Lung Cancer: Targets and Therapy

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ORIGINAL RESEARCHOpen Access Full Text Article

New prognostic biomarkers and therapeutic
effect of bevacizumab for patients with
non-small-cell lung cancer
This article was published in the following Dove Press journal:
Lung Cancer: Targets and Therapy
3 August 2017
Number of times this article has been viewed

Maiko Niki
Takashi Yokoi
Takayasu Kurata
Shosaku Nomura
First Department of Internal
Medicine, Kansai Medical University,
Hirakata, Osaka, Japan

Background: Several biomarkers have emerged as potential prognostic and predictive markers
for non-small-cell lung cancer (NSCLC). Successful inhibition of angiogenesis with the antivascular endothelial growth factor antibody, bevacizumab, has improved the efficacy seen withstandard cytotoxic therapy of NSCLC. However, despite such enhanced treatment strategies,
the prognosis for patients with advanced NSCLC remains poor.
Patients and methods: We assessed potential biomarkers in 161 NSCLC patients and 42
control patients. Enzyme-linked immunosorbent assay methods were used to evaluate threebiomarkers: platelet-derived microparticle (PDMP), high-mobility group box-1 (HMGB1),
and plasminogen activator inhibitor-1 (PAI-1). We studied the effects of bevacizumab on theexpression of these markers. We also analyzed the relationship of the newly designed risk factor (NDRF) to overall survival and disease-free survival. The NDRF classification of patientswas determined from the levels of PDMP, HMGB1, and PAI-1. To determine the individual
prognostic power of PDMP, HMGB1, and PAI-1, we evaluated associations between their levels
and patient outcomes by Kaplan–Meier survival analysis in a derivation cohort.
Results: PDMP, HMGB1, and PAI-1 levels were higher in NSCLC patients compared with
control patients. Notably, the difference in PDMP levels exhibited the strongest statistical significance (p<0.001). Multivariate analysis showed that HMGB1 and PAI-1 levels were significantly
correlated with PDMP levels. Patients who received standard chemotherapy with bevacizumab
exhibited significantly reduced levels of all three markers compared with patients who received
standard chemotherapy. NDRF3 status (high levels of all three markers) was significantly correlated with a poor prognosis (p<0.05 for overall survival and disease-free survival).
Conclusion: Our results demonstrate that abnormal levels of PDMP, HMGB1, and PAI-1 are
related to each other in NSCLC. Moreover, our findings suggest that the vascular complications
associated with these markers may contribute to a poor prognosis for NSCLC patients.
Keywords: non-small-cell lung cancer, platelet-derived microparticle, HMGB1, PAI-1,
bevacizumab

Introduction
Correspondence: Shosaku Nomura
First Department of Internal Medicine,Kansai Medical University, 2-3-1 Shinmachi, Hirakata, Osaka 573-1191, Japan
Tel +81 72 804 2754
Fax +81 72 804 2041
Email shossaku-n@mbp.ocn.ne.jp

Eighty-five percent of all lung cancers are non-small-cell lung cancer (NSCLC), andapproximately two-thirds of NSCLCs are at an advanced stage at diagnosis.1 The
current prognosis for patients with advanced NSCLC remains poor despite enhanced
treatment strategies.2,3 Several biomarkers have emerged as potential prognostic andpredictive markers for NSCLC. In particular, epidermal growth factor receptor (EGFR)
has been the subject of intensive research.4 Several studies have shown that mutated
EGFR can be biomarkers for NSCLC.5,6 In addition, anaplastic lymphoma kinase91

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http://dx.doi.org/10.2147/LCTT.S138887

