Open Access

NRP‑1 and KDR polymorphisms are associated with survival time in patients with advanced gastric cancer

  • Authors:
    • Yue‑Jian Zhuo
    • Yu Shi
    • Tao Wu
  • View Affiliations

  • Published online on: September 10, 2019     https://doi.org/10.3892/ol.2019.10842
  • Pages: 4629-4638
  • Copyright: © Zhuo et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Neuropilin‑1 (NRP‑1), a member of the NRP‑family, has been reported to be vital for tumor angiogenesis, growth and metastasis. As a co‑receptor of vascular endothelial growth factor (VEGF), NRP‑1 can bind to VEGF and meditate vascular development through the VEGF‑VEGF receptor 2 (VEGFR2) signaling pathway. Furthermore, NRP‑1 is capable of binding with platelet‑derived growth factor (PDGF) to regulate the PDGF‑PDGF receptor (PDGR) signaling pathway in tumor angiogenesis. In the present study, The DNA was obtained from the paraffin‑embedded tissues of patients with advanced gastric cancer (AGC), amplified using PCR and subsequently sequenced to determine the polymorphisms within NRP‑1, VEGFR2 [kinase insert domain receptor (KDR)] and PDGF. The effect of the functional polymorphism of the aforementioned genes on the overall survival (OS) and progression‑free survival (PFS) of 81 patients with advanced gastric cancer was examined. Three single nucleotide polymorphisms (SNPs) of KDR were significantly associated with clinical outcomes. The rs1870377 TT genotype was positively associated with longer OS and PFS times compared with the AA+AT genotype (PFS, P=0.012; OS, P=0.038), the rs7692791 wild‑type TT genotype was positively associated with longer PFS time and the rs2034965 AA+GA genotype was associated with shorter OS time (P=0.034). With regards to the SNPs of NRP‑1, the rs2065364 AA genotype was significantly associated with improved OS and PFS times (PFS, P=0.023; OS, P=0.045). Following multivariate analysis using Cox proportional hazards regression models, patients with the KDR rs7692791 TT genotype experienced a longer PFS time compared with those with the CT genotype (P=0.016), and patients with the NRP‑1 rs2065364 variant‑type AA genotype still experienced a longer PFS time compared with those patients with the AG+GG genotypes (P=0.006). Regarding OS, the results demonstrated that the KDR rs2034965 AG+GG genotypes presented with a significant reduction in OS time (P=0.029), and that the KDR rs1870377 AT+AA genotypes had worse OS times compared with the wild‑type TT genotype (P=0.021). In addition, increased mortality risk and AGC progression were significantly associated with the number of adverse alleles for combinations of NRP‑1 rs2065364 and KDR rs1870377. In conclusion, the data from the present study demonstrated that the selected KDR and NRP‑1 gene polymorphisms may be potential prognostic biomarkers in AGC.

Introduction

Gastric cancer is one of the most common cancer types worldwide and remains the third leading cause of cancer-associated mortality, accounting for >783,000 deaths worldwide in 2018 (1). The diagnostic rate of early gastric cancer is only 10% in China, and most patients are at an advanced stage when clinically diagnosed, which confers a poor prognosis (2).

Systematic chemotherapy is the major treatment for patients with advanced gastric cancer (AGC). Platinum-fluoropyrimidine- and paclitaxel-fluoropyrimidine-based chemotherapy regimens are recommended as the first-line treatments in line with the Chinese Society of Clinical Oncology guidelines (3).

However, patients with the same tumor stage and receiving similar treatment can exhibit different clinical outcomes, and gastric cancer is a complex disease and its prognosis and progression are significantly affected by genetic and environmental factors (4). Identifying predictive genetic biomarkers could therefore contribute to the development of individualized therapy and follow-up strategies (5).

Neuropilin-1 (NRP-1) is a type I transmembrane glycoprotein distributed at the surface of cells that has been reported to affect neuronal axon guidance and embryonic angiogenesis (6), and to serve as a co-receptor regulating tumorigenesis in the vascular endothelial growth factor (VEGF)-VEGF receptor 2 (VEGFR2) [kinase insert domain receptor (KDR)] or platelet-derived growth factor (PDGF)-PDGF receptor (PDGFR) signaling pathways (7,8). VEGFRs are a type of tyrosine kinase receptor, and include VEGFR1 and VEGFR2 (KDR), which can be activated by binding with VEGF ligands (9). The VEGF-VEGFR2 signaling pathway is the leading pathway that activates the proliferation and migration of endothelial cells, therefore promoting angiogenesis and stimulating tumor growth and invasion (10,11). Furthermore, PDGF isoforms can transduce signals via binding to structurally similar α- and β-tyrosine kinase receptors, known as PDGFRα and PDGFRβ, respectively. The PDGF-PDGFR signaling pathway serves critical roles in regulating proliferation and survival of certain cell types (e.g. hematopoietic stem cell, vascular endothelial cell and vascular smooth muscle cell) during embryogenesis, and overexpression or mutation of the PDGF-PDGFR pathway can stimulate tumor cell proliferation (12,13). Previous studies have reported that polymorphisms within VEGF and KDR impacted their expression at the gene level (14,15). Thus, polymorphisms of these two signaling pathways may affect AGC prognosis by regulating the expression of the aforementioned genes and therefore affect the survival of patients with AGC. The present study investigated the association between polymorphisms of the NRP-1, KDR, PDGFβ, PDGFRβ and PDGFRα genes and the prognosis of patients with AGC.

