), microparticles (Mp), Von Willebrand factor (vWF), total protein S, urokinase-type plasminogen activator (uPA), tumor necrosis factor (TNFα), β2-glycoprotein I (β2GPI), and fibronectin levels were measured utilizing commercially-available ELISA methods. Thrombin generation profile (TGA) was measured using a fluorometric kinetic assay. Platelets, leukocytes, lymphocytes, and neutrophils were measured in conjunction with the complete blood profile.

Results

Statistically significant differences were noted in levels of PAI-1, D-Dimer, factor XIII, CRP, microparticles, vWF, uPA, TNFα, β2GPI, fibronectin, and TGA when compared to normal (all P values < .001). Platelet to leukocyte ratio (PLA) correlated to TNFα and fibronectin (R = −0.31 and −0.53, respectively) and the platelet to neutrophil ratio (PNR) correlated to factor XIII and β2GPI (R = 0.40 and 0.40, respectively).

Conclusion

Plasma samples from lymphoma patients demonstrated a significantly altered thrombo-inflammatory biomarker profile that has notable correlations to blood cellular indices.

Keywords

lymphoma inflammation thrombosis biomarkers cancer

Introduction

Lymphomas compose a group of hematogenous malignancies of lymphocytic origin and represent approximately 5% of all malignancies in the US.¹ Despite widespread impact, the etiology of most lymphomas has yet to be clearly elucidated, however, various risk factors, such as the thrombo-inflammatory profile of the tumor's microenvironment, have been strongly implicated in mediating both tumor pathogenicity and disease progression due to downstream effects on immune function.^2,3 As a result of the actions of various thrombo-inflammatory biomarkers, both the activation of proto-oncogenes and inactivation of tumor suppressor genes have been observed and suspected of leading to lymphoma progression.⁴

It has previously been established that elevated levels of thrombo-inflammatory biomarkers are associated with lymphoma and play a role in not only the induction of a hyper-coagulable state, but also in the generation of prognostic scores and models.^5,6 In addition, increased complication rate and disease severity risk associated with such hemostatic perturbations in the lymphoma population have been supported through the association of lymphoma risk and biomarker genetic polymorphisms.⁷ Indeed, in analyzing circulating levels of C-reactive protein (CRP), von Willebrand factor (vWF), fibrinogen, phospholipase A2, and factor VIII in lymphoma patients, Menschikowski et al. found patients with elevated levels to have a statistically reduced survival time.⁸

Several other groups have come across similar findings in their studies and generated powerful prognostic information solely utilizing circulating levels of thrombo-inflammatory biomarkers in various lymphoma populations.^9-12 Despite these findings, a thorough prognostic model remains yet to be developed due to various limiting factors in previous studies such as variety of biomarkers studies, population specificities, and a lack of blood cellular indices. Thus, thrombo-inflammatory biomarker profiles composed of a broader range of molecules and their correlating blood cellular indices are needed in order to more fully elucidate and utilize the potential prognostic value of serological lymphoma assays.

In this study, we present findings composed of 98 test and 50 control subjects designed to investigate whether plasma circulating levels of 12 thrombo-inflammatory biomarkers (PAI-1, D-Dimer, factor XIIIa, CRP, Mp, vWF, total protein S, uPA, TNF, β2GPI, fibronectin, and TGA) and their corresponding blood cellular indices (neutrophil to lymphocyte ratio [NLR], platelet to neutrophil [PNR], platelet to leukocyte ratio [PLA] and platelet to lymphocyte ratios [PLR]) are associated with a lymphoma diagnosis.

Patients and Methods

Study Design

In this retrospective study, plasma samples of lymphoma patients were used to investigate the relationship between thromboinflammatory biomarkers and blood cellular indices of inflammation in lymphoma patients with respect to healthy controls.

Patients’ demographic information was collected in addition to lymphoma and malignancy characteristics and categorizes appropriately. Plasma analytes consisted of citrated blood samples from 98 patients with histologically confirmed diagnosis of non-Hodgkin lymphoma (NHL), Hodgkin lymphoma (HL) and Chronic lymphocytic leukemia/Small lymphocytic lymphoma (CLL/SLL). Samples were collected under the IRB approved protocol from the Clinic of Hematology Unit, University of Belgrade, Belgrade, Serbia. Eligibility Criteria included availability of plasma sample, consent to biochemical studies, and diagnosis of histologically confirmed lymphoma. Control measurements were generated by analyzing normal human plasma (NHP) of 25 male and 25 female samples which were obtained from a commercial source (George King Biomedical, Overland park, KS, US.). Normal human pooled plasma was used for referencing purposes.