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Niki et al(ALK) mutation is another critical NSCLC biomarker.7 Thesebiomarkers are critical for the selection of the best therapy
for lung cancer patients. Inflammatory or hematologic biomarkers such as high-mobility group box-1 (HMGB1),8 mean
platelet volume (MPV),9,10 neutrophil or platelet/lymphocyte
rates,11,12 and microparticle (MP) levels13,14 also serve as
prognostic and predictive markers for NSCLC. The utilityof an inflammation-related index has also been reported.15
However, the reliability and usefulness of these markers arecontroversial at present.
Current topics in lung cancer therapy are focusing on tyrosine kinase inhibitors (TKIs) and programmed death receptor-1(PD-1) blockers.16 The specific driver mutations and development of small molecular TKIs such as EGFR and ALK arereported.17 Immunotherapy in the form of checkpoint inhibitors also represents a landmark success in NSCLC treatment,
and patients have experienced durable responses with thesetreatments with good tolerability. Nivolumab exerts antitumor
activity by blocking PD-1 on T lymphocytes and is currently
approved as second-line therapy for advanced NSCLC.18
Angiogenesis is particularly critical to tumor growth andmetastatic dissemination, and overexpression of vascularendothelial growth factor (VEGF), an angiogenic growth
factor, has been associated with a poor prognosis in patientswith NSCLC.19,20 Bevacizumab is an anti-VEGF monoclonal
antibody that inhibited tumor-associated angiogenesis in
preclinical and clinical studies.21,22 In the Phase III Eastern
Cooperative Oncology Group E4599 and AVAil in lung trials, the addition of bevacizumab to platinum-based doublet
chemotherapy resulted in significant improvements in theoverall response rate and median progression-free survival.23,24 Bevacizumab demonstrated good therapeutic effectin many clinical trials.23–26 We also previously reported that
pemetrexed and carboplatin plus bevacizumab, followed bymaintenance with pemetrexed and bevacizumab, was effective and tolerable in patients with non-squamous NSCLC.27
Many individuals with cancer are in a hypercoagulable
state, and the elevated risk of thrombosis conferred by hypercoagulativity increases patient morbidity and mortality.28
Cancer patients frequently develop venous thromboembolism(VTE), and various potential predictive biomarkers havebeen evaluated for association with VTE in cancer progression.29–32 For example, analysis of blood cells can effectively
predict the risk of VTE development.30 Additionally, D-dimer,
prothrombin fragment 1+2, and soluble P-selectin levels canaccurately predict VTE risk.32 Furthermore, MP levels arealso an accurate marker of VTE risk.33–35 In particular, theclinical course of blood cancer such as multiple myeloma

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includes cytokines, chemokines, growth factors, vascularendothelial cells, and/or the hemostatic-coagulatory system.36,37 Furthermore, some anticancer drugs are related tocoagulopathy.38,39 For example, bevacizumab may also belinked with the risk of thrombosis.40,41 However, some studies
also reported no relationship between bevacizumab and therisk of VTE in cancer patients.42
In this study, we evaluated several thrombosis-related
markers in NSCLC patients receiving standard chemotherapy
with or without bevacizumab treatment. We also evaluatedthe utility of several thrombosis-related biomarkers for prognostic prediction for NSCLC patients.

Patients and methods
Patients
NSCLC patients and healthy volunteers were recruited from
Kansai Medical Hirakata Hospital and Kansai Medical
University (Osaka, Japan) from September 2011 to October 2015. NSCLC patients were grouped according to theguidelines for tumor-node-metastasis stage I–IV, based on
primary tumor size, lymph node involvement, and distance
between the metastasis and primary tumor.43 This study was
conducted in accordance with the Declaration of Helsinki
and Good Clinical Practice guidelines and approved by theinstitutional review board of Kansai Medical University(Hirakata, Japan). Written informed consent was obtained
from all participants. The primary objective of this study
was the analysis of thrombosis-related biomarkers in NSCLC
patients with or without bevacizumab treatment. The secondary objective was the evaluation of prognostic prediction for
NSCLC patients using a newly designed risk factor (NDRF)
obtained from the results of the primary objective.

Treatments
The present study is an evaluation of thrombosis-related
biomarkers in NSCLC patients treated with standard chemotherapy with or without bevacizumab and is thus not a clinical
trial to evaluate any usefulness regarding therapeutics. The
standard chemotherapy treatment included combination of
the following drugs: carboplatin, pemetrexed, docetaxel,
paclitaxel, gemcitabine, vinorelbine, cisplatin, etoposide,irinotecan, TS-1, and TKIs.