Materials and methods

Study population

A total of 100 patients with AGC from the Second Affiliated Hospital of Dalian Medical University (Dalian, Liaoning, China) were recruited between January 2011 and June 2016. The inclusion criteria were as follows: i) Patients were histopathologically diagnosed with gastric adenocarcinoma; ii) patients had inoperable locally advanced, metastatic or recurrent gastric cancer (AGC); iii) patients had an Eastern Cooperative Oncology Group performance status (ECOG-PS) ≤2 (16); and iv) patients underwent at least 2 cycles of chemotherapy at the Second Affiliated Hospital when diagnosed with postoperative recurrence or inoperable advanced gastric cancer. The exclusion criteria were as follows: i) Patients who received chemotherapy, radiotherapy and/or biological treatment previously; ii) patients with an ECOG-PS >2; and iii) patients with multiple primary malignant neoplasms. The 100 patients were followed up by clinic visits and phone calls every 2 months, and clinical outcomes were recorded until October 2018. Genotype information was not available for 8 patients, 5 cases were lost to follow-up and 6 patients failed to receive the protocol treatment. Therefore, 81 patients were analyzed in the present study. Unresectable patients were staged according to imaging and gastroscopy when histopathologically diagnosed by biospy, and the postoperative recurrence patients were staged according to postoperative pathology. Tumors were staged using the 7th edition of the Tumor-Node-Metastasis (TNM) staging system of the International Union Against Cancer/American Joint Committee on Cancer (17). Chemotherapy was given prior to the present study, and regimens included platinum and fluoropyrimidine [cisplatin (D) 80 mg/m2 on day 1 and fluorouracil (F) 750 mg/m2 from day 1–4; D 80 mg/m2 on day 1 and capecitabine (X) 1,000 mg/m2 from day 1–14], and paclitaxel (P) and fluoropyrimidine [P, 150 mg/m2 on day 1 and F 750 mg/m2 from day 1–5; P 150 mg/m2 on day 1 and X 1,000 mg/m2 from day 1–14]. The regimens were repeated every 21 days. Chemotherapy was stopped in case of disease progression, patient refusal or grade 3–4 toxicity according to National Cancer Institute Common Terminology Criteria for Adverse Events version 4.03 (18).

SNP selection

The SNP loci of the target genes were selected from the public SNP database of the 1,000 Genome Project in the National Center for Biotechnology Information (NCBI) using minor allele frequency (MAF) >0.1 in the Chinese Han population and the Hardy-Weinberg equilibrium with a P-value of >0.1, then tagSNPs with a cut-off value of R2>0.8, and covering the gene and flanking 3 kb either side of the gene regions were chosen by the Genome Variation Server (https://gvs.gs.washington.edu). In total, 66 tagSNPs (27 from KDR gene, 32 from NRP-1 gene and 7 from PDGFβ) were selected, however due to financial constraints, 10 SNPs (rs7692791, rs6838752, rs2034965, rs1531290, rs13109660 from KDR, rs2070296, rs2804495, rs2065364 from NRP-1, and rs4821877, and rs9622978 from PDGFβ) were randomly selected from the tagSNPs. In addition, five disease-associated SNPs (rs1870377 and rs2305948 from KDR, rs6554162 and rs1800812 from PDGFRα, and rs2302273 from PDGFRβ) were selected according to their use in previous literature (1924). Finally, the 15 SNPs (Table SI) of KDR rs7692791, rs2305948, rs6838752, rs2034965, rs1531290, rs13109660 and rs1870377, of NRP-1 rs2070296, rs2804495 and rs2065364, of PDGFβ rs4821877 and rs9622978, of PDGFRα rs6554162 and rs1800812, and of PDGFRβ rs2302273, were obtained from the SNP database of the NCBI (http://www.ncbi.nlm.nih.gov/SNP).

SNP genotyping

The tissues from patients with AGC were obtained via biopsy or surgery, fixed with 10% neutral buffer formalin for 24 h at room temperature, immersed in 60°C paraffin, embedded in a paraffin block and stored at 4°C. Genomic DNA was extracted from paraffin-embedded tissues of patients with AGC using the QIAamp DNA FFPE Tissue kit (Qiagen GmbH) according to the manufacturer's instructions. The polymerase chain reaction (PCR) primers for SNPs were designed using Sequenom Assay Design 3.1 software (Sequenom) and are listed in Table SII. A thermocycler (PTC-100PCR; MJ Research) and KAPA Taq HotStart DNA polymerase (Kapa Biosystems; Roche Diagnostics) were used for PCR amplification, the thermal cycling program employed was as follows: 94°C for 5 min, followed by 35 cycles of 30 sec at 94°C, then 30 sec of annealing at 60°C, 30 sec of extension at 72°C, and a final elongation step at 72°C for 10 min. The PCR products were sequenced using a 3730XL DNA Analyzer (Applied Biosystems; Thermo Fisher Scientific, Inc.).

Statistical analysis

In the present study, the genetic model was divided into 3 types, namely, general, dominant and recessive models, as follows: Dominant model, MW+MM vs. WW; recessive model, WW+WM vs. MM; and general model, MM vs. WM vs. WW, where W indicates the wild-type allele and M the mutant allele). Before analysis, the Hardy-Weinberg equation for the equilibrium of allele distributions was tested by the χ2 test (Table I) and the SNPs with a P-value of <0.05 were excluded. The progression-free survival (PFS) and overall survival (OS) probabilities were estimated using the Kaplan-Meier method. The association between SNPs and PFS and OS were analyzed by log-rank tests and Cox regression analyses. Hazard ratios (HR) and 95% confidence intervals (CIs) were estimated for the uivariate and multivariate analyses usingy Cox regression analyses. Bonferroni's correction was applied for multiple comparisons (with the significance level set at P<0.025). Statistical analyses were conducted using SPSS v.21.0 (IBM Corp.). All tests were two-sided, and P<0.05 was considered to indicate a statistically significant difference.

