ORIGINAL RESEARCH article

Front. Oncol., 04 June 2021

Sec. Radiation Oncology

Volume 11 - 2021 | https://doi.org/10.3389/fonc.2021.651632

Association of Inflammation-Related Gene Polymorphisms With Susceptibility and Radiotherapy Sensitivity in Head and Neck Squamous Cell Carcinoma Patients in Northeast China

  • YL

    Ying Li * †

  • LZ

    Li Zhu †

  • HY

    Hongmin Yao

  • YZ

    Ye Zhang

  • XK

    Xiangyu Kong

  • LC

    Liping Chen

  • YS

    Yingqiu Song

  • AM

    Anna Mu

  • XL

    Xia Li *

  • Department of Radiation Oncology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, and Key Laboratory of Tumor Radiosensitization and Normal Tissue Radioprotection of Liaoning Province, Shenyang, China

Abstract

Background:

Inflammation-related gene polymorphisms are some of the most important determinants for cancer susceptibility, clinical phenotype diversity, and the response to radiotherapy and chemotherapy. However, the relationship between these polymorphisms and head and neck squamous cell carcinoma (HNSCC) remains unclear. The aim of this study was to investigate the role of inflammation-related gene polymorphisms in the developmental risk and radiotherapy sensitivity of HNSCC.

Methods:

The Matrix-Assisted Laser Desorption Ionization Time of Flight (MALDI-TOF) genotyping system was used to genotype 612 individuals from a Chinese population for 28 inflammation-related gene polymorphisms.

Results:

The protein kinase B (AKT1) rs1130233 TT, dominance model (CT+TT vs. CC), recessive model (TT vs. CT+CC), and rs2494732 CC genotypes were associated with reduced risk of HNSCC (P=0.014; P=0.041; P=0.043). The polymeric immunoglobulin receptor (PIGR) rs291097 GA, dominance model (GA+AA vs. GG), and rs291102 dominance model (GA+AA vs. GG) were associated with increased risk of HNSCC (P=0.025; P=0.025; P=0.040). The interleukin-4 receptor-α (IL-4RA) rs1801275 AA genotype was significantly correlated with increased radiotherapy sensitivity of HNSCC patients (P=0.030). In addition, age ≤ 60 years, non-smoker status, and normal levels of squamous cell carcinoma antigen (SCC) were found to be associated with increased radiotherapy sensitivity of HNSCC patients (P=0.033; P=0.033; P=0.030).

Conclusion:

The AKT1 rs1130233, AKT1 rs2494732, PIGR rs291097, and PIGR rs291102 polymorphisms were significantly related to the risk of HNSCC. The IL-4RA rs1801275 polymorphism, age ≤ 60 years, non-smoker status, and normal levels of SCC were significantly associated with increased radiotherapy sensitivity of HNSCC.

Introduction

Head and neck squamous cell carcinoma (HNSCC) is a general term for a set of different tumors located in the lips, oral cavity, pharynx (nasopharynx, oropharynx and hypopharynx), as well as the larynx, salivary glands, and thyroid glands (). HNSCC is sixth in the world in overall incidence, and is also a major cancer type that leads to death (). The initiation and development of HNSCC is a multistep process influenced by various genetic and environmental factors. Tobacco and alcohol consumption are the most classical risk factors associated with its development. At least 75% of HNSCC cases are attributable to the combination of both tobacco and alcohol use (). However, the role of genetic factors in head and neck squamous cell carcinogenesis is largely unknown.

Single nucleotide polymorphisms (SNPs) are a class of genetic factors that have been implicated in HNSCC susceptibility and determine inter-individual variations in HNSCC risk. Genetic polymorphisms can weaken intrinsic protective mechanisms and increase the damage caused by environmental carcinogens (). Carriers of susceptible genotypes are at a greater risk of developing cancer than those with resistant genotypes under similar conditions (). Therefore, genetic factors may play a crucial role in HNSCC risk and clinical outcome.

Inflammation is an important cellular process that can be activated in response to tissue damage, infections, and other cellular stress factors6. There is a relationship between inflammation and the development of many cancers where tumorigenesis was initiated at the site of inflammation (, ). Interleukin-1 (IL-1) is a pleiotropic cytokine involved in the initiation of immune and inflammatory responses. The IL-1 gene family has been reported to play a crucial role in the pathogenesis of various cancers (–). The interleukin-1 receptor antagonist (IL-1RN) polymorphism is associated with cervical cancer (). Additionally, there is a pro-inflammatory cytokine haplotype (IL-6 CC, IL-10 GG, TNF-α AA) that is associated with adverse prognosis that may act through an inflammatory-mediated mechanism (). Furthermore, protein kinase B (AKT1) is an important downstream effector of the gene of phosphate and tension homology deleted on chromosome ten/phosphoinositide 3-kinase/protein kinase B (PTEN/PI3K/AKT) signal transduction pathway. Aberrant expression and genetic variation of the AKT1 gene are suggested to be involved in several types of human cancers, including oral squamous cell carcinoma (OSCC) (). The AKT1 rs1130214 and rs3803300 polymorphisms were related to OSCC susceptibility in a Chinese Han population (). The polymeric immunoglobulin receptor (PIGR) 1739C>T is a missense mutation that results in an alanine residue being changed to valine near an endoproteolytic cleavage site. This variant can alter the efficiency of PIGR to release the Epstein–Barr virus immunoglobulin A (IgA-EBV) complex and consequently increase the susceptibility of populations in endemic areas to develop NPC (). PIGR 8880C>T is also related to NPC susceptibility (). Additionally, the cyclooxygenase-2 (COX-2) gene (PTGS2) rs5275 variant contributes to NPC risk in a Chinese population ().

Chronic inflammation promotes genetic and epigenetic aberrations that result in various pathogeneses. These changes may be useful biomarkers in liquid biopsies for early detection and prevention of various cancers (). To achieve our aim, analysis of candidate genes in a Chinese population was performed to study 28 SNPs in inflammation-related genes that could possibly be associated with the risk of developing HNSCC.

Materials and Methods

Research Design and Study Population

The study design was approved by the Human Ethics Committee of Liaoning Cancer Hospital (Shenyang, China). Each individual provided written informed consent during an epidemiological investigation. Patients were from Liaoning Cancer Hospital and received surgical resection or needle biopsy diagnosis/treatment between 2018 and 2019. The control participants were recruited from health check center in Liaoning Province hospital between 2018 and 2019. The HNSCC patient group and the control group were matched at a 1:2 ratio. All diagnoses of HNSCC patients were based on histopathological examinations. Information regarding smoking habits, alcohol consumption, and family history in cases were acquired by a “face-to-face” questionnaire survey. We collected fasting venous blood from each one and stored the samples at −20°C as serum and clotted cells.

To further evaluate the relationship of polymorphisms with clinicopathological parameters of HNSCC, histology or clinical data were assessed according to World Health Organization criteria. Additionally, tumor-node-metastasis (TNM) staging was performed according to the 8th edition of the International Union Against Cancer (UICC)/American Joint Committee on Cancer (AJCC) (2017) criteria ().

SNP Selection

A compilation of genes involved in the inflammatory response was conducted on the basis of a published panel of inflammation-associated genes (, , –, –44) and the NCBI-Gene website analysis (https://www.ncbi.nlm.nih.gov/gene/). In this study, we selected 16 genes and 28 SNPs for analysis. They are as follows: AKT1 rs130233 and rs2494732; complement C3d receptor 2 (CR2) rs3813946; IL10 rs1800871, rs1800872, and rs1800896; IL1A rs17561; IL1B rs1143627, rs16944, and rs1143634; IL1RN rs419598; IL21R rs2189521; IL4 rs2243250 and rs2227284; IL4RA rs1801275; IL6 rs1800796; PIGR rs291097 and rs291102; tumor necrosis factor (TNF) rs1799964, rs1800629, rs361525, rs1800630 and rs1799724; TNFRSF1A rs4149570; TNFSF7 rs7259857; COX-2 rs5275 and rs20417; B-cell lymphoma-2 (BCL2) rs2279115.

SNP Genotyping

Genomic DNA was extracted from peripheral blood samples obtained from the study participants using the phenol-cholesterol method according to a standard procedure (45). The Matrix-Assisted Laser Desorption Ionization Time of Flight (MALDI-TOF) genotyping system was used to genotype 612 individuals for 28 inflammation-related gene polymorphisms. MALDI-TOF is a medium-to-high-throughput technology platform that takes both sensitivity and specific into account and used mass spectrometry for direct detection (46). Amplification and extension primers were designed by BGI. The charged analytes were detected and measured using time of flight analyzers. During MALDI-TOF analysis, the m/z ratio of an ion was measured by determining the time required for the ion to travel the length of the flight tube (47, 48). Primers sequences are listed in Supplementary Table 1.