Measurement of biomarkers
All biomarkers were measured at two points: before therapy
and 1 month after therapy. In addition, we measured biomarkers if the patient’s condition took a sudden turn for the worse.
Patient blood samples were collected in plain or sodium

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citrate containing tubes and left at room temperature for aminimum of 1 hour. Serum and citrated plasma were isolated by 20-minute centrifugation at 1,000×g at 4°C. Serumwas divided into aliquots and frozen at −30°C until use.
Human interleukin (IL)-6, monocyte-chemotactic protein(MCP)-1, regulated on activation normally T-cell expressedand secreted (RANTES), soluble vascular cell adhesion
molecule (sVCAM)-1, soluble E (sE)-selectin, angiopoietin(Ang)-2, VEGF, and plasminogen activator inhibitor-1 (PAI1) enzyme-linked immunosorbent assay (ELISA) kits werepurchased from Thermo Fisher Scientific (Waltham, MA,
USA). HMGB1 was measured using the HMGB1 ELISAKit II (Shino-test Corp, Kanagawa, Japan). Serum levels ofcytokines and soluble factors were measured according to
the manufacturer’s instructions. Recombinant products andstandard solutions provided with commercial kits served as
positive controls. Normal ranges were as follows: IL-6: 0.2–
4.5 pg/mL, MCP-1: 170–570 pg/mL, RANTES: 23.9–58.5
ng/mL, sVCAM-1: 395–714 ng/mL, sE-selectin: 23.0–79.2
ng/mL, HMGB1: 1.2–4.8 ng/mL, Ang-2: 500–2,000 pg/mL,
VEGF: 40–500 pg/mL, and PAI-1: 1.1–10.5 ng/mL.

Assessment of platelet-derived
microparticle (PDMP)
Blood samples were collected with a 21-gauge needle froma peripheral vein into vacutainers containing EDTA-ACD(NIPRO Co Ltd, Japan) to minimize platelet activation. The
samples were handled as described in the manufacturer’s
protocol. Briefly, the samples were gently mixed by inverting the tube once or twice, stored at room temperature for
2–3 hours, and centrifuged at 8,000×g for 5 minutes at room
temperature. Storage of samples at room temperature for
2–3 hours did not affect the PDMP level. Immediately after
centrifugation, 200 µL of the upper layer of supernatantsfrom 2-mL samples were collected to avoid contaminationof platelets44,45 and the samples were stored at −40°C until
analysis. PDMP levels were measured in duplicate using an
ELISA kit (JIMRO Co Ltd, Japan) and monoclonal antibodies against glycoprotein CD42b and CD42a.44–48 The range
of normal PDMP values was 3–8 U/mL.

Statistical analysis

Results are shown as the mean ± standard errors. Statisticallysignificant differences between groups were identified using
the chi-square or Student’s t-tests. Correlations between
PDMP level and continuous variables were assessed using
multivariable linear regression analyses. Receiver operating characteristics curve analysis was used to estimate anLung Cancer: Targets and Therapy 2017:8

New prognostic biomarkers and therapeutic effect of bevacizumab

optimal cutoff value for biomarkers. Overall survival (OS)was defined as the time from initial diagnosis to the time ofdeath from any cause or the date the patient was last known
to be alive. Disease-free survival (DFS) was measured fromthe date of diagnosis until the date of disease recurrenceor death, or until the date the patient was last known to bedisease-free. Univariate analyses of OS were performed
using the Kaplan–Meier product-limit method with the logrank test and the Cox proportional hazards model. The 95%
confidence interval (CI) for the survival rate was calculatedusing Greenwood’s method. The Brookmeyer and Crowley
method was used to calculate the 95% CI of the mediansurvival time. p-values <0.05 were considered statisticallysignificant. All analyses were performed using the StatFlex
program (version 6) (Artec Inc, Osaka, Japan).