Laboratory Assays

A fluorogenic substrate method for thrombin generation was used which employed a Fluoroskan Ascent fluorimeter, calibrated automated thrombogram from Diagnostica Stago (Parsippany, NJ, US). Reagents used in this assay included the fluo-substrate, fluo-buffer, tissue factor high reagent (mixture of tissue factor and phospholipids) and a thrombin calibrator. The tissue factor activator used in this study was a recombinant version which is commercially available. The thrombin generation assay was carried out in 96-well Immulon 2HB transparent round bottom plates. Thrombin generation potential was measured in terms of the peak thrombin concentration, lag time, and endogenous thrombin potential (ETP)/area under the curve (AUC). % peak thrombin and % endogenous thrombin potential were calculated.

Quantification of thrombo-inflammatory biomarker levels, such as PAI-1, D-Dimer, factor XIIIa, CRP, Mp, vWF, total protein S, uPA, TNF, β2GPI, and fibronectin, was performed utilizing commercially-available and clinically-approved sandwich ELISA kits by Aniara Diagnostica (West Chester, Ohio, USA), utilizing a SpectraMax plus 384 microplate reader (Molecular Devices, CA, USA) set at 450 nm, as recommended by the manufacturer.

Statistical Analyses

Demographical data were analyzed via descriptive statistics. Blood cellular indices, such as NLR, PNR, PLA, and PLR, were calculated in conjunction with the patients’ complete blood profile via Coulter counter via simple ration generation.

GraphPad Prism software was used in performing statistical analyses, such as one-way ANOVA, Pearson's correlation coefficient, Sidak's multiple comparisons, and calculation of means and standard error of the mean. All conducted statistical analyses were 2-sided with an α of .05.

Results

Patient Demographics

The demographical data of the enrolled patients are summarized in Table 1. Participants were an average of 56.8 ± 12.7 years old at time of initial diagnosis and composed of a relative even split of males (57.1%) and females (42.9%). Within the broader category of lymphoma, the most common histologic subtypes were aggressive Non-Hodgkin Lymphoma (46.9%, NHL), small lymphocytic lymphoma (23.5%, SLL), indolent NHL (17.3%), and Hodgkin Lymphoma (9%).

Table 1.

Participant Demographic Data.

Parameter	Cases (n = 98)	Percent	Mean ± SD (years)
Age at diagnosis			56.8 ± 12.7
0 to 40	13	13.3	32.8 ± 7.3
41 to 60	41	41.8	53.5 ± 5.5
61 to 80	43	43.9	66.7 ± 4.8
80+	1	1.0	82
Sex
Males	56	57.1
Females	42	42.9
Histologic subtype
Aggressive NHL	46	46.9
SLL	23	23.5
Indolent NHL	17	17.3
Hodgkin lymphoma	9	9.2
Other	3	3.1
CCI
0	67	68.37
1	9	9.18
2	8	8.16
3	9	9.18
4	5	5.10

Abbreviation: CCI, Charleson Comorbidity Index; calculated excluding lymphoma as a contributory factor.

These results portray the demographical data of the patient population studied.

Circulating Thrombo-Inflammatory Biomarker Levels

Thrombo-inflammatory biomarker values of percent change from normal values were calculated as 103.74% (PAI-1), 350.53% (D-Dimer), −43.42% (factor XIIIa), 2442.64% (CRP), −38.84% (Mp), 267.38% (vWF), −4.61% (total protein S), −27.86% (uPA), 740.84% (TNFα), −4.04% (β2GPI), −51.48% (fibronectin), and −58.10% (TGA). Figure 1 depicts Tukey box and whisker plots for individual biomarkers in terms of percent change in lymphoma patients compared to normal. Table 2 shows the composite results these in terms of Mean ± 1 standard deviation of 98 lymphoma patients and 50 normal plasma samples. The percent change values indicate the relative change of the biomarkers in comparison to the normal.

Figure 1.

Tukey box and whisker plots of thrombo-inflammatory biomarker percent change values in lymphoma patients (A-L).

Table 2.

Distribution of Thrombo-inflammatory Biomarker in Normal and Lymphoma Patients.