Radiosensitivity Analysis

Radiosensitivity analysis was done according to the new response evaluation criteria for solid tumors: Revised response evaluation criteria in solid tumors (RECIST) guideline (version 1.1) (49). Patients who were sensitive to radiation therapy were categorized as either complete response (CR) or partial response (PR). Patients who were not sensitive to radiation therapy were categorized as either progressive disease (PD) or stable disease (SD). Radiosensitivity was assessed one month after radiotherapy, and the results were compared with the MRI image before radiotherapy. The criteria for classification are as follows:

  • CR: patients had a disappearance of all target lesions and any pathological lymph nodes (whether target or non-target) were required to have a short axis reduction to <10 mm.

  • PR: patients were required to have at least a 30% decrease in the sum of the diameters of target lesions, using the baseline sum diameters as a reference.

  • PD: patients were required to have at least a 20% increase in the sum of the diameters of target lesions, using the smallest sum of the study as a reference. In addition to the relative increase of 20%, the sum was also required to demonstrate an absolute increase of at least 5 mm. Patients that had an appearance of one or more new lesions were also categorized as PD.

  • SD: patients were required to have neither a sufficient level of shrinkage to qualify for PR nor a sufficient amount of increase to qualify for PD. The smallest sum diameters were used as references.

Statistical Analysis

Statistical analysis was performed using SPSS (version 22.0). Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for the relationships between both SNPs and disease risk were calculated by multivariable logistic regression, with adjustments for gender and age. If stratified by sex, then the age was adjusted; if stratified by age, then the sex was adjusted. Chi-squared tests were used to assess the correlation between different genotypes and the clinicopathological parameters and radiosensitivity of HNSCC patients.

Results

Baseline Patient Characteristics

To analyze the risk of HNSCC, the study subjects included 211 patients with HNSCC and 401 age- and sex-matched control subjects. The comparisons of baseline characteristics between cases and controls are shown in Table 1. There was a significant difference in both age and sex distribution between the HNSCC group and the control group. The overall mean age and mean age of menarche differed significantly between cases and controls (both P<0.001). In cases, the mean menopausal age was 58.00 years and only a small proportion of cases had a family history of cancer (15.2%). In cases with invasion depth, 55.2% and 44.8% of cases were in T1-2 and T3-4, respectively. Tumor stages I-II (23.7%) and III-IV (76.3%) accounted for the majority of HNSCC cases, whereas 69.6% of cases had positive lymph nodes and 5.9% of cases had metastasis (Table 1).

Table 1

CharacteristicsCasesControlsP value
Sample size211401
Age<0.001
Mean±SD56.83±0.7536.25±0.63
Mmenarche5832
Range14-9017-73
GenderFemale49(23.2%)175(43.6%)<0.001
Male162(76.8%)226(56.4%)
T stage1-296(55.2%)
3-478(44.8%)
N stageNegative55(30.4%)
Positive126(69.6%)
M stageNegative177(94.1%)
Positive11(5.9%)
Clinical stageI-II44(23.7%)
III-IV142(76.3%)
SmokingNo102(48.3%)
Yes109(51.7%)
DrinkingNo106(50.2%)
Yes105(49.8%)
Family history of cancerNo179(84.8%)
Yes32(15.2%)
SCCNormal80(79.2%)
Increased21(20.8%)
CEANormal60(93.8%)
Increased4(6.3%)
CYFRANormal16(48.5%)
Increased17(51.5%)
EBVNegative30(83.3%)
Positive6(16.7%)
Blood typeA40(33.6%)
B32(26.9%)
AB14(11.8%)
O33(27.7%)

The baseline characteristics of the objects.

There was a significant difference in both age and sex distribution between the HNSCC group and the control group (both P<0.001). The case group is significantly older than the control group. Men are significantly more than women, especially in the case group.

Association of 28 Inflammation-Associated Gene SNPs With HNSCC Risk

Multivariable logistic regression was used to investigate the association of 28 inflammation-associated gene SNPs with HNSCC risk. The results indicated that the AKT1 rs1130233 and rs2494732 SNPs, as well as the PIGR rs291097 and rs291102 SNPs, had a significant association with HNSCC risk progression (Table 2). We also found that the carriers of the AKT1 rs1130233 TT genotype, dominance model (CT+TT vs. CC), recessive model (TT vs. CT+CC), or the AKT1 rs2494732 CC genotype had reduced risk of HNSCC (P<0.05), whereas those with the PIGR rs291097 GA genotype, dominance model (GA+ AA vs. GG), or PIGR rs291102 dominance model (GA+ AA vs. GG) had an increased risk of HNSCC (P<0.05). However, we found no significant differences with the other 24 SNPs in HNSCC risk progression (Table 2).