Results
Clinical characteristics of the study
subjects
A total of 161 NSCLC and 42 control (bronchial asthma
or chronic obstructive pulmonary disease) patients wererecruited for this study. The mean age of the patients was 68
years, with a range of 21–84 years. Among the 161 NSCLC
patients, a total of 106 reported a history of smoking while
the remaining 55 had never smoked. Table 1 details the histological classification of tumors from NSCLC patients. The
performance status (PS) was 0–2 in 121 patients and 3 or 4
in 40 patients. Sixteen patients had stage IIIb disease, and
135 patients had stage IV disease. One hundred and thirty
seven patients had received at least one regimen of systemic
chemotherapy, and 22 patients had received best supportive
care alone.

Levels of various biomarkers in NSCLCWe examined a panel of potential biomarkers for NSCLC in
the NSCLC patient group and the healthy controls. The levelsof IL-6 and MCP-1 were not significantly different between
the two groups (Table 2). However, the levels of RANTES,sVCAM-1, sE-selectin, HMGB1, Ang-2, VEGF, PAI-1, and
PDMP were higher in NSCLC patients compared with control patients. Notably, PDMP levels exhibited the strongest
statistical significance between the two groups (p<0.001).

Variable analysis of various biomarkers
We next used univariate and multivariate regression­analyses to investigate the associations between 15 variables
and PDMP concentration in NSCLC patients (Table 3). Univariate analyses revealed that platelet, RANTES, sVCAM-1,

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Niki et al

Table 1 Clinical characteristics of the study subjects

Table 3 Multiregression analysis on PDMP in NSCLC

Patients (n=161)

Controls (n=42)

n

n

%

Age, years: median (range)

68 (21–84)

Sex (male)
Smokers
Non smokers
Performance status
0–2
3–4
Stage
Illb
IV
Cell type
Adenocarcinoma
Squamous cell carcinoma
Large cell carcinoma
Unknown type

110
106
55

68.3 3065.8 2034.2 22

121
40

75.2
24.8

16
135

9.9
83.9

93
45
14
9

57.8
28.0
8.7
5.6

%

64 (34–81)
71.4
47.6
52.4

Table 2 Levels of soluble factors and microparticles
Cytokine/factor

Control

NSCLCnIL-6 (pg/mL)
MCP-1 (pg/mL)
RANTES (ng/mL)
sVCAM-1 (ng/mL)
sE-selectin (ng/mL)
HMGB1 (ng/mL)
Ang-2 (pg/mL)
VEGF (pg/mL)
PAI-1 (ng/mL)
PDMP (U/mL)

42
12.1±8.8467±16157.1±10.8728±1318
66.5±11.7
7.5±1.8
1,093±398
421±8814.1±4.7
13.5±4.1

161
14.6±19.5529±12774.8±21.2
1,188±397
87.5±20.2
13.8±3.2
1,569±728
584±12824.9±5.631.9±7.3

p-value
NS
NS
<0.05
<0.05
<0.05
<0.01
<0.05
<0.05
<0.01
<0.001

Notes: The p-values are for control versus NSCLC. Data are shown as mean ± SD.
Abbreviations: IL-6, interleukin-6; MCP-1, monocyte chemoattractant protein-1;
RANTES, regulated on activation normally T-cell expressed and secreted; sVCAM-1,
soluble vascular cell adhesion molecule-1; sE-selectin, soluble E-selectin; HMGB1,
high-mobility group box-1; Ang-2, angiopoietin-2; VEGF, vascular endothelial
growth factor; PAI-1, plasminogen activator inhibitor-1; PDMP, platelet-derived
microparticle; NS, not significant; NSCLC, non-small-cell lung cancer; SD, standard
deviation.

sE-selectin, HMGB1, VEGF, and PAI-1 were significantly
associated with PDMP. HMGB1 and PAI-1 levels also significantly correlated with PDMP levels using the multivariate
analysis.