Table I.

Hardy-Weinberg equilibrium test results of selected SNPs.

Table I.

Hardy-Weinberg equilibrium test results of selected SNPs.

GeneSNPχ2P-value
KDRrs76927910.110.739
rs23059480.020.902
rs68387520.650.418
rs20349651.660.197
rs131096600.030.860
rs18703770.580.455
rs15312903.370.067
NRP-1rs20702963.460.062
rs28044950.080.775
rs20653641.000.317
PDGFβrs962297811.540.007a
rs48218770.000.998
PDGFRαrs65541620.000.951
rs180081230.3 <0.001a
PDGFRβrs23022737.040.007a

a P<0.05. KDR, kinase insert domain receptor; NRP-1, neuropilin-1; PDGF, platelet-derived growth factor; PDGFR, PDGF receptor; SNP, single nucleotide polymorphism.

Results

Patient clinical characteristics

A total of 81 patients were recruited in the present study, including 56 men (69.1%) and 25 women (30.9%). The age of the patients ranged from 30 to 83 years, and the mean age was 60.7±10.1 years. By October 2018, 79 patients were deceased, 2 had been lost during follow-up, and the median PFS and OS times were 5.5 and 11.0 months, respectively. The association between clinical pathological features and survival time are listed in Table II. The results demonstrated that TNM stage analyzed by Kaplan-Meier analysis was significantly associated with longer OS time (log-rank, P=0.047), and the platinum-based chemotherapy regimen was significantly associated with longer PFS time (log-rank, P=0.025). Associations between survival time and other clinical characteristics were not identified.

Table II.

Association between characteristics and prognosis of patients with advanced gastric cancer.

Table II.

Association between characteristics and prognosis of patients with advanced gastric cancer.

Log-rank P-value

VariablesnmPFS (95% CI)mOS (95% CI)PFSOS
Sex 0.4330.703
  Male565.0 (3.3–6.7)11 (9.6–12.4)
  Female256.0 (3.6–8.4)12 (9.4–14.5)
Age, years 0.7730.898
  >60335.5 (2.8–8.2)10.2 (6.0–14.4)
  ≥60486.0 (4.8–7.2)11.0 (10.0–12.0)
N stage 0.5900.081
  N1+N2495.0 (3.6–6.4)11.6 (10.4–12.8)
  N3325.0 (2.8–7.2)10.2 (8.6–11.8)
TNM stage 0.0800.047a
  I, II and III266.8 (5.9–7.7)12.0 (8.5–15.5)
  IV554.5 (2.8–6.2)10.5 (8.5–12.5)
Tumor size, cm 0.8030.916
  >5315.0 (2.9–7.1)11.0 (9.0–13.0)
  ≥5506.0 (4.4–7.6)11.0 (9.5–12.5)
Differentiation 0.4150.079
  Well to moderate276.0 (4.5–7.5)14.8 (8.0–21.6)
  Poor544.5 (3.5–5.5)10.2 (8.2–12.2)
Platinum chemotherapy regimen 0.025a0.359
  Platinum included386 (4.6–7.4)11.6 (9.3–13.9)
  Non-platinum included434.5 (3.3–5.7)10.5 (9.0–12.0)
Paclitaxel chemotherapy regimen 0.3930.484
  Paclitaxel included394.4 (2.9–5.9)11.0 (7.1–14.9)
  Non-paclitaxel included426 (4.6–7.4)11.0 (10.2–11.8)

a P<0.05. CI, confidence interval; OS, overall survival (months); mOS, median overall survival; PFS, progression-free survival (months); mPFS, median progression-free survival; TNM, Tumor-Node-Metastasis.

Associations between genotype and survival time

Associations between genotype and prognosis were estimated by Kaplan-Meier analysis, statistical significance was determined by the log-rank test and the genotype information are listed in Table SIII. The associations between the three types of genetic models (general, dominant and recessive) and survival time were analyzed (Table III). The results demonstrated that of all the selected SNPs, five SNPs (KDR rs7692791, KDR rs1870377, KDR rs2034965, NRP-1 rs2065364 and NRP-1 rs2804495) were significantly associated with PFS or OS; however, SNPs from PDGF and PDGFR genes were not associated with clinical outcomes.

Table III.

Effect of SNPs in selected genes on the prognosis in patients with advanced gastric cancer.

Table III.

Effect of SNPs in selected genes on the prognosis in patients with advanced gastric cancer.

Log-rank P-value for PFSLog-rank P-value for OS


GeneSNPAllelic changeGeneralDominantRecessiveGeneralDominantRecessive
KDRrs7692791T/C0.032a0.009a0.2810.2270.0930.364
rs2305948C/T0.6190.3290.8710.2770.1090.748
rs6838752T/C0.0970.1370.0530.2030.2540.095
rs2034965G/A0.1550.0650.2400.0650.0310.883
rs13109660G/A0.7950.5220.6870.3650.3760.481
rs1870377T/A0.030a0.008a0.2560.0910.032a0.250
rs1531290A/G0.2360.1280.3130.4510.8450.207
NRP-1rs2070296G/A0.4980.4170.4860.9930.9640.909
rs2804495G/T0.0640.1500.028a0.0850.3080.029
rs2065364G/A0.0520.3000.015a0.1130.5870.037a
PDGFβrs4821877C/T0.7120.4900.8620.9490.9330.747
PDGFRαrs6554162G/A0.5130.3220.7510.5010.4130.561

a P<0.05. KDR, kinase insert domain receptor; NRP-1, neuropilin-1; PDGF, platelet-derived growth factor; PDGFR, PDGF receptor; OS, overall survival; PFS, progression-free survival; SNP, single nucleotide polymorphism.