Table 2

GenetypeSNPCasesControlsP valueP valueOR (95%CI)
AKT1rs1130233N=208N=4000.020
CC58(27.9%)77(19.3%)/1(Ref)
CT98(47.1%)189(47.3%)0.1490.65(0.36,1.17)
TT52(25.0%)134(33.5%)0.0140.45(0.24,0.85)
CT+TT vs. CC//0.0410.57(0.33,0.98)
TT vs.CT+CC//0.0460.60(0.36,0.99)
AKT1rs2494732N=209N=3950.678
TT18(8.6%)27(6.8%)/1(Ref)
CT97(46.4%)158(40.0%)0.2200.56(0.22,1.41)
CC94(45.0%)210(53.2%)0.0430.38(0.15,0.97)
CT+CC vs. TT//0.0890.46(0.19,1.13)
CC vs.CT+TT//0.0730.66(0.42,1.04)
CR2rs3813946N=209N=3960.309
TT154(73.7%)313(79.0%)/1(Ref)
CT53(25.4%)79(19.9%)0.8250.94(0.55,1.62)
CC2(1.0%)4(1.0%)0.1660.24(0.03,1.81)
CT+CC vs. TT//0.6120.87(0.51,1.49)
CC vs.CT+TT//0.1480.22(0.03,1.70)
IL10rs1800871N=208N=4000.861
AA90(43.3%)164(41.0%)/1(Ref)
GA98(47.1%)197(49.3%)0.3950.82(0.51,1.31)
GG20(9.6%)39(9.8%)0.5721.27(0.55,2.91)
GA+GG vs. AA//0.5350.86(0.55,1.37)
GG vs. GA+AA//0.3901.40(0.65,3.04)
IL10rs1800872N=208N=4000.861
TT90(43.3%)164(41.0%)/1(Ref)
GT98(47.1%)197(49.3%)0.3950.82(0.51,1.31)
GG20(9.6%)39(9.8%)0.5721.27(0.55,2.91)
GT+GG vs.TT//0.5350.86(0.55,1.37)
GG vs.GT+TT//0.3901.40(0.65,3.04)
IL10rs1800896N=209N=4000.297
TT174(83.3%)322(80.5%)/1(Ref)
CT33(15.8%)77(19.3%)0.5520.84(0.46,1.51)
CC2(1.0%)1(0.3%)0.6561.89(0.12,30.68)
CT+CC vs. TT//0.6100.86(0.48,1.54)
CC vs.CT+TT//0.6481.90(0.12,30.24)
IL1Ars17561N=208N=4000.833
CC166(79.8%)327(81.8%)/1(Ref)
CA40(19.2%)69(17.3%)0.7541.10(0.60,2.01)
AA2(1.0%)4(1.0%)0.8691.21(0.13,11.72)
CA+AA vs. CC//0.7381.11(0.61,1.99)
AA vs.CA+CC//0.8821.19(0.12,11.72)
IL1Brs1143627N=208N=3940.588
AA51(24.5%)111(28.2%)/1(Ref)
AG107(51.4%)188(47.7%)0.9490.98(0.58,1.67)
GG50(24.0%)95(24.1%)0.4030.76(0.40,1.45)
AG+GG vs. AA//0.6490.90(0.54,1.51)
GG vs. AG+AA//0.3880.79(0.46,1.35)
IL1Brs16944N=209N=3970.710
GG52(24.9%)111(28.0%)/1(Ref)
GA106(50.7%)191(48.1%)0.8810.96(0.56,1.63)
AA51(24.4%)95(23.9%)0.4690.79(0.42,1.50)
GA+AA vs. GG//0.6860.90(0.54,1.51)
AA vs.GA+GG//0.4930.83(0.48,1.42)
IL1Brs1143634N=209N=4000.761
GG199(95.2%)381(95.3%)/1(Ref)
GA10(4.8%)18(4.5%)0.8611.10(0.38,3.17)
AA0(0.0%)1(0.3%)NA5.06×10-7(5.06×10-7,5.06×10-7)
GA+AA vs. GG//0.8641.10(0.38,3.16)
AA vs.GA+GG//NA4.59×10-7(4.59×10-7,4.59×10-7)
IL1RNrs419598N=143N=3930.292
TT128(89.5%)336(85.5%)/1(Ref)
CT13(9.1%)54(13.7%)0.1220.52(0.22,1.19)
CC2(1.4%)3(0.8%)0.7131.49(0.18,12.33)
CT+CC vs. TT//0.1780.58(0.26,1.28)
CC vs.CT+TT//0.6661.57(0.20,12.39)
IL21Rrs2189521N=208N=3950.050
TT131(63.0%)208(52.7%)/1(Ref)
CT67(32.2%)160(40.5%)0.2800.77(0.47,1.24)
CC10(4.8%)27(6.8%)0.6130.78(0.30,2.05)
CT+CC vs. TT//0.2670.77(0.48,1.22)
CC vs.CT+TT//0.7780.87(0.32,2.34)
IL4rs2243250N=209N=3950.427
CC9(4.3%)13(3.3%)/1(Ref)
CT76(36.4%)127(32.2%)0.6520.76(0.23,2.54)
TT124(59.3%)255(64.6%)0.3840.55(0.14,2.12)
CT+TT vs. CC//0.4680.63(0.18,2.20)
TT vs.CT+CC//0.2510.76(0.48,1.21)
IL4rs2227284N=209N=3950.344
TT144(68.9%)294(74.4%)/1(Ref)
GT60(28.7%)94(23.8%)0.4091.24(0.74,2.09)
GG5(2.4%)7(1.8%)0.3362.54(0.38,16.88)
GT+GG vs.TT//0.3171.30(0.78,2.16)
GG vs.GT+TT//0.3702.24(0.38,13.07)
IL4RArs1801275N=207N=4000.116
AA152(73.4%)272(68.0%)/1(Ref)
GA53(25.6%)114(28.5%)0.9951.00(0.60,1.67)
GG2(1.0%)14(3.5%)0.2000.31(0.05,1.85)
GA+GG vs. AA//0.7560.92(0.56,1.52)
GG vs. GA+AA//0.2000.31(0.05,1.87)
IL6rs1800796N=209N=3950.942
GG26(12.4%)47(11.9%)/1(Ref)
CG87(41.6%)170(43.0%)0.8521.08(0.49,2.38)
CC96(45.9%)178(45.1%)0.4871.32(0.61,2.84)
CG+CC vs.GG//0.6461.19(0.57,2.50)
CC vs.CG+GG//0.3861.23(0.77,1.94)
PIGRrs291097N=209N=4000.125
GG188(90.0%)372(93.0%)/1(Ref)
GA21(10.0%)28(7.0%)0.0252.49(1.12,5.53)
AA0(0%)0(0.0%)NANA
GA+AA vs. GG//0.0252.49(1.12,5.53)
AA vs.GA+GG//NANA
PIGRrs291102N=208N=3960.794
GG165(79.3%)323(81.6%)/1(Ref)
GA41(19.7%)70(17.7%)0.0541.82(0.99,3.35)
AA2(1.0%)3(0.8%)0.2913.76(0.32,43.88)
GA+AA vs. GG//0.0401.86(1.03,3.38)
AA vs.GA+GG//0.3493.17(0.28,35.45)
TNFrs1799964N=209N=3950.732
TT124(59.3%)246(62.3%)/1(Ref)
CT74(35.4%)132(33.4%)0.3881.24(0.76,2.01)
CC11(5.3%)17(4.3%)0.2802.03(0.56,7.29)
CT+CC vs. TT//0.2901.29(0.81,2.05)
CC vs.CT+TT//0.3461.79(0.53,5.99)
TNFrs1800629N=209N=3960.725
GG0(0%)347(87.6%)/1(Ref)
GA208(99.5%)47(11.9%)NANA
AA1(0.5%)2(0.5%)NANA
GA+AA vs. GG//NANA
AA vs.GA+GG//0.4700.36(0.02,5.74)
TNFRSF1Ars4149570N=205N=3950.370
CC43(21.0%)101(25.6%)/1(Ref)
CA102(49.8%)194(49.1%)0.4391.27(0.69,2.34)
AA60(29.3%)100(25.3%)0.3051.39(0.74,2.61)
CA+AA vs. CC//0.3261.33(0.75,2.34)
AA vs.CA+CC//0.4511.22(0.73,2.03)
TNFSF7rs7259857N=209N=3960.804
TT166(79.4%)322(81.3%)/1(Ref)
CT40(19.1%)70(17.7%)0.9981.00(0.54,1.85)
CC3(1.4%)4(1.0%)0.2412.86(0.49,16.59)
CT+CC vs. TT//0.7571.10(0.61,1.98)
CC vs.CT+TT//0.2392.87(0.50,16.60)
TNFrs361525N=209N=3960.467
GG191(91.4%)364(91.9%)/1(Ref)
GA18(8.6%)32(8.1%)0.6401.21(0.54,2.73)
AA0(0%)0(0%)NANA
GA+AA vs. GG//0.6401.21(0.54,2.73)
AA vs.GA+GG//NANA
TNFrs1800630N=207N=3950.899
CC141(68.1%)274(69.4%)/1(Ref)
CA59(28.5%)110(27.8%)0.7401.09(0.65,1.82)
AA7(3.4%)11(2.8%)0.2772.30(0.51,10.35)
CA+AA vs. CC//0.5911.15(0.70,1.88)
AA vs.CA+CC//0.3272.06(0.49,8.75)
TNFrs1799724N=205N=3980.893
CC153(74.6%)302(75.9%)/1(Ref)
CT48(23.4%)90(22.6%)0.9841.01(0.59,1.73)
TT4(2.0%)6(1.5%)0.5002.17(0.23,20.75)
CT+TT vs. CC//0.8881.04(0.61,1.77)
TT vs.CT+CC//0.4952.22(0.23,21.93)
COX-2rs5275N=209N=3960.848
AA139(66.5%)270(68.2%)/1(Ref)
GA65(31.1%)115(29.0%)0.7551.08(0.66,1.78)
GG5(2.4%)11(2.8%)0.9450.94(0.16,5.48)
GA+GG vs. AA//0.7751.07(0.66,1.75)
GG vs. GA+AA//0.9270.92(0.16,5.22)
COX-2rs20417N=208N=3930.881
CC188(90.4%)358(91.1%)/1(Ref)
CG19(9.1%)34(8.7%)0.7550.87(0.37,2.05)
GG1(0.5%)1(0.3%)0.8672.34(0.00,47610.96)
CG+GG vs.CC//0.7670.88(0.38,2.06)
GG vs.CG+CC//0.8602.30(0.00,28090.30)
BCL2rs2279115N=209N=3950.470
GG96(45.9%)166(42.0%)/1(Ref)
GT88(42.1%)169(42.8%)0.9441.02(0.63,1.64)
TT25(12.0%)60(15.2%)0.2180.61(0.28,1.34)
GT+TT vs.GG//0.7280.92(0.58,1.46)
TT vs.GT+GG//0.2100.64(0.32,1.28)

Association of 28 inflammation-associated gene SNPs with HNSCC risk.

In the case group and the control group, there were significantly more people carrying the AKT1 rs1130233 heterozygous CT genotype than those carrying the wild type and the mutant type(P=0.020). The carriers of the AKT1 rs1130233 TT genotype, dominance model (CT+TT vs. CC), recessive model (TT vs. CT+CC), or the AKT1 rs2494732 CC genotype had reduced risk of HNSCC (P=0.014, P=0.041, P=0.046, P=0.043), whereas those with the PIGR rs291097 GA genotype, dominance model (GA+ AA vs. GG), or PIGR rs291102 dominance model (GA+ AA vs. GG) had an increased risk of HNSCC (P=0.025, P=0.025, P=0.040).

Stratified Analysis of the Association of 28 Inflammation-Associated Gene SNPs With HNSCC Risk

In stratified analyses, we found that the IL-1RN rs419598 TT genotype and dominance model (CT+TT vs. CC) conferred a 0.12-fold and 0.16-fold reduction in HNSCC progression, respectively, in individuals older than age 60. However, in those age 60 or younger, the AKT1 rs1130233 TT genotype and dominance model (CT+TT vs. CC), IL-21R rs2189521 CT genotype and dominance model (CT+ CC vs. TT), and BCL2 rs2279115 recessive model (TT vs. GT+GG) conferred a 0.48-fold, 0.57-fold, 0.61-fold, 0.60-fold, and 0.49-fold reduction in HNSCC progression, respectively. In addition, in men, the AKT1 rs1130233 TT genotype and dominance model (CT+TT vs. CC) and the BCL2 rs2279115 TT genotype and recessive model (TT vs. GT+GG) conferred a 0.37-fold, 0.43-fold, 0.37-fold, and 0.41-fold reduction in HNSCC progression, respectively. In women, the IL-21R rs2189521 CT genotype and dominance model (CT+TT vs. TT) conferred a 0.39-fold and 0.43-fold reduction in HNSCC progression, respectively. However, the PIGR rs291097 GA genotype and dominance model (GA+AA vs. GG) and the TNF rs1800630 AA genotype conferred a 3.43-fold, 3.43-fold, and 9.42-fold increase in HNSCC progression, respectively. All these stratified analysis results are shown in Table 3.