Effects of bevacizumab on biomarkers
We next evaluated the concentrations of IL-6, MCP-1,
RANTES, sE-selectin, sVCAM-1, PDMP, PAI-1, HMGB1,
Ang-2, and VEGF in NSCLC patients treated with standard
chemotherapy with or without bevacizumab (Figure 1). In
the patients who received standard treatment without bevacizumab (n=70), the concentrations of all markers showed nosignificant changes. However, in the patients who received

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AnalysisAge (years)
Sex (men)
Hb (g/d1)
WBC (× 102/µL)
PLT (×104/µL)
CRP (mg/dL)
IL-6 (pg/mL)
MCP-1 (pg/mL)
RANTES (ng/mL)
sVCAM-1 (ng/mL)
sE-selectin (ng/mL)
HMGB1 (ng/mL)
Ang-2 (pg/mL)
VEGF (pg/mL)
PAI-1 (ng/mL)

Univariate

Multivariatebp-value

0.2198–0.0989
0.1774–0.02390.32040.23840.2217
0.1986
0.41490.39650.38720.42890.23830.32910.61420.083840.32457
0.12393
0.28374
0.04841*0.08672
0.09233
0.116410.00342*0.01012*0.00875*
0.00201*
0.08669
0.04564*0.00011*b

p-value

0.25470.063990.25320.23210.27550.31480.05987
0.097620.08391
0.04148*0.2362
0.55810113230.00894*

Notes: b: standardized progression coefficients, *indicates statistical significance..
Abbreviations: IL-6, interleukin-6; MCP-1, monocyte chemoattractant protein-1;
RANTES, regulated on activation normally T-cell expressed and secreted; sVCAM-1,
soluble vascular cell adhesion molecule-1; sE-selectin, soluble E-selectin; HMGB1,
high-mobility group box-1; Ang-2, angiopoietin-2; VEGF, vascular endothelial
growth factor; PAI-1, plasminogen activator inhibitor-1; PDMP, platelet-derived
microparticle; NSCLC, non-small-cell lung cancer; Hb, hemoglobin; WBC, whiteblood cells; CRP, C-reactive protein; PLT, platelet.

standard therapy with bevacizumab (n=91), the concentrations of sE-selectin, sVCAM-1, PDMP, PAI-1, HMGB1,
Ang-2, and VEGF were significantly altered after treatment(n=91). IL-6, MCP-1, and RANTES levels showed no statistically significant difference.

Survival analysis using PDMP, HMGB1,
and PAI-1
We focused our attention on three biomarkers, PDMP,HMGB1, and PAI-1, because of the following reasons: 1)
PDMP levels exhibited the strongest statistically significant
difference between NSCLC patients and controls (p<0.001);
2) HMGB1 and PAI-1 levels in NSCLC patients were significantly correlated with PDMP levels by the multivariate
analysis; and 3) the concentrations of PDMP, PAI-1, and
HMGB1 were also significantly altered after treatment with
bevacizumab.
Therefore, we determined NDRF classification basedon the levels of PDMP, HMGB1, and PAI-1, and analyzed
the contribution of NDRF to OS or DFS. The number of
NSCLC patients with high levels of PDMP, HMGB1, orPAI-1 was 63, 34, or 41, respectively (Table 4). NDRFs
were classified as 0–3 according to the following definitions – NDRF0: patients with low levels of PDMP, HMGB1,
and PAI-1; NDRF1: patients with one factor (HMGB1 or

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New prognostic biomarkers and therapeutic effect of bevacizumab
Before(pg/mL)80(pg/mL)

IL-6

800



60


40
20
0

Bev(–) Bev(+)(U/mL)
80

PDMP(ng/mL)

MCP-1




120

RANTES(ng/mL)(ng/mL)sE-selectin

160



2,000

90

120

400

60

80

1,000

200

30

40

500

0

Bev(–) Bev(+)(ng/mL)60

PAI-1(ng/mL)

Bev(–) Bev(+)

p<0.01900

0(pg/mL)

VEGF

1,200





p<0.05

3,000

30

10

600

2,000

20

15

5

300

1,000

Bev(–) Bev(+)

0

Bev(–) Bev(+)

0

Bev(–) Bev(+)

0

Bev(–) Bev(+)
Ang-2

4,000

40

0

p<0.01

1,500(pg/mL)HMGB1

15
p<0.010

Bev(–) Bev(+)

20

 p<0.001

0sVCAM-1


p<0.05

600

45

60

After

Bev(–) Bev(+)