Following univariate analysis (Tables IV and V), the dominant model of KDR rs1870377 indicated that AA+AT carriers were associated with shorter PFS and OS times compared with TT carriers [PFS: HR, 2.618; 95% CI, 1.235–5.550; P=0.012; OS: HR, 2.041; 95% CI, 1.042–3.999; P=0.038) (Fig. 1). Furthermore, in a recessive model of NRP-1 rs2065364, AA genotype carriers exhibited more favorable PFS and OS times compared with the GG+AG genotypes (PFS: HR, 2.896; 95% CI, 1.159–7.237; P=0.023; OS: HR, 2.367; 95% CI, 1.019–5.496; P=0.045) (Fig. 2). However, KDR rs1870377 variant AA and AT genotype were significantly associated with poor PFS times compared with wild-type TT (AA vs. TT: HR, 3.221; 95% CI, 1.356–7.651; P=0.008; AT vs. TT: HR, 2.545, 95% CI, 1.159–5.589; P=0.020; Table IV) (Fig. S1). Furthermore, the KDR rs7692791 CT genotype was associated with lower PFS times compared with the wild-type TT genotype (HR, 1.829, 95% CI, 1.091–3.066, P=0.022; Table IV) (Fig. 3). In addition, in the dominant model of KDR rs2034965, AA+GA genotypes were significantly associated with reduced OS times (HR, 1.687; 95% CI, 1.039–2.738; P=0.034; Table IV) (Fig. 3). Statistical significance between SNPs and survival time in other polymorphisms was not found.

Table IV.

Associations of SNPs in selected genes and PFS in patients with advanced gastric cancer.

Table IV.

Associations of SNPs in selected genes and PFS in patients with advanced gastric cancer.

Univariate analysisMultivariate analysis


SNPOutcomemPFS, monthsModelLog-rank P-valueHR (95% CI)P-valueHR (95% CI)a P-valuea
KDR rs7692791PFS General0.032 0.020 0.012
4.2CC0.099b1.926 (0.859–4.319)0.1122.053 (0.855–4.929)0.107
5.0CT0.018b1.829 (1.091–3.066)0.0221.969 (1.150–3.369)0.013
6.0TT Reference Reference
Dominant0.009 0.010 0.006
6.0TT Reference Reference
5.0CC+CT 1.892 (1.156–3.098)0.0111.982 (1.196–3.284)0.008
KDR rs1870377PFS General0.030 0.017 0.127
4.0AA0.005b3.221 (1.356–7.651)0.0082.892 (0.987–8.474)0.053
5.5AT0.015b2.545 (1.159–5.589)0.0201.778 (0.724–4.366)0.209
10.0TT Reference Reference
Dominant0.008 0.009 0.051
10.0TT Reference Reference
4.5AA+AT 2.618 (1.235–5.550)0.0121.970 (0.861–4.503)0.108
NRP-1 rs2065364PFS Recessive0.015 0.017 0.004
8.0AA Reference Reference
4.5AG+GG 2.896 (1.159–7.237)0.0233.905 (1.485–10.268)0.006

a Adjusted for age, sex, N stage, TNM stage, platinum included or not and differentiation.

b Bonferroni-adjusted P-value=0.05/2, so P<0.025 was considered statistically significant. CI, confidence interval; HR, hazard ratio; KDR, kinase insert domain receptor; NRP-1, neuropilin-1; PFS, progression-free survival; mPFS, median progression-free survival; SNP, single nucleotide polymorphism.

Table V.

Associations of SNPs in selected genes and OS in patients with advanced gastric cancer.

Table V.

Associations of SNPs in selected genes and OS in patients with advanced gastric cancer.

Univariate analysisMultivariate analysis


SNPOutcomemOS, monthsModelLog-rank P-valueHR (95% CI)P-valueHR (95% CI)aP-value
KDR rs2034965OS Dominant0.031 0.032 0.029
11.6GG Reference Reference
10.3AA+GA 1.687 (1.039–2.738)0.0341.978 (1.193–3.280)0.008
NRP1 rs2065364OS Recessive0.037 0.039 0.105
17.8AA Reference Reference
11.0AG+GG 2.367 (1.019–5.496)0.0452.048 (0.847–4.952)0.112
KDR rs1870377OS Dominant0.032 0.034 0.035
16.0TT Reference Reference
10.8AA+AT 2.041 (1.042–3.999)0.0382.264 (1.130–4.536)0.021
NRP1 rs2804495OS Recessive0.029 0.031 0.084
8.8TT Reference Reference
12.0GT+GG 1.710 (1.046–2.796)0.0331.570 (0.924–2.667)0.095

a Adjusted for age, gender, N stage, TNM stage and differentiation. CI, confidence interval; HR, hazard ratio; KDR, kinase insert domain receptor; NRP-1, neuropilin-1; OS, overall survival; mOS, median overall survival; SNP, single nucleotide polymorphism.

For multivariate analysis (Tables IV and V), adjustments were performed for different variables in PFS and OS. Variables that were considered clinically relevant, such as age and TNM stage, or that presented an association with survival time following univariate analysis as listed in Table II were entered into a multivariate Cox proportional-hazards regression model. KDR rs7692791 remained significantly associated with PFS, and the TT genotype was associated with better prognosis compared with the CT genotype (HR, 1.969; 95% CI, 1.150–3.369; P=0.013). Furthermore, the association between NRP-1 rs2065364 AG+GG genotypes and shorter PFS remained significant following adjustment (HR, 3.905; 95% CI, 1.485–10.268; P=0.006). Furthermore, KDR rs2034965 AA+GA genotypes remained significantly associated with worse OS following adjustment (HR, 1.978; 95% CI, 1.193–3.280, P=0.008), and the KDR rs1870377 AA+AT genotypes were also associated with shorter OS compared with the wild-type TT genotype following adjustment (HR, 2.264; 95% CI, 1.130–4.536; P=0.021).