Table 3

GenetypeSNPCasesControlsP valueP valueOR (95%CI)
Age>60
AKT1rs1130233N=84N=170.332
CC21(25.0%)2(11.8%)/1(Ref)
CT41(48.8%)8(47.1%)0.6100.64(0.124,3.52)
TT22(26.2%)7(41.2%)0.1500.29(0.05,1.57)
CT+TT vs. CC//0.3020.44(0.09,2.10)
TT vs.CT+CC//0.1650.45(0.15,1.38)
AKT1rs2494732N=85N=170.460
TT7(8.2%)0(0%)/1(Ref)
CT39(45.9%)9(52.9%)NA3.55×10-8(3.55×10-8,3.55×10-8)
CC39(45.9%)8(47.1%)NA2.74×10-8(2.74×10-8,2.74×10-8)
CT+CC vs. TT//NA8.80×10-8(8.80×10-8,8.80×10-8)
CC vs.CT+TT//0.8510.90(0.31,2.60)
CR2rs3813946N=85N=170.684
TT62(72.9%)14(82.4%)/1(Ref)
CT22(25.9%)3(17.6%)0.4421.70(0.44,6.57)
CC1(1.2%)0(0%)NANA
CT+CC vs. TT//0.4111.76(0.46,6.79)
CC vs.CT+TT//NANA
IL10rs1800871N=83N=170.186
AA37(44.6%)5(29.4%)/1(Ref)
GA40(48.2%)12(70.6%)0.1760.45(1.14,1.43)
GG6(7.2%)0(0%)NANA
GA+GG vs. AA//0.2580.52(0.17,1.62)
GG vs. GA+AA0.1/NANA
IL10rs1800872N=83N=170.186
TT37(44.6%)5(29.4%)/1(Ref)
GT40(48.2%)12(70.6%)0.1760.45(0.14,1.43)
GG6(7.2%)0(0%)NANA
GT+GG vs.TT//0.2580.52(0.17,1.62)
GG vs.GT+TT//NANA
IL10rs1800896N=84N=170.806
TT72(85.7%)14(82.4%)/1(Ref)
CT11(13.1%)3(17.6%)0.6480.72(0.17,2.96)
CC1(1.2%)0(0%)NANA
CT+CC vs. TT//0.7190.77(0.19,3.15)
CC vs.CT+TT//NANA
IL1Ars17561N=84N=170.764
CC63(75.0%)14(82.4%)/1(Ref)
CA20(23.8%)3(17.6%)0.7331.27(0.32,5.02)
AA1(1.2%)0(0%)NANA
CA+AA vs. CC//0.6311.40(0.36,5.44)
AA vs.CA+CC//NANA
IL1Brs1143627N=85N=170.979
AA19(22.4%)4(23.5%)/1(Ref)
AG44(51.8%)9(52.9%)0.8960.92(0.24,3.46)
GG22(25.9%)4(23.5%)1.0001.00(0.21,4.79)
AG+GG vs. AA//0.9620.97(0.28,3.40)
GG vs. AG+AA//0.8901.09(0.32,3.75)
IL1Brs16944N=84N=170.974
GG19(22.6%)4(23.5%)/1(Ref)
GA43(51.2%)9(52.9%)0.9600.97(0.26,3.62)
AA22(26.2%)4(23.5%)0.9561.05(0.22,5.00)
GA+AA vs. GG//0.9881.01(0.29,3.51)
AA vs.GA+GG//0.8731.11(0.32,3.80)
IL1Brs1143634N=84N=170.610
GG80(95.2%)16(94.1%)/1(Ref)
GA4(4.8%)1(5.9%)0.9270.90(0.09,8.89)
AA0(0%)0(0%)NANA
GA+AA vs. GG//0.9270.90(0.09,8.89)
AA vs.GA+GG//NANA
IL1RNrs419598N=63N=160.007
TT59(93.7%)11(68.8%)/1(Ref)
CT3(4.8%)5(31.3%)0.0130.12(0.02,0.64)
CC1(1.6%)0(0%)NANA
CT+CC vs. TT//0.0220.16(0.03,0.77)
CC vs.CT+TT//NANA
IL21Rrs2189521N=85N=170.404
TT52(61.2%)13(76.5%)/1(Ref)
CT29(34.1%)4(23.5%)0.2881.95(0.57,6.66)
CC4(4.7%)0(0%)NANA
CT+CC vs. TT//0.2032.21(0.65,7.50)
CC vs.CT+TT//NANA
IL4rs2243250N=85N=170.446
CC4(4.7%)0(0%)/1(Ref)
CT29(34.1%)8(47.1%)NANA
TT52(61.2%)9(52.9%)NANA
CT+TT vs. CC//NANA
TT vs.CT+CC//0.5301.40(0.49,4.05)
IL4rs2227284N=85N=170.293
TT60(70.6%)9(52.9%)/1(Ref)
GT24(28.2%)8(47.1%)0.1260.43(0.15,1.27)
GG1(1.2%)0(0%)NANA
GT+GG vs.TT//0.1410.44(0.15,1.31)
GG vs.GT+TT//NANA
IL4RArs1801275N=83N=170.901
AA63(75.9%)13(76.5%)/1(Ref)
GA19(22.9%)4(23.5%)0.8320.87(0.25,3.07)
GG1(1.2%)0(0%)NANA
GA+GG vs. AA//0.8850.91(0.26,3.20)
GG vs. GA+AA//NANA
IL6rs1800796N=85N=170.809
GG17(20.0%)4(23.5%)/1(Ref)
CG32(37.6%)5(29.4%)0.2612.57(0.50,13.38)
CC36(42.4%)8(47.1%)0.8941.10(0.29,4.19)
CG+CC vs.GG//0.5711.45(0.40,5.18)
CC vs.CG+GG//0.6340.77(0.27,2.24)
PIGRrs291097N=84N=170.321
GG78(92.9%)17(100%)/1(Ref)
GA6(7.1%)0(0.0%)NANA
AA0(0%)0(0.0%)NANA
GA+AA vs. GG//NANA
AA vs.GA+GG//NANA
PIGRrs291102N=85N=170.383
GG69(81.2%)15(88.2%)/1(Ref)
GA16(18.8%)2(11.8%)0.6301.48(0.30,7.34)
AA0(0%)0(0%)NANA
GA+AA vs. GG//0.6301.48(0.30,7.34)
AA vs.GA+GG//NANA
TNFrs1799964N=85N=170.996
TT51(60.0%)10(58.8%)/1(Ref)
CT29(34.1%)5(35.3%)0.9341.05(0.34,3.27)
CC5(5.9%)1(5.9%)0.9250.89(0.09,9.22)
CT+CC vs. TT//0.8991.07(0.36,3.18)
CC vs.CT+TT//0.9310.91(0.10,8.49)
TNFrs1800629N=85N=170.833
GG0(0%)0(0%)/1(Ref)
GA84(98.8%)17(100%)NANA
AA1(1.2%)0(0%)NANA
GA+AA vs. GG//NANA
AA vs.GA+GG//NANA
TNFRSF1Ars4149570N=82N=170.513
CC21(25.6%)6(35.3%)/1(Ref)
CA36(43.9%)8(47.1%)0.5691.42(0.42,4.81)
AA25(30.5%)3(17.6%)0.2582.40(0.53,10.90)
CA+AA vs. CC//0.3601.70(0.55,5.27)
AA vs.CA+CC//0.3301.95(0.51,7.48)
TNFSF7rs7259857N=85N=170.175
TT63(74.1%)15(88.2%)/1(Ref)
CT22(25.9%)2(11.8%)0.2532.49(0.52,11.90)
CC0(0%)0(0%)NANA
CT+CC vs. TT//0.2532.49(0.52,11.90)
CC vs.CT+TT//NANA
TNFrs361525N=85N=170.267
GG77(90.6%)14(82.4%)/1(Ref)
GA8(9.4%)3(17.6%)0.4520.57(0.13,2.49)
AA0(0%)0(0%)NANA
GA+AA vs. GG//0.4520.57(0.13,2.49)
AA vs.GA+GG//NANA
TNFrs1800630N=85N=170.731
CC57(67.1%)12(70.6%)/1(Ref)
CA25(29.4%)5(29.4%)0.8291.14(0.36,3.64)
AA3(3.5%)0(0%)NANA
CA+AA vs. CC//0.7061.25(0.39,3.96)
AA vs.CA+CC//0.3272.06(0.49,8.75)
TNFrs1799724N=82N=170.806
CC62(75.6%)13(76.5%)/1(Ref)
CT18(22.0%)4(23.5%)0.9700.98(0.28,3.44)
TT2(2.4%)0(0%)NANA
CT+TT vs. CC//0.8721.11(0.32,3.86)
TT vs.CT+CC//NANA
COX-2rs5275N=85N=160.210
AA61(71.8%)8(50.0%)/1(Ref)
GA22(25.9%)7(43.8%)0.0960.37(1.12,1.19)
GG2(2.4%)1(6.3%)0.1350.13(0.01,1.88)
GA+GG vs. AA//0.0660.35(0.11,1.07)
GG vs. GA+AA//0.2850.25(0.02,3.13)
COX-2rs20417N=85N=170.557
CC76(89.4%)14(82.4%)/1(Ref)
CG8(9.4%)3(17.6%)0.2170.39(0.09,1.74)
GG1(1.2%)0(0%)NANA
CG+GG vs.CC//0.2690.43(0.10,1.91)
GG vs.CG+CC//NANA
BCL2rs2279115N=85N=170.355
GG38(44.7%)5(29.4%)/1(Ref)
GT34(40.0%)10(58.8%)0.1490.41(0.12,1.38)
TT13(15.3%)2(11.8%)0.8510.84(0.14,4.96)
GT+TT vs.GG//0.2280.50(0.16,1.55)
TT vs.GT+GG//0.7031.37(0.27,6.80)
Age≤60
AKT1rs1130233N=124N=3830.031
CC37(29.8%)75(19.6%)/1(Ref)
CT57(46.0%)181(47.3%)0.0070.64(0.39,1.05)
TT30(24.2%)127(33.2%)0.0140.48(0.27,0.86)
CT+TT vs. CC//0.0210.57(0.36,0.92)
TT vs.CT+CC//0.0800.66(0.41,1.05)
AKT1rs2494732N=124N=3780.212
TT11(8.9%)27(7.1%)/1(Ref)
CT58(46.8%)149(39.4%)0.7650.89(0.41,1.93)
CC55(44.4%)202(53.4%)0.1910.59(0.27,1.30)