0

p<0.05Bev(–) Bev(+)

Figure 1 Comparison of biomarkers before and after treatment with or without bevacizumab.
Abbreviations: IL-6, interleukin-6; MCP-1, monocyte chemoattractant protein-1; RANTES, regulated on activation normally T-cell expressed and secreted; sVCAM-1,
soluble vascular cell adhesion molecule-1; sE-selectin, soluble E-selectin; HMGB1, high-mobility group box-1; Ang-2, angiopoietin-2; VEGF, vascular endothelial growth factor;
PAI-1, plasminogen activator inhibitor-1; PDMP, platelet-derived microparticle; NS, not significant; Bev, bevacizumab.

PAI-1) elevated; NDRF2: patients with two factors (PDMP,HMGB1, or PAI-1) elevated; and NDRF3: patients with all
three ­factors elevated (Table 4).
We next evaluated the association between the levels of
PDMP, HMGB1, and PAI-1 and patient outcome using Kaplan–
Meier survival analysis in the derivation cohort to determine
the individual prognostic power of each biomarker. NDRF3
was significantly correlated with a poor prognosis (p<0.05 forboth OS and DFS; Figures 2 and 3). NDRF2 was also associated with an unfavorable OS (p<0.05; Figure 2), but was not
associated with DFS (Figure 3). Neither NDRF1 nor NDFR0
was correlated with either OS or DFS (Figures 2 and 3).
Multivariate analysis clearly revealed that NDRF2 or
NDRF3 was an independent unfavorable prognostic factor for OS (hazard ratio: 1.579, 95% CI: 1.118–2.334,
p=0.0013). In contrast, being female (p=0.0011), having a
PS of 0–2 (p<0.0001), having non-squamous cell carcinoma(p=0.0039), and having stage IIIb disease (p=0.0416) wereindependent favorable prognostic factors (Table 5). Being

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Table 4 NDRF classification using PDMP, HMGB1, and PAI-1
nMarker
PDMP
HMGB1
PAI-1

92
69
122
39
116
45

Low (<30.57)
High (≥30.57)
Low (<12.98)
High (≥12.98)
Low (<23.63)
High (≥23.63)

New index by above markers
NDRF0: no high level of above markers
NDRF1: no fewer than one high level of above markers
NDRF2: no fewer than two high levels of above markers
NDRF3: all high levels of above markers

79
35
24
23

Abbreviations: NDRF, newly designed risk factor; PDMP, platelet-derived
microparticle; HMGB1, high-mobility group box-1; PAI-1, plasminogen activator
inhibitor-1.

younger than 70 years (p=0.3697) was, however, not a significant factor. Furthermore, no significant difference in OS
was observed between patients with and without a historyof smoking (p=0.7649).

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Probability of OS

Niki et al
1.00

NDRF0 (n=79)

0.80

NDRF1 (n=35) Log rank p=0.511 *1
NDRF2 (n=24) Log rank p=0.047 *2
NDRF3 (n=23) Log rank p=0.022 *3

0.60
0.40
0.20
0

0

10

20

30
40
Time (month)

50

60

Probability of DFS

Figure 2 Kaplan–Meier curves for OS of the patients according to NDRF.
Notes: NDRF0: no high level of above markers; NDRF1: no fewer than one highlevel of above markers; NDRF2: no fewer than two high levels of above markers;
NDRF3: all high levels of above markers. *1-3: vs NDRF0.
Abbreviations: NDRF, newly designed risk factor; OS, overall survival.

1.00

NDRF0 (n=79)

0.80

NDRF1 (n=35) Log rank p=0.539 *1
NDRF2 (n=24) Log rank p=0.622 *2
NDRF3 (n=23) Log rank p=0.025 *3

0.60
0.40
0.20
0

0

10

20

30
40
Time (month)

50

60

Figure 3 Kaplan–Meier curves for DFS of patients according to NDRF.
Notes: NDRF0: no high level of above markers; NDRF1: no fewer than one highlevel of above markers; NDRF2: no fewer than two high levels of above markers;
NDRF3: all high levels of above markers. *1-3: vs NDRF0.
Abbreviations: NDRF, newly designed risk factor; DFS, disease-free survival.