Effect of risk allele combinations on PFS and OS

To study the combined effects of polymorphisms on survival time, risk alleles were selected according to the aforementioned results. The NRP-1 rs2065364G allele and the KDR rs1870377 A allele were found to be unfavorable for PFS and OS. Subsequently, the NRP-1 rs2065364/KDR rs1870377 combination was tested for its association with survival time and numbers of ‘risk alleles’ (Tables VI and VII). The results suggested that patients carrying >2 risk alleles were more likely to have shorter PFS and OS times compared with carriers with 1–2 risk alleles (PFS: HR, 0.427; 95% CI, 0.260–0.701; P=0.008; OS: HR, 0.523; 95% CI, 0.323–0.845; P=0.008; Tables VI and VII) (Fig. 4). Following adjustment, this association was also significant (PFS: HR, 0.427; 95% CI, 0.257–0.709; P=0.001; OS: HR, 0.511; 95% CI, 0.314–0.833; P=0.007).

Table VI.

Association between number of risk alleles and overall survival in patients with advanced gastric cancer.

Table VI.

Association between number of risk alleles and overall survival in patients with advanced gastric cancer.

Univariate analysisMultivariate analysisa


Alleles combinationnHR (95% CI)P-valueHR (95% CI)P-value
rs2065364/rs1870377
1-2 risk alleles390.523 (0.323–0.845)0.0080.511 (0.314–0.833)0.007
3-4 risk alleles42Reference Reference

a Adjusted for age, sex, N stage, TNM stage, platinum included or not and differentiation. CI, confidence interval; HR, hazard ratio; OS, overall survival.

Table VII.

Association between number of risk alleles and progression-free survival in patients with advanced gastric cancer.

Table VII.

Association between number of risk alleles and progression-free survival in patients with advanced gastric cancer.

Univariate analysisMultivariate analysisa


Alleles combinationnHR (95% CI)P-valueHR (95% CI)P-value
rs2065364/rs1870377
1-2 risk alleles390.427 (0.260–0.701)0.0080.427 (0.257–0.709)0.001
3-4 risk alleles42Reference Reference

a Adjusted for age, sex, N stage, TNM stage, platinum included or not and differentiation. CI, confidence interval; HR, hazard ratio; PFS, progression-free survival.

Discussion

The results from the present study demonstrated that polymorphisms of NRP-1 and KDR genes were associated with clinical outcome in patients with AGC. Following univariate analysis, KDR rs1870377 AA+AT genotypes were found to be associated with shorter PFS and OS times compared with the wild-type TT genotype, and the KDR rs1870377 variant AA and AT genotypes were significantly associated with poor PFS time compared with wild-type TT genotype. Furthermore, the NRP-1 rs2065364 homozygous mutant AA genotype was significantly associated with higher PFS and OS times compared with the GG+AG genotypes. The genotypes of KDR rs7692971 and KDR rs2034965 were also significantly associated with higher PFS and OS times, respectively. Following adjustment, the KDR rs7692791 TT genotype was associated with increased PFS time compared with the CT genotype, and the NRP-1 rs2065364 AG+GG genotypes were associated with shorter PFS times compared with the AA genotype. The KDR rs2034965 AA+GA genotypes were associated with worse OS times compared with the GG genotype. The KDR rs1870377 AA+AT genotypes were associated with shorter OS times compared with the TT genotype. Additionally, increasing number of risk alleles with the NRP-1 rs2065364/KDR rs1870377 combination was significantly associated with shorter OS and PFS times. These results demonstrated that NRP-1 rs2065364, KDR rs7692791, KDR rs2034965 and KDR rs1870377 may be considered as independent indicators of prognosis in patients with AGC.

NRP-1 was originally found to be crucial for neuronal axon guidance and embryonic angiogenesis, and was identified as a novel receptor involved in angiogenesis (68). Previous studies reported that the NRP-1 gene is associated with tumorigenesis and progression. One study reported that NRP-1 overexpression is associated with the promotion of gastric cancer migration, invasion and growth (25). Lin et al (26) demonstrated that NRP-1 is a novel TEA domain transcription factor target that serves a crucial role in hepatocellular carcinoma tumorigenesis. A previous demonstrated that NRP-1 is abnormally highly expressed in non-small cell lung tumor tissue, and is associated with patient prognosis (27). Another study reported that NRP-1 affects the chemosensitivity of cancer cells (28), Wey et al (28) demonstrated that NRP-1 overexpression in pancreatic cancer cell lines is associated with increased chemoresistance to gemcitabine in vitro. Yue et al (29) reported that NRP-1 overexpression increases osteosarcoma cell survival following exposure to doxorubicin. To the best of our knowledge, no study has demonstrated the association between NRP-1 SNPs and cancer. The present study confirmed that the NRP-1 rs2065364 AA genotype was associated with increased PFS time compared with the AG+GG genotypes. Further molecular investigation is required to reveal the underlying mechanisms involved.