CT+CC vs. TT//0.3790.71(0.34,1.51)
CC vs.CT+TT//0.0850.69(0.46,1.05)
CR2rs3813946N=124N=3790.497
TT92(74.2%)299(78.9%)/1(Ref)
CT31(25.0%)76(20.1%)0.3331.27(0.78,2.07)
CC1(0.8%)4(1.1%)0.7490.70(0.08,6.40)
CT+CC vs. TT//0.3821.24(0.77,2.00)
CC vs.CT+TT//0.6940.64(0.70,5.90)
IL10rs1800871N=125N=3830.913
AA53(42.4%)159(41.5%)/1(Ref)
GA58(46.4%)185(48.3%)0.8010.95(0.61,1.46)
GG14(11.2%)39(10.2%)0.9961.00(0.50,2.00)
GA+GG vs. AA//0.8250.95(0.63,1.45)
GG vs. GA+AA//0.9431.02(0.53,1.98)
IL10rs1800872N=125N=3830.913
TT53(42.4%)159(41.5%)/1(Ref)
GT58(46.4%)185(48.3%)0.8010.95(0.61,1.46)
GG14(11.2%)39(10.2%)0.9961.00(0.50,2.00)
GT+GG vs.TT//0.8250.95(0.63,1.45)
GG vs.GT+TT//0.9431.02(0.53,1.98)
IL10rs1800896N=125N=3830.651
TT102(81.6%)308(80.4%)/1(Ref)
CT22(17.6%)74(19.3%)0.5040.83(0.49,1.42)
CC1(0.8%)1(0.3%)0.3823.62(0.20,64.97)
CT+CC vs. TT//0.5820.86(0.51,1.46)
CC vs.CT+TT//0.3740.67(0.21,64.70)
IL1Ars17561N=124N=3830.932
CC103(83.1%)313(81.7%)/1(Ref)
CA20(16.1%)66(17.2%)0.7850.93(0.53,1.62)
AA1(0.8%)4(1.0%)0.6790.63(0.07,5.79)
CA+AA vs. CC//0.7250.91(0.53,1.56)
AA vs.CA+CC//0.7030.65(0.07,5.98)
IL1Brs1143627N=123N=3770.768
AA32(26.0%)107(28.4%)/1(Ref)
AG63(51.2%)179(47.5%)0.5501.16(0.71,1.91)
GG28(22.8%)91(24.1%)0.9501.02(0.57,1.83)
AG+GG vs. AA//0.6541.11(0.70,1.78)
GG vs. AG+AA//0.7390.92(0.56,1.50)
IL1Brs16944N=125N=3800.883
GG33(26.4%)107(28.2%)/1(Ref)
GA63(50.4%)182(47.9%)0.6781.11(0.68,1.82)
AA29(23.2%)91(23.9%)0.9531.02(0.57,1.81)
GA+AA vs. GG//0.7551.08(0.68,1.71)
AA vs.GA+GG//0.8190.95(0.58,1.54)
IL1Brs1143634N=125N=3830.838
GG119(95.2%)365(95.3%)/1(Ref)
GA6(4.8%)17(4.4%)0.8581.09(0.41,2.89)
AA0(0%)1(0.3%)NANA
GA+AA vs. GG//0.9131.06(0.40,2.77)
AA vs.GA+GG//NANA
IL1RNrs419598N=80N=3770.919
TT69(86.3%)325(86.2%)/1(Ref)
CT10(12.5%)49(13.0%)0.8701.06(0.51,2.23)
CC1(1.3%)3(0.8%)0.7641.42(0.14,14.18)
CT+CC vs. TT//0.8151.09(0.53,2.22)
CC vs.CT+TT//0.7761.40(0.14,13.97)
IL21Rrs2189521N=123N=3780.049
TT79(64.2%)195(51.6%)/1(Ref)
CT38(30.9%)156(41.3%)0.0310.61(0.39,0.96)
CC6(4.9%)27(7.1%)0.2080.55(0.22,1.39)
CT+CC vs. TT//0.0190.60(0.39,0.92)
CC vs.CT+TT//0.3810.66(0.26,1.67)
IL4rs2243250N=124N=3780.371
CC5(4.0%)13(3.4%)/1(Ref)
CT47(37.9%)119(31.5%)0.9220.95(0.31,2.88)
TT72(58.1%)246(65.1%)0.5580.72(0.25,2.14)
CT+TT vs. CC//0.6760.80(0.27,2.33)
TT vs.CT+CC//0.1890.75(0.49,1.15)
IL4rs2227284N=124N=3780.216
TT84(67.7%)285(75.4%)/1(Ref)
GT36(29.0%)86(22.8%)0.3231.27(0.79,2.02)
GG4(3.2%)7(1.9%)0.2662.08(0.57,7.59)
GT+GG vs.TT//0.2311.32(0.84,2.07)
GG vs.GT+TT//0.3101.94(0.54,6.99)
IL4RArs1801275N=124N=3830.239
AA89(71.8%)259(67.6%)/1(Ref)
GA34(27.4%)110(28.7%)0.8700.96(0.61,1.53)
GG1(0.8%)14(3.7%)0.1650.23(0.03,1.82)
GA+GG vs. AA//0.5970.88(0.56,1.40)
GG vs. GA+AA//0.1700.24(0.03,1.85)
IL6rs1800796N=124N=3780.411
GG9(7.3%)43(11.4%)/1(Ref)
CG55(44.4%)165(43.7%)0.4441.37(0.61,3.07)
CC60(48.4%)170(45.0%)0.2811.54(0.70,3.38)
CG+CC vs.GG//0.3381.45(0.68,3.11)
CC vs.CG+GG//0.6161.11(0.74,1.68)
PIGRrs291097N=125N=3830.077
GG110(88.0%)355(92.7%)/1(Ref)
GA15(12.0%)28(7.3%)0.1081.74(0.86,3.44)
AA0(0.0%)0(0.0%)NANA
GA+AA vs. GG//0.1081.74(0.86,3.44)
AA vs.GA+GG//NANA
PIGRrs291102N=123N=3790.591
GG96(78.0%)308(81.3%)/1(Ref)
GA25(20.3%)68(17.9%)0.3761.27(0.75,2.14)
AA2(1.6%)3(0.8%)NANA
GA//0.2841.32(0.79,2.21)
AA vs.GA+GG//NANA
TNFrs1799964N=124N=3780.775
TT73(58.9%)236(62.4%)/1(Ref)
CT45(36.3%)126(33.3%)0.5371.15(0.74,1.78)
CC6(4.8%)16(4.2%)0.6661.25(0.46,3.38)
CT+CC vs. TT//0.4931.16(0.76,1.77)
CC vs.CT+TT//0.7401.18(0.44,3.15)
TNFrs1800629N=124N=3790.567
GG0(0%)0(0%)/1(Ref)
GA124(100%)377(99.5%)NANA
AA0(0%)2(0.5%)NANA
GA+AA vs. GG//NANA
AA vs.GA+GG//NANA
TNFRSF1Ars4149570N=123N=3780.256
CC22(17.9%)95(25.1%)/1(Ref)
CA66(53.7%)186(49.2%)0.2041.43(0.82,2.50)
AA35(28.5%)97(25.7%)0.1571.55(0.85,2.84)
CA+AA vs. CC//0.1421.48(0.88,2.50)
AA vs.CA+CC//0.4681.19(0.75,1.89)
TNFSF7rs7259857N=124N=3790.379
TT103(83.1%)307(81.0%)/1(Ref)
CT18(14.5%)68(17.9%)0.3470.76(0.43,1.35)
CC3(2.4%)4(1.1%)NANA
CT+CC vs. TT//0.5390.84(0.49,1.45)
CC vs.CT+TT//NANA
TNFrs361525N=124N=3790.506
GG114(91.9%)350(92.3%)/1(Ref)
GA10(8.1%)29(7.7%)0.9570.98(0.46,2.10)
AA0(0%)0(0%)NANA
GA+AA vs. GG//0.9570.98(0.46,2.10)
AA vs.GA+GG//NANA
TNFrs1800630N=122N=3780.978
CC84(68.9%)262(69.3%)/1(Ref)
CA34(27.9%)105(27.8%)0.8241.06(0.66,1.69)
AA4(3.3%)11(2.9%)0.7951.17(0.35,3.90)
CA+AA vs. CC//0.7971.06(0.68,1.66)
AA vs.CA+CC//0.3272.06(0.49,8.75)
TNFrs1799724N=123N=3810.915
CC91(74.0%)289(75.9%)/1(Ref)
CT30(24.4%)86(22.6%)0.7371.09(0.67,1.77)
TT2(1.6%)6(1.5%)0.9300.93(0.18,4.79)
CT+TT vs. CC//0.7641.08(0.67,1.73)
CT+TT vs. CC//0.9080.91(1.18,4.67)
COX-2rs5275N=124N=3800.418
AA78(62.9%)262(68.9%)/1(Ref)
GA43(34.7%)108(28.4%)0.3101.26(0.81,1.96)
GG3(2.4%)10(2.6%)0.9780.98(0.26,3.75)
GA+GG vs. AA//0.3431.23(0.80,1.90)
GG vs. GA+AA//0.8930.91(0.24,3.45)
COX-2rs20417N=123N=3760.826
CC112(91.1%)344(91.5%)/1(Ref)
CG11(8.9%)31(8.2%)0.9680.99(0.47,2.05)
GG0(0.0%)1(0.3%)NANA
CG+GG vs.CC//0.9290.97(0.47,2.01)
GG vs.CG+CC//NANA
BCL2rs2279115N=124N=3780.276
GG58(46.8%)161(42.6%)/1(Ref)
GT54(43.5%)159(42.1%)0.9201.02(0.66,1.59)
TT12(9.7%)58(15.3%)0.0570.51(0.25,1.02)
GT+TT vs.GG//0.4620.86(0.57,1.30)
TT vs.GT+GG//0.0370.49(0.25,0.96)
Male
AKT1rs1130233N=160N=2250.028
CC48(30.0%)42(18.7%)/1(Ref)
CT71(44.4%)109(48.4%)0.0880.49(0.21,1.11)
TT41(25.6%)74(32.9%)0.0140.37(0.17,0.82)
CT+TT vs. CC//0.0250.43(0.21,0.90)