Distribution	Mean (patients)	SD (patients)	Mean (normals)	SD (normals)	Percent change	P-value
PAI-1 (ng/ml)	21.331	28.17	10.468	13.59	103.785	.852
DDimer (ng/ml)	868.616	1197.47	192.804	210.60	350.517	.000
XIII-A (%)	87.952	25.68	155.444	48.07	−43.419	<.0001
CRP (ug/ml)	16.019	27.61	0.628	0.98	2450.331	.002
Microparticles (nM)	16.025	18.22	26.197	12.31	−38.830	.499
vWF (%)	260.507	197.62	70.909	21.80	267.383	.004
Total Protein S (%)	94.518	30.39	99.093	10.23	−4.616	>.9999
uPA (ng/ml)	0.584	0.32	0.813	0.45	−28.083	0.964
TNF-a (pg/ml)	15.556	20.08	1.851	1.18	740.296	.001
B2GPI (ng/ml)	34.564	12.95	36.023	4.57	−4.048	>.9999
Fibronectin (ug/ml)	105.770	107.65	217.394	54.58	−51.346	.622
Peak Thrombin (nM)	113.706	53.40	271.400	data unavailable	−58.104	<.0001

These results represent Mean ± standard deviation of 98 lymphoma patients and 50 normal plasma samples. The percent change values indicate the relative change of the biomarkers in comparison to the normal.

As shown in Table 2, Sidak's multiple comparisons test demonstrated statistical significance in the differences of D-Dimer, factor XIIIa, CRP, vWF, TNFα, and TGA levels between lymphoma patients and normal (all P values ≤ .001).

Blood Cellular Indices Compared to Thrombo-Inflammatory Biomarker

Measured blood cellular parameters were found to be 234.32 ± 131.50 × 10⁹/L (platelets), 32.34 ± 58.31 × 10⁹/L (leukocytes), 21.17 ± 8.40 × 10⁹/L (neutrophils), and 6.93 ± 7.49 × 10⁹/L (lymphocytes) with calculated percent change values of −14.79%, 361.95%, 30.94%, and −28.60%, respectively. Platelet to leukocyte ratio (PLA), PLR, PNR, and NLR values of 28.98 ± 23.11, 21.09 ± 28.53, 4.52 ± 2.19, and 4.68 ± 5.18, respectively, were generated.

Table 3 shows the relationship between thrombo-inflammatory biomarker levels and blood cellular indices. Pearson product-moment correlation coefficients suggested an interrelationship between PLA and TNFα and fibronectin (R values = −0.31 and −0.53, respectively) and PNR and factor XIII and B2GPI (R values = 0.40 and 0.40, respectively).

Table 3.

Blood Cellular Indices and Biomarker Correlations in Lymphoma Patients.

R-Values	Platelet:Leukocyte	Platelet:Lymphocyte	Platelet:Neutrophil	Neutrophil:Lymphocyte
PAI-1 (ng/ml)	0.06	0.00	0.04	−0.01
D-Dimer (ng/ml)	0.09	0.03	−0.02	0.06
XIIIa (%)	0.14	0.14	0.40	0.04
CRP (ug/ml)	−0.03	0.16	−0.01	0.17
Microparticles (nM)	−0.01	−0.11	0.02	−0.12
vWF (%)	−0.19	0.08	−0.09	0.15
Total Protein S (%)	0.14	0.21	0.15	0.16
uPA (ng/ml)	0.02	0.01	0.22	−0.06
TNF-a (pg/ml)	−0.31	−0.11	−0.05	−0.10
B2GPI (ng/ml)	0.10	−0.01	0.40	−0.13
Fibronectin (ng/ml)	−0.53	0.05	0.22	−0.03
Peak Thrombin (nM)	0.13	0.12	0.09	0.14

Various blood cellular indices were correlated with the individual biomarkers in terms of Pearson correlation coefficients.