KDR (VEGFR-2) is a tyrosine kinase receptor that can regulate signal transduction by binding to VEGF via its extracellular domain (9). VEGF/VEGFR2 is an important signaling pathway that can promote proliferation, survival and migration of vascular endothelial cells and increase vascular permeability (9,10). The cellular processes mediated by the VEGF-VEGFR2 signaling cascade can lead to angiogenesis and therefore regulate tumor growth and invasion, and therapeutic resistance (10,11). Previous studies reported that KDR gene polymorphisms are associated with clinical outcomes in various types of cancer, including colorectal cancer, glioma, hepatocellular carcinoma and gastric cancer. Torben et al (30) reported that VEGFR2 1192C>T and −604T>C polymorphisms were associated with increased microvessel density in colorectal cancer. A previous study of glioma in the Chinese population demonstrated that three SNPs of VEGFR2 (rs7667298, rs2305948 and rs1870377) are correlated with an increased risk of a glioma when homozygous (31). Another study described that the VEGFR-2 rs2305948 T polymorphism frequency is higher in patients with gastroenteropancreatic neuroendocrine neoplasms compared with that in the healthy population (19). In the present study, among the genetic variations of the VEGFR2 gene, the KDR rs1870377 and KDR rs7692791 TT genotypes were found to be associated with a better prognosis, and the KDR rs2034965 GG genotype was associated with increased OS time. Zhu et al (20) demonstrated that the VEGFR2 rs1870377 TT genotype confers a favorable prognosis in gastric cancer. Furthermore, Wang et al (21) investigated the correlation between polymorphisms of four genes from the epidermal growth factor receptor (EGFR) pathway and the clinical outcome of 363 patients with hepatocellular carcinoma, and reported that EGFR rs2034965 with the AA genotype is negatively correlated with disease-free survival. These results were consistent with the results from the present study; however, inconsistent results were reported in other types of cancer, and Kim et al (22) reported that the VEGFR2 rs1870377 TT genotype is associated with shorter OS time in patients with diffuse large B cell lymphoma. Furthermore, it was reported that rs7692791 C allele is significantly correlated with increased OS and DFS in hepatocellular carcinoma (21). These discordances may be partly attributed to the different types of cancer, the different clinical characteristics of the patients and the study sizes. The rs1870377 mutation is located in the coding region of KDR and is a missense mutation. The functional role of this gene polymorphism remains unclear.

Proteins from the PDGF family are crucial to stimulate the proliferation, survival and migration of mesenchymal cells (32), This family consists of 5 different isoforms, named disulphide-bonded homodimers of A-, B-, C- and D-polypeptide chains and the heterodimer PDGF-AB. PDGFR is classified as a receptor tyrosine kinase, and the 5 PDGF isoforms can activate cellular responses via PDGFRα and PDGFRβ (32,33). Overactivation of the PDGF-PDGFR signaling pathway has been reported to be associated with tumorigenesis (34). PDGFR gene mutations have been found in malignancies. Point mutations in PDGFRα were found in ~5% of gastrointestinal stroma tumors, which led to amino acid residue changes, therefore activating PDGFR kinase activity (35). In addition, a study reported that rs1800812 T allele and rs6554162 G allele in PDGFRα were related to decreased frequency in patients with papillary thyroid cancer compared with that in the healthy population (23). A previous study demonstrated that two SNPs in PDGFβ (rs5757573 T>C and rs6001516 C>T) were associated with an increased risk of pancreatic cancer (36). Furthermore, Volz et al (24) found that the SNP (rs2302273 C>T) in PDGFRβ gene was associated with a significantly longer PFS time in patients with metastatic colorectal cancer. However, in the present study, no relevance was found between SNPs and prognosis.

The present study had some limitations. Firstly, only 81 patients with AGC were eligible for statistical analysis. Since the sample size was relatively small, the results from this study should be considered as preliminary data and for generation of a hypothesis for subsequent investigation. Secondly, since the patients studied had AGC, it is not known whether the results could be applicable to patients with other types of gastric cancer. Further investigation should therefore be conducted to validate the results.

In conclusion, the results from the present study demonstrated that KDR rs7692791 and NRP-1 rs2065364 were positively associated with PFS. Furthermore, KDR rs2034965 and KDR rs1870377 significantly negatively correlated with OS time following multivariate analysis in patients with AGC. In addition, the numbers of ‘risk alleles’ of NRP-1 rs2065364/KDR rs1870377 combination were significantly associated with survival time. These results suggested that genetic variants in NRP-1 and KDR genes may affect the biological features and prognosis of patients with AGC. Due to limited funding, the underlying mechanisms were not explored, and further investigation is required to verify these results.

Supplementary Material

Supporting Data

Acknowledgements

The authors would like to thank Dr Dan Lv (Department of Medical Oncology, The Second Hospital Affiliated to Dalian Medical University) and Professor Na Gao (Department of Obstetrics and Gynecology, First Affiliated Hospital of Dalian Medical University) for their technical assistance.

Funding

No funding was recieved.

Availability of data and materials

All data and materials generated and/or used during the study are available from the corresponding author upon reasonable request.

Authors' contributions

TW and YS designed the study. YS collected the patients' clinical data. YJZ and YS performed the experiments. YJZ and YS analyzed the data and wrote the manuscript. TW contributed to the revision of the manuscript. All the authors read and approved the final manuscript.