TT vs.CT+CC//0.0620.53(0.28,1.03)
AKT1rs2494732N=161N=2220.516
TT13(8.1%)13(5.9%)/1(Ref)
CT72(44.7%)93(41.9%)0.2490.44(0.11,1.79)
CC76(47.2%)116(52.3%)0.1430.37(0.10,1.40)
CT+CC vs. TT//0.1750.40(0.11,1.50)
CC vs.CT+TT//0.2920.73(0.40,1.32)
CR2rs3813946N=161N=2220.226
TT115(71.4%)175(78.8%)/1(Ref)
CT44(27.3%)44(19.8%)0.9151.04(0.52,2.07)
CC2(1.2%)3(1.4%)0.1520.21(0.02,1.78)
CT+CC vs. TT//0.8290.93(0.47,1.82)
CC vs.CT+TT//0.1300.19(0.02,1.64)
IL10rs1800871N=160N=2250.876
AA68(42.5%)92(40.9%)/1(Ref)
GA76(47.5%)107(47.6%)0.5610.83(0.45,1.54)
GG16(10.0%)26(11.6%)0.4811.49(0.49,4.48)
GA+GG vs. AA//0.7410.90(0.50,1.65)
GG vs. GA+AA//0.3551.63(0.58,4.55)
IL10rs1800872N=160N=2250.876
TT68(42.5%)92(40.9%)/1(Ref)
GT76(47.5%)107(47.6%)0.5610.83(0.45,1.54)
GG16(10.0%)26(11.6%)0.4811.49(0.49,4.48)
GT+GG vs.TT//0.7410.90(0.50,1.65)
GG vs.GT+TT//0.3551.63(0.58,4.55)
IL10rs1800896N=161N=2250.070
TT134(83.2%)175(77.8%)/1(Ref)
CT25(15.5%)50(22.2%)0.2270.63(0.30,1.33)
CC2(1.2%)0(0%)NANA
CT+CC vs. TT//0.3050.68(0.32,1.42)
CC vs.CT+TT//NANA
IL1Ars17561N=160N=2250.237
CC130(81.3%)179(79.6%)/1(Ref)
CA30(18.8%)42(18.7%)0.8600.93(0.42,2.09)
AA0(0%)4(1.8%)NANA
CA+AA vs. CC//0.6920.85(0.39,1.88)
AA vs.CA+CC//NANA
IL1Brs1143627N=160N=2200.281
AA35(21.9%)63(28.6%)/1(Ref)
AG86(53.8%)103(46.8%)0.2801.47(0.73,2.95)
GG39(24.4%)54(24.5%)0.8071.12(0.46,2.75)
AG+GG vs. AA//0.3601.36(0.70,2.65)
GG vs. AG+AA//0.7890.91(0.44,1.86)
IL1Brs16944N=161N=2230.475
GG37(23.0%)63(28.3%)/1(Ref)
GA84(52.2%)105(47.1%)0.3451.40(0.70,2.79)
AA40(24.8%)55(24.7%)0.7241.17(0.48,2.86)
GA+AA vs. GG//0.3951.33(0.69,2.57)
AA vs.GA+GG//0.9330.97(0.48,1.97)
IL1Brs1143634N=161N=2250.388
GG155(96.3%)214(95.1%)/1(Ref)
GA6(3.7%)11(4.9%)0.9790.98(0.23,4.11)
AA0(0.0%)0(0.0%)NANA
GA+AA vs. GG//0.9790.98(0.23,4.11)
AA vs.GA+GG//NANA
IL1RNrs419598N=109N=2200.972
TT98(89.9%)196(89.1%)/1(Ref)
CT10(9.2%)22(10.0%)0.8780.92(0.29,2.87)
CC1(0.9%)2(0.9%)0.6381.88(0.14,26.09)
CT+CC vs. TT//0.9941.00(0.34,2.95)
CC vs.CT+TT//0.6591.80(0.13,24.45)
IL21Rrs2189521N=160N=2220.364
TT98(61.3%)123(55.4%)/1(Ref)
CT55(34.4%)83(37.4%)0.6311.17(0.62,2.21)
CC7(4.4%)16(7.2%)0.7030.78(0.22,2.79)
CT+CC vs. TT//0.7481.11(0.60,2.03)
CC vs.CT+TT//0.6360.74(0.21,2.60)
IL4rs2243250N=161N=2220.736
CC6(3.7%)7(3.2%)/1(Ref)
CT59(36.6%)74(33.3%)0.8550.86(0.17,4.35)
TT96(59.6%)141(63.5%)0.7400.72(0.10,5.13)
CT+TT vs. CC//0.7700.77(0.13,4.42)
TT vs.CT+CC//0.5500.83(0.45,1.53)
IL4rs2227284N=161N=2220.715
TT110(68.3%)154(69.4%)/1(Ref)
GT47(29.2%)65(29.3%)0.9881.00(0.52,1.91)
GG4(2.5%)3(1.4%)0.3723.91(0.20,78.06)
GT+GG vs.TT//0.8721.05(0.55,2.01)
GG vs.GT+TT//0.3663.36(0.24,46.79)
IL4RArs1801275N=159N=2250.609
AA114(71.7%)162(72.0%)/1(Ref)
GA43(27.0%)57(25.3%)0.7451.17(1.13,1.20)
GG2(1.3%)6(2.7%)0.6380.59(0.06,5.44)
GA+GG vs. AA//0.8311.07(0.56,2.07)
GG vs. GA+AA//0.6050.54(0.05,5.50)
IL6rs1800796N=161N=2220.566
GG21(13.0%)22(9.9%)/1(Ref)
CG68(42.2%)92(41.4%)0.6651.26(0.44,3.63)
CC72(44.7%)108(48.6%)0.8561.10(0.39,3.08)
CG+CC vs.GG//0.7401.18(0.44,3.20)
CC vs.CG+GG//0.8040.93(0.51,1.68)
PIGRrs291097N=161N=2250.331
GG146(90.7%)208(92.4%)/1(Ref)
GA15(9.3%)17(7.6%)0.2451.89(0.65,5.49)
AA0(0.0%)0(0.0%)NANA
GA+AA vs. GG//0.2451.89(0.65,5.49)
AA vs.GA+GG//NANA
PIGRrs291102N=160N=2220.613
GG127(79.4%)185(83.3%)/1(Ref)
GA32(20.0%)36(16.2%)0.3041.51(0.69,3.31)
AA1(0.6%)1(0.5%)0.8891.33(0.02,74.43)
GA+AA vs. GG//0.3011.51(0.69,3.27)
AA vs.GA+GG//0.9201.22(0.02,62.17)
TNFrs1799964N=161N=2210.904
TT101(62.7%)135(61.1%)/1(Ref)
CT52(32.3%)76(34.4%)0.6931.14(0.61,2.13)
CC8(5.0%)10(4.5%)0.7281.38(0.22,8.52)
CT+CC vs. TT//0.6461.16(0.63,2.13)
CC vs.CT+TT//0.7581.31(0.23,7.37)
TNFrs1800629N=161N=2210.413
GG101(62.7%)135(61.1%)/1(Ref)
GA0(0%)0(0%)NANA
AA60(37.3%)86(38.9%)NANA
GA+AA vs. GG//NANA
AA vs.GA+GG//0.5410.38(0.02,8.39)
TNFRSF1Ars4149570N=158N=2250.422
CC31(19.6%)57(25.3%)/1(Ref)
CA84(53.2%)110(48.9%)0.0652.14(0.96,4.81)
AA43(27.2%)58(25.8%)0.3001.62(0.65,4.01)
CA+AA vs. CC//0.0901.92(0.90,4.10)
AA vs.CA+CC//0.8170.92(0.47,1.82)
TNFSF7rs7259857N=161N=2220.832
TT126(78.3%)179(80.6%)/1(Ref)
CT33(20.5%)41(18.5%)0.9051.05(0.48,2.31)
CC2(1.2%)2(0.9%)0.2833.86(0.33,45.27)
CT+CC vs. TT//0.6951.16(0.55,2.48)
CC vs.CT+TT//0.2763.95(0.33,46.85)
TNFrs361525N=161N=2220.406
GG146(90.7%)204(91.9%)/1(Ref)
GA15(9.3%)18(8.1%)0.0533.05(0.99,9.42)
AA0(0%)0(0%)NANA
GA+AA vs. GG//0.0533.05(0.98,9.42)
AA vs.GA+GG//NANA
TNFrs1800630N=159N=2220.708
CC115(72.3%)153(68.9%)/1(Ref)
CA40(25.2%)61(27.5%)0.4620.78(0.40,1.52)
AA4(2.5%)8(3.6%)0.4780.46(0.05,3.95)
CA+AA vs. CC//0.3920.75(0.39,1.45)
AA vs.CA+CC//0.5200.50(0.06,4.18)
TNFrs1799724N=160N=2230.997
CC120(75.0%)167(74.9%)/1(Ref)
CT37(23.1%)52(23.3%)0.7771.11(0.55,2.23)
TT3(1.9%)4(1.8%)0.7031.97(0.06,63.57)
CT+TT vs. CC//0.7331.13(0.56,2.26)
TT vs.CT+CC//0.7141.90(0.06,58.99)
COX-2rs5275N=161N=2220.965
AA105(65.2%)144(64.9%)/1(Ref)
GA51(31.7%)72(32.4%)0.9021.04(0.54,1.99)
GG5(3.1%)6(2.7%)0.8001.37(0.12,15.44)
GA+GG vs. AA//0.8671.06(0.56,1.99)
GG vs. GA+AA//0.8081.32(0.14,12.27)
COX-2rs20417N=160N=2190.503
CC142(88.8%)196(89.5%)/1(Ref)
CG17(10.6%)23(10.5%)0.6070.77(0.28,2.12)
GG1(0.6%)0(0%)NANA
CG+GG vs.CC//0.6180.77(0.28,2.13)
GG vs.CG+CC//NANA
BCL2rs2279115N=161N=2220.036
GG75(46.6%)93(41.9%)/1(Ref)
GT68(42.2%)82(36.9%)0.8240.93(0.49,1.75)
TT18(11.2%)47(21.2%)0.0440.37(0.14,0.97)
GT+TT vs.GG//0.3490.75(0.41,1.37)
TT vs.GT+GG//0.0440.41(0.17,0.98)
Female