Table 5 Multivariate analysis of overall survival
CovariateHR

95% Clp-value

NDRF0 vs NDRF2 or 3Age, ≤70 y vs ≥71 y
Female vs. male
Non smokers vs smoker
ECOG PS 0–2 vs. 3–4
Non-sq vs. sq
Stage Illb vs IV

1.579
0.914
0.529
1.028
0.374
0.593
0.582

1.118–2.334
0.632–1.315
0.375–0.812
0.679–1.536
0.217–0.534
0.296–0.975
0.413–0.872

0.0013*0.31460.0011*0.7649<0.0001*
0.0039*0.0416*

Notes: *p<0.05. NDRF0: no high level of above markers; NDRF2: no fewer than
two high levels of above markers; NDRF3: all high levels of above markers.
Abbreviations: NDRF, newly designed risk factor; ECOG, Eastern Cooperative
Oncology Group; PS, performance status; sq, squamous cell carcinoma; HR, hazard
ratio; CI, confidence interval.

Discussion
Multiple studies have attempted to identify prognostic factors or molecular biomarkers to predict the likelihood oflung cancer metastasis or recurrence. At present, useful96

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prognostic factors include disease staging, PS, histology,
sex, and age.49–51 Additionally, recent efforts have focused on
identifying potential endothelial, hematological, or inflammatory biomarkers for NSCLC.9,13–15,52 We found that levels of
RANTES, sVCAM-1, sE-selectin, HMGB1, Ang-2, VEGF,
PAI-1, and PDMP were higher in NSCLC patients than incontrol individuals. Some of these biomarkers have beenpreviously associated with NSCLC prognosis.8,14,53–55
In the present study, we studied the effect of bevacizumabon the expression of various markers. Bevacizumab improved
PDMP, HMGB1, and PAI-1 levels as well as sE-selectin,sVCAM-1, Ang-2, and VEGF. Angiogenesis is important for
tumor growth and metastasis, with the pro-angiogenic protein,
VEGF, being a major regulator of angiogenesis in normal andmalignant tissues.56–58 The overexpression of VEGF has beencorrelated with a poor prognosis in patients with NSCLC.20
Some reports have shown that bevacizumab may be linked
with a risk of thrombosis.40,41 However, another study also
reported no relationship between bevacizumab and risk of
VTE in cancer patients.42 Our results support the latter because
bevacizumab improved thrombosis-related biomarkers suchas PDMP, HMGB1, and PAI-1. Together, this suggests that
PDMP may be an important biomarker in NSCLC.
PDMP is a platelet-related biomarker with procoagulant
activity and contributes to thrombosis formation and atherosclerosis.44–46 Several associations between platelet-based
parameters and NSCLC progression have been previously
reported.9,10,50 Inagaki et al9 reported that the MPV:platelet
count ratio was closely associated with survival in patientswith advanced NSCLC. Kumagai et al10 reported that a low
MPV predicted an unfavorable prognosis following curative resection of NSCLC. Furthermore, Zhang and Ran51
conducted a meta-analysis and demonstrated that an elevatedplatelet count confers a poor prognosis for patients withlung cancer. Therefore, we investigated whether PDMP or
PDMP-associated biomarkers were associated with NSCLC
prognosis. We investigated 15 variables and their associations
with PDMP in NSCLC patients by multivariable analysis.
Our results showed that levels of HMGB1 and PAI-1 weresignificantly correlated with PDMP levels.
HMGB1 has been previously reported as a potential
prognostic factor for NSCLC.8,53–55 Naumnik et al8 identified increased levels of HMGB1 in patients with advanced
NSCLC undergoing chemotherapy. However, they concluded
that HMGB1 concentration did not influence survival time
following NSCLC treatment because there was no significant
difference in HMGB1 levels before and after chemotherapy.
In contrast, Wang et al53 reported that HMGB1 was highly
expressed in NSCLC and may be valuable as a prognostic
Lung Cancer: Targets and Therapy 2017:8