Ethics approval and consent to participate

The present study was approved by the Clinical Research Ethics Committee of The Second Hospital Affiliated to Dalian Medical University and was conducted in accordance with The Declaration of Helsinki. Participants were fully informed of the procedures and provided written informed consent.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1 

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA and Jemal A: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 68:394–424. 2018. View Article : Google Scholar : PubMed/NCBI

2 

Wang Y, Li Z, Shan F, Miao R, Xue K, Li Z, Gao C, Chen N, Gao X, Li S and Ji J: Current status of diagnosis and treatment of early gastric cancer in China-Data from China gastrointestinal cancer surgery union. Zhonghua Wei Chang Wai Ke Za Zhi. 21:168–174. 2018.(In Chinese). PubMed/NCBI

3 

Wang FH, Shen L, Li J, Zhou ZW, Liang H, Zhang XT, Tang L, Xin Y, Jin J, Zhang YJ, et al: The Chinese Society of Clinical Oncology (CSCO): Clinical Guidelines for the Diagnosis and Treatment of Gastric Cancer. Cancer Commun (Lond). 39:102019. View Article : Google Scholar : PubMed/NCBI

4 

Van Cutsem E, Sagaert X, Topal B, Haustermans K and Prenen H: Gastric cancer. Lancet. 388:2654–2664. 2016. View Article : Google Scholar : PubMed/NCBI

5 

Chen K, Yang D, Li X, Sun B, Song F, Cao W, Brat DJ, Gao Z, Li H, Liang H, et al: Mutational landscape of gastric adenocarcinoma in Chinese: Implications for prognosis and therapy. Proc Natl Acad Sci USA. 112:1107–1112. 2015. View Article : Google Scholar : PubMed/NCBI

6 

Fujisawa H, Takagi S and Hirata T: Growth-associated expression of a membrane protein, neuropilin, in Xenopus optic nerve fibers. Dev Neurosci. 17:343–349. 1995. View Article : Google Scholar : PubMed/NCBI

7 

Muhl L, Folestad EB, Gladh H, Wang Y, Moessinger C, Jakobsson L and Eriksson U: Neuropilin 1 binds PDGF-D and is a co-receptor in PDGF-D-PDGFRβ signaling. J Cell Sci. 130:1365–1378. 2017. View Article : Google Scholar : PubMed/NCBI

8 

Raimondi C: Neuropilin-1 enforces extracellular matrix signalling via ABL1 to promote angiogenesis. Biochem Soc Trans. 42:1429–1434. 2014. View Article : Google Scholar : PubMed/NCBI

9 

Ferrara N, Gerber HP and LeCouter J: The biology of VEGF and its receptors. Nat Med. 9:669–676. 2003. View Article : Google Scholar : PubMed/NCBI

10 

Karaman S, Leppänen VM and Alitalo K: Vascular endothelial growth factor signaling in development and disease. Development. 145(pii): dev1510192018. View Article : Google Scholar : PubMed/NCBI

11 

Shibuya M and Claesson-Welsh L: Signal transduction by VEGF receptors in regulation of angiogenesis and lymphangiogenesis. Exp Cell Res. 312:549–560. 2006. View Article : Google Scholar : PubMed/NCBI

12 

Farooqi AA and Siddik ZH: Platelet-derived growth factor (PDGF) signalling in cancer: Rapidly emerging signalling landscape. Cell Biochem Funct. 33:257–265. 2015. View Article : Google Scholar : PubMed/NCBI

13 

Heldin CH, Lennartsson J and Westermark B: Involvement of platelet-derived growth factor ligands and receptors in tumorigenesis. J Intern Med. 283:16–44. 2018. View Article : Google Scholar : PubMed/NCBI

14 

Sa-Nguanraksa D, Kooptiwut S, Chuangsuwanich T, Pongpruttipan T, Malasit P and O-Charoenrat P: Vascular endothelial growth factor polymorphisms affect gene expression and tumor aggressiveness in patients with breast cancer. Mol Med Rep. 9:1044–1048. 2014. View Article : Google Scholar : PubMed/NCBI

15 

Glubb DM, Cerri E, Giese A, Zhang W, Mirza O, Thompson EE, Chen P, Das S, Jassem J, Rzyman W, et al: Novel functional germline variants in the VEGF receptor 2 gene and their effect on gene expression and microvessel density in lung cancer. Clin Cancer Res. 17:5257–5267. 2011. View Article : Google Scholar : PubMed/NCBI

16 

Oken M, Creech R, Tormey D, Horton J, Davis TE, McFadden ET and Carbone PP: Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol. 5:649–655. 1982. View Article : Google Scholar : PubMed/NCBI

17 

Edge SB, Byrd DR, Compton CC, Fritz AG, Greene FL and Trotti A III: AJCC Cancer Staging Manual. 7th. Springer; New York, NY: 2010

18 

National Cancer Institute (NCI), . Common Terminology Criteria for Adverse Events CTCAE). Version 4.03. 2010.

19 

Berardi R, Torniai M, Partelli S, Rubini C, Pagliaretta S, Savini A, Polenta V, Santoni M, Giampieri R, Onorati S, et al: Impact of vascular endothelial growth factor (VEGF) and vascular endothelial growth factor receptor (VEGFR) single nucleotide polymorphisms on outcome in gastroenteropancreatic neuroendocrine neoplasms. PLoS One. 13:e01970352018. View Article : Google Scholar : PubMed/NCBI

20 

Zhu X, Wang Y, Xue W, Wang R, Wang L, Zhu ML and Zheng L: The VEGFR-2 protein and the VEGFR-2 rs1870377 A>T genetic polymorphism are prognostic factors for gastric cancer. Cancer Biol Ther. 20:497–504. 2019. View Article : Google Scholar : PubMed/NCBI

21 

Wang W, Ma XP, Shi Z, Zhang P, Ding DL, Huang HX, Saiyin HG, Chen TY, Lu PX, Wang NJ, et al: Epidermal growth factor receptor pathway polymorphisms and the prognosis of hepatocellular carcinoma. Am J Cancer Res. 5:396–410. 2015.PubMed/NCBI

22 

Kim MK, Suh C, Chi HS, Cho HS, Bae YK, Lee KH, Lee GW, Kim IS, Eom HS, Kong SY, et al: VEGFA and VEGFR2 genetic polymorphisms and survival in patients with diffuse large B cell lymphoma. Cancer Sci. 103:497–503. 2012. View Article : Google Scholar : PubMed/NCBI