Discussion

Thrombo-Inflammatory Biomarkers

While the lymphoma subgroup of this malignancy represents a large and heterogeneous class of cancers with varying presentations and degrees of aggressiveness, its propensity for eliciting a hypercoagulable state has been repeatedly demonstrated as a unifying factor.^13-16 To further elucidate the lymphoma thrombo-inflammatory microenvironment, we analyzed the interrelationships between not only multiple biomarkers, but also various blood cellular indices in order to generate a more holistic lymphoma inflammatory and hypercoagulable profile. In a Serbian population of lymphoma patients, data showed thrombo-inflammatory variations in the form of elevated plasma values of D-Dimer, CRP, vWF, TNFα, neutrophils, and leukocytes and decreased values of factor XIII, platelets, and lymphocytes, overall suggesting a predominantly thrombotic state. Interestingly, the thrombin generation parameters were reduced in the lymphoma cohort, presumably due to the ongoing progressive consumption of coagulation factors due to hypercoagulable state, resulting in this observed change.

The thrombotic component of the overall plasma biomarker profile has previously been extensively documented and our results suggest similar tumor microenvironment characteristics.^17-19 D-Dimer values were elevated by 350.53% and had a mean value of 868.62 ± 1197.47 ng/mL, suggesting both a much higher mortality risk per double increase of D-dimer levels (1.5 hazard ratio, P = .001) and a 22% chance of developing a venous thromboembolism (VTE) over a period of 6 months (P = .0033).^20,21

Similarly, elevated levels of CRP, a major biomarker of inflammation, also contribute to the thrombo-inflammatory nature of lymphoma as shown in previous studies and reaffirmed in the current study (mean = 16.02 ± 27.61 µg/mL).⁵ As a common endpoint in the mechanistic actions of pro-inflammatory cytokines such as interleukin-1 and TNFα, CRP has been implicated in playing a major role in the poor prognosis and systemic inflammatory response associated with lymphoma.^22,23 Furthermore, multivariate analysis of CRP values ≥ 10 µg/mL were found to be independently associated with a significantly reduced overall survival (OS) (relative risk = 1.952, P = .012).⁵ Lymphoma patients are susceptible to infections due to their immuno-compromised status and it is likely that, in this lymphoma cohort, some of these patients have subclinical infection which may have contributed to the elevated levels of CRP.

In regard to TNFα, a key endogenous factor and major inflammatory cytokine, substantial evidence has been generated implicating its expression with extensive and pronounced contribution to the lymphoma-associated thrombo-inflammatory state, poor prognosis, and reduced progression-free survival.^3,24 In the Serbian cohort, TNFα expression was markedly increased in comparison to the control group (740% ± 1085.28%), suggesting a sizable contribution to the pro-inflammatory state of lymphoma by TNFα. However, levels of TNFα in lymphoma have been associated with a broad spectrum of tumoral effects ranging from neoplasm regression to metastasis promotion.^3,25,26

A key coagulation mediator and marker of thrombosis, vWF, serves a crucial role in hemostasis and, in the pathogenic state, has been shown to directly promote thrombotic processes that contribute to arterial and venous thrombosis risk.^27,28 Indeed, in a prospective study of 19 237 individuals with a median follow-up of 7.8 years, values of vWF ≥ 204% activity demonstrated a hazard ratio of 7.6 for developing VTE.²⁹ Comparatively, this study's cohort demonstrated a mean vWF percent activity of 260.51 ± 197.62, further potentiating the pathogenic involvement of vWF in both promoting and maintaining a thrombo-inflammatory state.

Regarding the thrombin generation parameters, peak thrombin concentration was found to be lower (113.71 ± 53.40 nM) in the lymphoma cohort compared to healthy individuals (271.4 nM). This is inconsistent with a previously reported healthy cohort, with a much higher mean of 271.4 nM.³⁰ The increase in various biomarkers of thrombin generation and a decreased generation of thrombin in lymphoma patients are consistent with previously established findings which indicated the lymphoma tumor microenvironment is associated with mediators of thrombosis with a decreased thrombogenesis.³¹ In addition, increased thrombin has also extensively been associated with the propagation of both tumor growth and metastasis.³²

With respect to factor XIIIa, this study's cohort demonstrated a mean percent activity of 87.95 ± 25.68 compared to normals’ mean percent activity of 155.44 ± 48.07 (−43.42 ± 16.52% change, P < .0001). Conventionally, a decrease in plasma factor XIIIa suggests a quantitative biomarker dynamics deviation towards hypocoagulability, i.e a reduced potential for the stabilization of fibrins in the final step of the coagulation pathway; however, factor XIIIa deficiency classification is not met until percent activity declines to less than 1%.^33,34 Thus, despite significant differences among the two groups, the relatively preserved percent activity of factor XIIIa within the lymphoma group suggests adequate clot stabilization potential, however, there is an inherent lack in previous studies which have explored this phenomenon in lymphoma patients. Limited data generated by Adany et al. suggested tumor-associated macrophages containing factor XIII to exist as a non-homogeneous population with varying protein expressivity.³⁵