AKT1rs1130233N=48N=1750.299
CC10(20.8%)35(20.0%)/1(Ref)
CT27(56.3%)80(45.7%)0.9141.05(0.42,2.66)
TT11(22.9%)60(34.3%)0.4570.66(0.22,2.00)
CT+TT vs. CC//0.7920.89(0.36,2.17)
TT vs.CT+CC//0.2880.64(0.28,1.46)
AKT1rs2494732N=48N=1730.118
TT5(10.4%)14(8.1%)/1(Ref)
CT25(52.1%)65(37.6%)0.7160.79(0.22,2.81)
CC18(37.5%)94(54.3%)0.1830.40(0.10,1.54)
CT+CC vs. TT//0.3700.57(0.16,1.96)
CC vs.CT+TT//0.0980.54(0.26,1.12)
CR2rs3813946N=48N=1740.848
TT39(81.3%)138(79.3%)/1(Ref)
CT9(18.8%)35(20.1%)0.5600.75(0.29,1.95)
CC0(0%)1(0.6%)NANA
CT+CC vs. TT//0.5040.73(0.28,1.86)
CC vs.CT+TT//NANA
IL10rs1800871N=48N=1750.790
AA22(45.8%)72(41.1%)/1(Ref)
GA22(45.8%)90(51.4%)0.4720.76(0.36,1.61)
GG4(8.3%)13(7.4%)0.9631.03(0.27,4.00)
GA+GG vs. AA//0.5250.79(0.38,1.64)
GG vs. GA+AA//0.7861.19(0.33,4.28)
IL10rs1800872N=48N=1750.790
TT22(45.8%)72(41.1%)/1(Ref)
GT22(45.8%)90(51.4%)0.4720.76(0.36,1.61)
GG4(8.3%)13(7.4%)0.9631.03(0.27,4.00)
GT+GG vs.TT//0.5250.79(0.38,1.64)
GG vs.GT+TT//0.7831.19(0.33,4.28)
IL10rs1800896N=48N=1750.855
TT40(83.3%)147(84.0%)/1(Ref)
CT8(16.7%)27(15.4%)0.5831.31(0.50,3.47)
CC0(0%)1(0.6%)NANA
CT+CC vs. TT//0.6681.24(0.47,3.24)
CC vs.CT+TT//NANA
IL1Ars17561N=48N=1750.015
CC36(75.0%)148(84.6%)/1(Ref)
CA10(20.8%)27(15.4%)0.4401.44(0.57,3.61)
AA2(4.2%)0(0%)NANA
CA+AA vs. CC//0.2641.66(0.68,4.02)
AA vs.CA+CC//NANA
IL1Brs1143627N=48N=1740.725
AA16(33.3%)48(27.6%)/1(Ref)
AG21(43.8%)85(48.9%)0.1260.52(0.22,1.20)
GG11(22.9%)41(23.6%)0.1120.44(0.16,1.21)
AG+GG vs. AA//0.0600.46(0.20,1.03)
GG vs. AG+AA//0.3220.64(0.27,1.54)
IL1Brs16944N=48N=1740.870
GG15(31.3%)48(27.6%)/1(Ref)
GA22(45.8%)86(49.4%)0.1570.54(0.23,1.27)
AA11(22.9%)40(23.0%)0.1480.47(0.17,1.31)
GA+AA vs. GG//0.0790.48(0.21,1.09)
AA vs.GA+GG//0.3590.66(0.28,1.59)
IL1Brs1143634N=48N=1750.414
GG44(91.7%)167(95.4%)/1(Ref)
GA4(8.3%)7(4.0%)0.5451.61(0.34,7.62)
AA0(0.0%)1(0.6%)NANA
GA+AA vs. GG//0.5631.57(0.34,7.32)
AA vs.GA+GG//NANA
IL1RNrs419598N=34N=1730.183
TT30(88.2%)140(80.9%)/1(Ref)
CT3(8.8%)32(18.5%)0.0600.25(0.06,1.06)
CC1(2.9%)1(0.6%)0.7871.56(0.06,39.87)
CT+CC vs. TT//0.0830.32(0.09,1.16)
CC vs.CT+TT//0.6632.01(0.09,46.98)
IL21Rrs2189521N=48N=1730.044
TT33(68.8%)85(49.1%)/1(Ref)
CT12(25.0%)77(44.5%)0.0220.39(0.17,0.87)
CC3(6.3%)11(6.4%)0.7600.79(0.17,3.59)
CT+CC vs. TT//0.0300.43(0.20,0.92)
CC vs.CT+TT//0.8601.15(0.24,5.56)
IL4rs2243250N=48N=1730.517
CC3(6.3%)6(3.5%)/1(Ref)
CT17(35.4%)53(30.6%)0.6090.63(0.11,3.73)
TT28(58.3%)114(65.9%)0.3600.43(0.07,2.65)
CT+TT vs. CC//0.4320.50(0.09,2.85)
TT vs.CT+CC//0.2810.66(0.32,1.40)
IL4rs2227284N=48N=1730.272
TT34(70.8%)140(80.9%)/1(Ref)
GT13(27.1%)29(16.8%)0.1132.03(0.85,4.85)
GG1(2.1%)4(2.3%)0.6971.69(0.12,23.95)
GT+GG vs.TT//0.1112.00(0.85,4.67)
GG vs.GT+TT//0.7981.40(0.11,18.01)
IL4RArs1801275N=48N=1750.066
AA38(79.2%)110(62.9%)/1(Ref)
GA10(20.8%)57(32.6%)0.4500.72(0.31,1.68)
GG0(0%)8(4.6%)NANA
GA+GG vs. AA//0.2650.62(0.27,1.43)
GG vs. GA+AA//NANA
IL6rs1800796N=48N=1730.469
GG5(10.4%)25(14.5%)/1(Ref)
CG19(39.6%)78(45.1%)0.9961.00(0.29,3.40)
CC24(50.0%)70(40.5%)0.3301.85(0.54,6.37)
CG+CC vs.GG//0.6131.35(0.42,4.33)
CC vs.CG+GG//0.1021.86(0.89,3.90)
PIGRrs291097N=48N=1750.131
GG42(87.5%)164(93.7%)/1(Ref)
GA6(12.5%)11(6.3%)0.0423.43(1.05,11.23)
AA0(0.0%)0(0.0%)NANA
GA+AA vs.GG//0.0423.43(1.05,11.23)
AA vs.GA+GG//NANA
PIGRrs291102N=48N=1740.880
GG38(79.2%)138(79.3%)/1(Ref)
GA9(18.8%)34(19.5%)0.1232.15(0.81,5.67)
AA1(2.1%)2(1.1%)0.2535.18(0.31,87.06)
GA+AA vs. GG//0.0942.22(0.87,5.63)
AA vs.GA+GG//0.3024.27(0.27,67.58)
TNFrs1799964N=48N=1740.137
TT23(47.9%)111(63.8%)/1(Ref)
CT22(45.8%)56(32.2%)0.2611.55(0.72,3.33)
CC3(6.3%)7(4.0%)0.2153.00(0.53,17.02)
CT+CC vs. TT//0.1751.66(0.80,3.45)
CC vs.CT+TT//0.2962.43(0.46,12.89)
TNFrs1800629N=48N=1740.035
GG23(47.9%)111(63.8%)/1(Ref)
GA0(0%)0(0%)NANA
AA25(52.1%)63(36.2%)NANA
GA+AA vs. GG//NANA
AA vs.GA+GG//NANA
TNFRSF1Ars4149570N=47N=1700.253
CC12(25.5%)44(25.9%)/1(Ref)
CA18(38.3%)84(49.4%)0.3440.63(0.24,1.63)
AA17(36.2%)42(24.7%)0.5561.32(0.53,3.31)
CA+AA vs. CC//0.7280.86(0.37,2.01)
AA vs.CA+CC//0.1631.74(0.80,3.80)
TNFSF7rs7259857N=48N=1740.840
TT40(83.3%)143(82.2%)/1(Ref)
CT7(14.6%)29(16.7%)0.8320.89(0.31,2.54)
CC1(2.1%)2(1.1%)0.5782.07(0.16,27.08)
CT+CC vs. TT//0.9690.98(0.36,2.63)
CC vs.CT+TT//0.5782.06(0.16,26.36)
TNFrs361525N=48N=1740.478
GG45(93.8%)160(92.0%)/1(Ref)
GA3(6.3%)4(8.0%)0.2270.41(0.10,1.74)
AA0(0%)0(0%)NANA
GA+AA vs. GG//0.2270.41(0.10,1.74)
AA vs.GA+GG//NANA
TNFrs1800630N=48N=1730.056
CC26(54.2%)121(69.9%)/1(Ref)
CA19(39.6%)49(28.3%)0.1411.81(0.82,3.97)
AA3(6.3%)3(1.7%)0.0369.42(1.16,76.25)
CA+AA vs. CC//0.0592.075(0.97,4.40)
AA vs.CA+CC//0.0566.71(0.95,47.39)
TNFrs1799724N=45N=1750.781
CC33(73.3%)135(77.1%)/1(Ref)
CT11(24.4%)38(21.7%)0.8720.93(0.39,2.25)
TT1(2.2%)2(1.1%)0.5242.59(0.14,48.43)
CT+TT vs. CC//0.9710.98(0.42,2.33)
TT vs.CT+CC//0.5132.75(0.13,57.01)
COX-2rs5275N=48N=1740.431
AA34(70.8%)126(72.4%)/1(Ref)
GA14(29.2%)43(24.7%)0.6431.22(0.54,2.72)
GG0(0%)5(2.9%)NANA
GA+GG vs. AA//0.7451.14(0.51,2.53)
GG vs. GA+AA//NANA
COX-2rs20417N=48N=1740.739
CC46(95.8%)162(93.1%)/1(Ref)
CG2(4.2%)11(6.3%)0.9121.10(0.20,6.24)
GG0(0.0%)1(0.6%)NANA
CG+GG vs.CC//0.9321.08(0.19,6.05)
GG vs.CG+CC//NANA
BCL2rs2279115N=48N=1730.263
GG21(43.8%)73(42.2%)/1(Ref)
GT20(41.7%)87(50.3%)0.8991.05(0.49,2.25)
TT7(14.6%)13(7.5%)0.3541.89(0.49,7.26)
GT+TT vs.GG//0.6161.21(0.58,2.52)
TT vs.GT+GG//0.3141.83(0.56,5.98)