23 

Kim MJ, Kim SK, Park HJ, Chung DH, Park HK, Lee JS, Kwon KH and Chung JH: PDGFRA promoter polymorphisms are associated with the risk of papillary thyroid cancer. Mol Med Rep. 5:1267–1270. 2012.PubMed/NCBI

24 

Volz NB, Stintzing S, Zhang W, Yang D, Ning Y, Wakatsuki T, El-Khoueiry RE, Li JE, Kardosh A, Loupakis F, et al: Genes involved in pericyte-driven tumor maturation predict treatment benefit of first-line FOLFIRI plus bevacizumab in patients with metastatic colorectal cancer. Pharmacogenomics J. 15:69–76. 2015. View Article : Google Scholar : PubMed/NCBI

25 

Peng Y, Liu YM, Li LC, Wang LL and Wu XL: MicroRNA-338 inhibits growth, invasion and metastasis of gastric cancer by targeting NRP1 expression. PLoS One. 9:e944222014. View Article : Google Scholar : PubMed/NCBI

26 

Lin J, Zhang Y, Wu J, Li L, Chen N, Ni P, Song L and Liu X: Neuropilin 1 (NRP1) is a novel tumor marker in hepatocellular carcinoma. Clin Chim Acta. 485:158–165. 2018. View Article : Google Scholar : PubMed/NCBI

27 

Ding M, Liu L, Hu C, Liu Y, Qiao Y and Jiang X: Expression of VEGFR2 and NRP-1 in non-small cell lung cancer and their clinical significance. Chin J Cancer Res. 26:669–677. 2014.PubMed/NCBI

28 

Wey JS, Gray MJ, Fan F, Belcheva A, McCarty MF, Stoeltzing O, Somcio R, Liu W, Evans DB, Klagsbrun M, et al: Overexpression of neuropilin-1 promotes constitutive MAPK signalling and chemoresistance in pancreatic cancer cells. Br J Cancer. 93:233–241. 2005. View Article : Google Scholar : PubMed/NCBI

29 

Yue B, Ma JF, Yao G, Yang MD, Cheng H and Liu GY: Knockdown of neuropilin-1 suppresses invasion, angiogenesis and increases the chemosensitivity to doxorubicin in osteosarcoma cells-an in vitro study. Eur Rev Med Pharmacol Sci. 18:1735–1741. 2014.PubMed/NCBI

30 

Hansen TF, Sørensen FB, Spindler KL, Olsen DA, Andersen RF, Lindebjerg J, Brandslund I and Jakobsen A: Microvessel density and the association with single nucleotide polymorphisms of the vascular endothelial growth factor receptor 2 in patients with colorectal cancer. Virchows Arch. 456:251–260. 2010. View Article : Google Scholar : PubMed/NCBI

31 

Zhang J, Yang J, Chen Y, Mao Q, Li S, Xiong W, Lin Y, Chen J and Ge J: Genetic variants of VEGF (rs201963 and rs3025039) and KDR (rs7667298, rs2305948, and rs1870377) are associated with glioma risk in a Han Chinese population: A case-control study. Mol Neurobiol. 53:2610–2618. 2016. View Article : Google Scholar : PubMed/NCBI

32 

Andrae J, Gallini R and Betsholtz C: Role of platelet-derived growth factors in physiology and medicine. Genes Dev. 22:1276–1312. 2008. View Article : Google Scholar : PubMed/NCBI

33 

Heldin CH and Westermark B: Mechanism of action and in vivo role of platelet-derived growth factor. Physiol Rev. 79:1283–1316. 1999. View Article : Google Scholar : PubMed/NCBI

34 

Pietras K, Sjöblom T, Rubin K, Heldin CH and Ostman A: PDGF receptors as cancer drug targets. Cancer Cell. 3:439–443. 2003. View Article : Google Scholar : PubMed/NCBI

35 

Heinrich MC, Corless CL, Duensing A, McGreevey L, Chen CJ, Joseph N, Singer S, Griffith DJ, Haley A, Town A, et al: PDGFRA activating mutations, in gastrointestinal stromal tumors. Science. 299:708–710. 2003. View Article : Google Scholar : PubMed/NCBI

36 

Duan B, Hu J, Liu H, Wang Y, Li H, Liu S, Xie J, Owzar K, Abbruzzese J, Hurwitz H, et al: Genetic variants in the platelet-derived growth factor subunit B gene associated with pancreatic cancer risk. Int J Cancer. 142:1322–1331. 2018. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

November-2019
Volume 18 Issue 5

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Zhuo YJ, Shi Y and Wu T: NRP‑1 and KDR polymorphisms are associated with survival time in patients with advanced gastric cancer. Oncol Lett 18: 4629-4638, 2019.
APA
Zhuo, Y., Shi, Y., & Wu, T. (2019). NRP‑1 and KDR polymorphisms are associated with survival time in patients with advanced gastric cancer. Oncology Letters, 18, 4629-4638. https://doi.org/10.3892/ol.2019.10842
MLA
Zhuo, Y., Shi, Y., Wu, T."NRP‑1 and KDR polymorphisms are associated with survival time in patients with advanced gastric cancer". Oncology Letters 18.5 (2019): 4629-4638.
Chicago
Zhuo, Y., Shi, Y., Wu, T."NRP‑1 and KDR polymorphisms are associated with survival time in patients with advanced gastric cancer". Oncology Letters 18, no. 5 (2019): 4629-4638. https://doi.org/10.3892/ol.2019.10842