Blood Cellular Indices

To further determine the thrombo-inflammatory interactions in lymphoma patients, we explored the associations between blood cellular indices ratios and biomarker levels which revealed an inverse relationship between the PLA and TNFα and fibronectin (R values = −0.31 and −0.53, respectively). In accordance with previous findings and the current study, the PLA has been associated with an increased release of pro-inflammatory and coagulation factors.^36-39 Notably, the PLA is negatively correlated to the also pro-inflammatory and coagulatory TNFα, however, this seemingly paradoxical relationship likely originates from the notion that consensus regarding TNFα‘s conclusive role in lymphoma has yet to be reached.^3,25,26 Additionally, this unexpected inverse relationship is further rationalized by the inherent nature of a reduced platelet count, and thus PLA, in lymphoma and concomitant increase in TNFα expression as demonstrated by the current study. In regards to the inverse relationship between PLA and fibronectin, previous studies have demonstrated high fibronectin expression with an increased OS (6.3 years vs 3.6 years, P = .05), thus generating the result of the current study.⁴⁰

The interplay between the PNR and factor XIII and β2GPI (R values = 0.40 and 0.40, respectively) are supported by the related mechanisms each component participates in throughout the pro-inflammatory and coagulant cascade.⁴¹ More specifically, the positive correlation between the PNR and factor XIIIa is corroborated through the positive associations between an increasing platelet count, and thus PNR, with an increase in thrombotic state and subsequent clot-stabilizing action of factor XIIIa.^42,43 Furthermore, the positive correlation between the PNR and its thrombo-inflammatory nature and the pro-coagulant properties of β2GPI, although still not fully understood, have been upheld by several groups; indeed, in addition to its plasminogen activating abilities, β2GPI has been shown to propagate the thrombotic cascade via indirect inhibition of protein C activation and interruption of anticoagulant Annexin V shield as well as directly interfering with thrombomodulin function.^44-46

Study Parameters

In designating the limitations and criteria of the various parameters assessed throughout this study, we aimed to strike a balance between exceeding the limitations of previously published works and maintaining overall feasibility. Consequently, while this study more holistically integrates different aspects of the thrombo-inflammatory state of lymphoma patients than previous works, subsequent to financial, temporal, and overall pragmatic boundaries, inherent limitations exist which confine the generality and scope of the current study.

We intended to assess the lymphoma thrombo-inflammatory state circulating biomarker levels in conjunction with blood cellular indices of inflammation using a more comprehensive and inclusive approach than previously done. This combinatorial approach allowed us to more aptly dissect the components responsible for the lymphoma thrombo-inflammatory state than previous studies which incurred limitations such as fewer biomarkers, limited blood cellular indices, a smaller patient population, or a combination thereof.^8,47-52 We believe the integration of parameters set within our design allowed us to more thoroughly depict the lymphoma state such that we were able to both uncover thrombo-inflammatory links within blood plasma measurements in lymphoma patients and further solidify the basis for future studies.

As a consequence of our comprehensive plasma analysis, we encountered various limitations in this study. Similar to the barriers reported by other groups, our study encountered marked variance within many measurements due to the limited sample size.^53-56 From a demographic standpoint, the lymphoma cohort was older than the control group, which is due to the fact that control plasma samples were obtained from a commercial source. An age-match control group with other balanced parameters is more appropriate for such a comparison which should be considered for future studies. Additionally, the high variance observed within our samples is, moreover, due to the heterogeneity of lymphoma sub-types observed in this cohort which consisted of both Hodgkin's and non-Hodgkin's lymphoma. In addition, with the ever-growing list of markers involved in the inflammatory process, our relatively inclusive list of 12 biomarkers is, in reality, limited based on current technological abilities and, in comparison to select previous studies, would benefit from expansion.⁴⁷

Conclusion

In conclusion, the overall relationship between thrombo-inflammatory biomarkers and blood cellular indices of inflammation in lymphoma patients is composed of many dynamic components, several of which partake in multiple processes. In comparison to normals, the thrombo-inflammatory biomarker profile and blood cellular indices in lymphoma patients demonstrated a close interrelationship which functioned to highlight the significant plasma alterations observed. These findings warrant further clinical outcome analysis in a larger, stratified lymphoma population cohort.