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and predictive marker for NSCLC. Therefore, the relevanceof HMGB1 in NSCLC prognosis is controversial.
Su et al55 reported that high PAI-1 expression in NSCLC
correlated with a poor prognosis. However, the authors
also observed that PAI-1 correlation is dependent on PAI2. Therefore, the individual relevance of these markers on
NSCLC prognosis remains unclear. Nevertheless, PAI-1 and/or HMGB1 levels may be important prognostic factors forlung cancer.54,55,59
We found that NSCLC patients with combined high levelsof PDMP, HMGB1, and PAI-1 had a poor prognosis. A previous report has described a significant elevation of PDMP inlung cancer.60 However, we did not identify any individual
effects of PDMP, HMGB1, or PAI-1 on NSCLC prognosis.
The level of endothelial cell-derived microparticles(EDMP) has also been identified as a prognostic biomarker
for NSCLC.13,14,60 Fleitas et al13 reported that circulatinglevels of EDMP and circulating endothelial cells correlatewith prognosis, and could be useful prognostic markers forpatients with advanced NSCLC. Consistent with these findings, Wang et al14 suggested that circulating EDMPs may be
a useful biomarker predictive of 1-year mortality in end-stage
NSCLC patients. Furthermore, Tseng et al60 reported that of
all MPs investigated, only an increased level of EDMP was
significantly associated with lung cancer. Unfortunately,
we could not measure EDMP in the present study. Therefore, whether high levels of PDMP, HMGB1, and PAI-1
in NSCLC patients are associated with the level of EDMP
remains unknown.
We propose that HMGB1 plays an important role in therelationships between HMGB1, PDMP, and PAI-1. HMGB1
is a nuclear protein that binds nucleosomes and promotes
DNA bending.61 However, HMGB1 released from intracellular stores into the blood stream also plays a crucial role inthe cellular response to tissue damage.62 HMGB1 expression
is detectable in multiple immune and inflammatory diseases,63and HMGB1 protein is sequestered by thrombomodulin invivo.64 Moreover, HMGB1 stimulates toll-like receptor (TLR)
and receptor for advanced glycation end products (RAGE).
Therefore, HMGB1 can activate platelets through TLR4.
Activation of TLR or RAGE on endothelial cells or platelets
stimulates release of PAI-1 and PDMP into the circulation andpromotes vasculopathy. Therefore, the presence of HMGB1,
PDMP, and PAI-1 could contribute to vascular complications
such as thrombosis formation. As many individuals withcancer are in a hypercoagulable state, the elevated risk ofthrombosis conferred by hypercoagulativity increases patient
morbidity and mortality.20

Lung Cancer: Targets and Therapy 2017:8

New prognostic biomarkers and therapeutic effect of bevacizumab

Our findings have several potential implications. First,
we have shown that a combined increase in the levels of
HMGB1, PDMP, and PAI-1 is related to NSCLC prognosis.
Second, we have described how vascular complications may
result from increased levels of these biomarkers to contributeto poor prognosis for NSCLC. Third, we demonstrated that
bevacizumab improved the levels of HMGB1, PDMP, andPAI-1 after treatment. Nevertheless, our study has some
limitations. We were unable to determine whether any relationship between EDMP and the three biomarkers exists.
Additionally, we did not investigate how different therapeuticstrategies other than bevacizumab, for example, TKIs and
PD blockers, affect the utility of the identified prognostic
markers. Furthermore, the mechanism of the decrease of
the thrombosis-related biomarkers after bevacizumab treatment has not been clarified. Finally, we could not recognize
the relationship between certain clinical proof of vascularcomplications and the elevation of PDMP, HMGB1, andPAI-1. Further confirmation of our observations in prospective studies is necessary.

Acknowledgments
This study was supported in part by a grant from the JapanFoundation of Neuropsychiatry and Hematology Research, aResearch Grant for Advanced Medical Care from the Ministryof Health and Welfare of Japan, and a grant (13670760 to SN)
from the Ministry of Education, Science and Culture of Japan.

Disclosure
The authors report no conflict of interests in this work.

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