Footnotes

Acknowledgments

The authors would like to acknowledge the faculty of The Hemostasis and Thrombosis Research Unit, Loyola University Health Science Division. In addition, we thoroughly appreciated the efforts put forth by the nursing and laboratory staff of the lymphoma center, University of Belgrade, Belgrade Serbia. Dr Paul Riley, of Diagnostica Stago (Paris, France), is gratefully acknowledged for generously providing us with the instrument and reagents for the thrombin generation studies. We also acknowledge the support of Dr Eva Wojcik, Chairperson, Department of Pathology and Laboratory Medicine. Finally, the authors acknowledge the guidance and support of vice provost of research, Dr Meharvan Singh, for enhancing the research program for students at Loyola University.

Author Contributions

(I) Conception and design: All authors. (II) Administrative support: Darko Antic, MD, PhD, Debra Hoppensteadt, PhD, and Jawed Fareed, PhD. (III) Provision of study materials or patients: All authors. (IV) Collection and assembly of data: MD, Mark Jaradeh, BS, Nausheen Baig, BA, Darko Antic, MD, PhD. (V) Data analysis and interpretation: Mark Jaradeh, BS, Nausheen Baig, BA. (VI) Manuscript writing: All authors. (VII) Final approval of manuscript: All authors.

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.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

Ethical Approval

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by institutional review board of Loyola University Medical Center (NO.211663: the registration number of ethics board) and individual consent for this retrospective analysis was waived.

ORCID iDs

Mark Jaradeh

Emily Bontekoe

Debra Hoppensteadt

Jawed Fareed

References

Mugnaini

Ghosh

. Lymphoma. Prim Care: Clin Off Pract. 2016;43(4):661-675.

Balkwill

Mantovani

. Inflammation and cancer: back to Virchow? The Lancet. 2001;357(9255):539-545.

Mantovani

Allavena

Sica

, et al. Cancer-related inflammation. Nature. 2008;454(7203):436-444.

Gaidano

Dalla-Favera

. Biologic and molecular characterization of non-Hodgkin’s lymphoma. Curr Opin Oncol. 1993;5(5):776-784.

Y-J

Z-M

Xia

, et al. Serum C-reactive protein (CRP) as a simple and independent prognostic factor in extranodal natural killer/T-cell lymphoma, nasal type. PLoS ONE. 2013;8(5):e64158.

Sase

Wada

Yamaguchi

, et al. Haemostatic abnormalities and thrombotic disorders in malignant lymphoma. Thromb Haemost. 2005;93(1):153-159.

Skibola

Curry

Nieters

. Genetic susceptibility to lymphoma. Haematologica. 2007;92(7):960-969.

Menschikowski

Hagelgans

Schuler

, et al. Plasma levels of phospholipase A2-IIA in patients with different types of malignancies: prognosis and association with inflammatory and coagulation biomarkers. Pathol Oncol Res. 2013;19(4):839-846.

Seymour

Talpaz

Cabanillas

, et al. Serum interleukin-6 levels correlate with prognosis in diffuse large-cell lymphoma. JCO. 1995;13(3):575-582.

10.

Salles

Bienvenu

Bastion

, et al. Elevated circulating levels of TNF and its p55 soluble receptor are associated with an adverse prognosis in lymphoma patients. Br J Haematol. 1996;93(2):352–359.

11.

Salven

Orpana

Teerenhovi

, et al. Simultaneous elevation in the serum concentrations of the angiogenic growth factors VEGF and bFGF is an independent predictor of poor prognosis in non-Hodgkin lymphoma: a single-institution study of 200 patients. Blood. 2000;96(12):7.

12.

Niitsu

Okamoto

Nakamine

, et al. Simultaneous elevation of the serum concentrations of vascular endothelial growth factor and interleukin-6 as independent predictors of prognosis in aggressive non-Hodgkin’s lymphoma: serum VEGF and IL-6 in aggressive NHL. Eur J Haematol. 2002;68(2):91-100.

13.

Maggio

van den Berg

Diepstra

, et al. Chemokines, cytokines and their receptors in Hodgkin’s lymphoma cell lines and tissues. Ann Oncol. 2002;13(1):52-56.

14.

Teruya-Feldstein

Tosato

Jaffe

. The role of chemokines in Hodgkin’s disease. Leuk Lymphoma. 2000;38(3-4):363-371.

15.

Schmitz

Stanelle

Hansmann

M-L

, et al. Pathogenesis of classical and lymphocyte-predominant Hodgkin lymphoma. Annu Rev Pathol Mech Dis. 2009;4(1):151-174.

16.

Gruss

H-J

Pinto

Duyster

, et al. Hodgkin’s disease: a tumor with disturbed immunological pathways. Immunol Today. 1997;18(4):156-163.

17.

Pulivarthi

Gurram

. Effectiveness of D-dimer as a screening test for venous thromboembolism: an update. N Am J Med Sci. 2014;6(10):491-499.

18.

Geng

Y-d

Chen

Y-R

Jin

, et al. Prognostic value of D-dimer in patients with diffuse large B-cell lymphoma: a retrospective study. Curr Med Sci. 2019;39(2):222-227.

19.

Liu

Zhou

, et al. Prognostic value of pretreatment plasma D-dimer levels in patients with diffuse large B cell lymphoma (DLBCL). Clin Chim Acta. 2018;482:191-198.

20.

Dunkler

Pirker

, et al. High D-dimer levels are associated with poor prognosis in cancer patients. Haematologica. 2012;97(8):1158-1164.

21.

Arpaia

Carpenedo

Verga

, et al. D-dimer before chemotherapy might predict venous thromboembolism. Blood Coagul Fibrinolysis. 2009;20(3):170-175.

22.

Cao

Shi

Y-x

Chen

J-o

, et al. Serum C-reactive protein as an important prognostic variable in patients with diffuse large B cell lymphoma. Tumor Biol. 2012;33(4):1039-1044.

23.

Allin

Nordestgaard

. Elevated C-reactive protein in the diagnosis, prognosis, and cause of cancer. Crit Rev Clin Lab Sci. 2011;48(4):155-170.

24.

Szlosarek

Balkwill

. Tumour necrosis factor α: a potential target for the therapy of solid tumours. Lancet Oncol. 2003;4(9):565-573.

25.

Ferraro

Leonardi

Convertino

, et al. Is there a risk of lymphoma associated with anti-tumor necrosis factor drugs in patients with inflammatory bowel disease? A systematic review of observational studies. Front Pharmacol. 2019;10(247).

26.

Herrinton

Liu

Weng

, et al. Role of thiopurine and anti-TNF therapy in lymphoma in inflammatory bowel disease. Am J Gastroenterol. 2011;106(12):2146-2153.

27.

Kawecki

Lenting

Denis

. Von Willebrand factor and inflammation. J Thromb Haemost. 2017;15(7):1285-1294.

28.

Calabrò

Gragnano

Golia

, et al. Von Willebrand factor and venous thromboembolism: pathogenic link and therapeutic implications. Semin Thromb Hemost. 2018;44(3):249-260.

29.

Tsai

Cushman

Rosamond

, et al. Coagulation factors, inflammation markers, and venous thromboembolism: the longitudinal investigation of thromboembolism etiology (LITE). Am J Med. 2002;113(8):636-642.

30.

Ząbczyk

Królczyk

Czyzewicz

, et al. Altered fibrin clot properties in advanced lung cancer: strong impact of cigarette smoking. Med Oncol. 2019;36(4):37.

31.

Rickles

Patierno

Fernandez

. Tissue factor, thrombin, and cancer. Chest. 2003;124(3):58S-68S.

32.

Reddel

Tan

Chen

. Thrombin generation and cancer: contributors and consequences. Cancers (Basel). 2019;11(1):100.

33.

Sawai

Yamanaka

Nomura

. Clinical significance of factor XIII activity and monocyte-derived microparticles in cancer patients. Vasc Health Risk Manag. 2020;16:103-110.

34.

Muszbek

Bereczky

Bagoly

, et al. Factor XIII: a coagulation factor with multiple plasmatic and cellular functions. Physiol Rev. 2011;91(3):931-972.

35.

Adány

Nemes

Muszbek

. Characterization of factor XIII containing macrophages in lymph nodes with Hodgkin’s disease. Br J Cancer. 1987;55(4):421-426.

36.

Liu

Yang

, et al. Platelet-leukocyte aggregate is associated with adverse events after surgical intervention for rheumatic heart disease. Sci Rep. 2019;9(1):13069.

37.

Cerletti

Tamburrelli

Izzi

, et al. Platelet-leukocyte interactions in thrombosis. Thromb Res. 2012;129(3):263-266.

38.

Cerletti

de Gaetano

Lorenzet

. Platelet – leukocyte interactions: multiple links between inflammation, blood coagulation and vascular risk. Mediterr J Hematol Infect Dis. 2010;2(3):e2010023.

39.

Jensen

De Nully Brown

Lund

, et al. Increased circulating platelet-leukocyte aggregates in myeloproliferative disorders is correlated to previous thrombosis, platelet activation and platelet count: platelet-leukocyte aggregates in myeloproliferative disorders. Eur J Haematol. 2001;66(3):143-151.

40.

Brandt

Montagna

Georgis

, et al. The combined expression of the stromal markers fibronectin and SPARC improves the prediction of survival in diffuse large B-cell lymphoma. Exp Hematol Oncol. 2013;2(1):27.

41.

Gao

Xie

, et al. B2-glycoprotein I is a cofactor for t-PA–mediated plasminogen activation. Arthritis Rheum. 2009;60(2):559-568.

42.

Rubio-Jurado

Iñiguez-Franco

Reyes

, et al. The clinical significance of coagulation and the inflammatory response in autoimmunity. Clinic Rev Allerg Immunol. 2012;42(2):172-180.

43.

Koupenova

Kehrel

Corkrey

, et al. Thrombosis and platelets: an update. Eur Heart J. 2017;38(11):785–791.

44.

Rahgozar

Giannakopoulos

Yan

, et al. Beta2-glycoprotein I protects thrombin from inhibition by heparin cofactor II: potentiation of this effect in the presence of anti–B2-glycoprotein I autoantibodies. Arthritis Rheum. 2008;58(4):1146-1155.

45.

Keeling

Wilson

Mackie

, et al. Role of beta 2-glycoprotein I and anti-phospholipid antibodies in activation of protein C in vitro. J Clin Pathol. 1993;46(10):908-911.

46.

McDonnell

Wincup

Buchholz

, et al. The role of beta-2-glycoprotein I in health and disease associating structure with function: more than just APS. Blood Rev. 2020;39.

47.

Purdue

Hofmann

Kemp

, et al. A prospective study of 67 serum immune and inflammation markers and risk of non-Hodgkin lymphoma. Blood. 2013;122(6):951-957.

48.

Conroy

Maskarinec

Morimoto

, et al. Non-Hodgkin lymphoma and circulating markers of inflammation and adiposity in a nested case-control study: the multiethnic cohort. Cancer Epidemiol Biomarkers Prev. 2013;22(3):337-347.

49.

Purdue

Lan

Bagni

, et al. Prediagnostic serum levels of cytokines and other immune markers and risk of non-Hodgkin lymphoma. Cancer Res. 2011;71(14):4898-4907.

50.

Kim

. The serum CXCL13 level is associated with the Glasgow Prognostic Score in extranodal NK/T-cell lymphoma patients. J Hematol Oncol. 2015;8(49).

51.

Hong

Ryu

Lee

, et al. Serum level of CXCL10 is associated with inflammatory prognostic biomarkers in patients with diffuse large B-cell lymphoma: serum CXCL10 in DLBCL. Hematol Oncol. 2017;35(4):480-486.

52.

Hara

Ando

Tsuchiya

, et al. Serum hepcidin-25 level linked with high mortality in patients with non-Hodgkin lymphoma. Ann Hematol. 2015;94(4):603-608.

53.

Shore

Arslan

, et al. Circulating cytokines and risk of B-cell non-Hodgkin lymphoma: a prospective study. Cancer Causes Control. 2010;21(8):1323-1333.

54.

Miyata-Takata

Takata

Toji

, et al. Elevation of serum interleukins 8, 4 and 1β levels in patients with gastrointestinal low-grade B-cell lymphoma. Sci Rep. 2015;5(1):18434.

55.

Guney

Soydinc

Basaran

, et al. Serum levels of interleukin-6 and interleukin10 in Turkish patients with aggressive non-Hodgkin’s lymphoma. Asian Pac J Cancer Prev. 2009;10(4):669–674.

56.

Anber

EL-Sebaie

Darwish

NHE

, et al. Prognostic value of some inflammatory markers in patients with lymphoma. Biosci Rep. 2019;39(3):BSR20182174.