Stratified analysis of the association of 28 inflammation-associated gene SNPs with HNSCC risk.

In the group older than 60 years, the IL1RN rs419598 TT genotype was the most in the case group and the control group (P=0.007). In the subgroup younger than 60 years old, AKT1 rs1130233 CT genotype and IL21R rs2189521 TT wild-type was the most in the case group and the control group (P=0.031, P=0.049). Among men, AKT1 rs1130233 CT heterozygosity (P=0.028) and BCL2 rs2279115 GG genotype were the most (P=0.036) in the case group and the control group. Among women, IL1A rs17561 CC genotype and IL-21R rs2189521 TT genotype were the most among the case group and the control group (P=0.015, P=0.044). However, normal people with TNF rs1800629 GG genotype was the most in control group, and AA gene was the most in cases (P=0.044).In the same time, in stratified analyses, we found that the IL-1RN rs419598 TT genotype and dominance model (CT+TT vs. CC) conferred a 0.12-fold and 0.16-fold reduction in HNSCC progression, respectively, in individuals older than age 60(P=0.013, P=0.022). However, in those age 60 or younger, the AKT1 rs1130233 TT genotype and dominance model (CT+TT vs. CC) (P=0.014, P=0.021) , IL-21R rs2189521 CT genotype and dominance model (CT+ CC vs. TT) (P=0.031, P=0.019), and BCL2 rs2279115 recessive model (TT vs. GT+GG) (P=0.037) conferred a 0.48-fold, 0.57-fold, 0.61-fold, 0.60-fold, and 0.49-fold reduction in HNSCC progression, respectively. In addition, in men, the AKT1 rs1130233 TT genotype and dominance model (CT+TT vs. CC) (P=0.014, P=0.025)and the BCL2 rs2279115 TT genotype and recessive model (TT vs. GT+GG) (P=0.044, P=0.044)conferred a 0.37-fold, 0.43-fold, 0.37-fold, and 0.41-fold reduction in HNSCC progression, respectively. In women, the IL-21R rs2189521 CT genotype and dominance model (CT+TT vs. TT) conferred a 0.39-fold and 0.43-fold reduction in HNSCC progression(P=0.022, P=0.030), respectively. However, the PIGR rs291097 GA genotype and dominance model (GA+AA vs. GG) (P=0.042, P=0.042) and the TNF rs1800630 AA genotype (P=0.036) conferred a 3.43-fold, 3.43-fold, and 9.42-fold increase in HNSCC progression, respectively.

Association of 28 Inflammation-Associated Gene SNPs With Radiotherapy Sensitivity of HNSCC Patients

We further analyzed the correlation between 28 SNPs and radiotherapy sensitivity of HNSCC individuals. We found that, compared with those with other genotypes, HNSCC patients carrying the IL-4RA rs1801275 AA wild-type genotype (40.9%) were more sensitive to radiotherapy (Table 4). There were no significant differences observed in the correlation analysis between the other 27 SNPs and radiotherapy sensitivity in HNSCC patients.

Table 4

GenetypeNon-sensitivitySensitivityP value
AKT1rs1130233N=17N=280.363
CC7(15.6%)7(15.6%)
CT5(11.1%)14(31.1%)
TT5(11.1%)7(15.6%)
AKT1rs2494732N=17N=280.560
TT2(4.4%)16(35.6%)
CT8(17.8%)9(20.0%)
CC7(15.6%)3(6.7%)
CR2rs3813946N=17N=280.645
TT13(28.9%)23(51.1%)
CT4(8.9%)5(11.1%)
CC0(0%)0(0%)
IL10rs1800871N=16N=280.809
AA9(20.5%)14(31.8%)
GA6(13.6%)13(29.5%)
GG1(2.3%)1(2.3%)
IL10rs1800872N=16N=280.809
TT9(20.5%)14(31.8%)
GT6(13.6%)13(29.5%)
GG1(2.3%)1(2.3%)
IL10rs1800896N=17N=280.814
TT15(33.3%)24(53.3%)
CT1(2.2%)3(6.7%)
CC1(2.2%)1(2.2%)
IL1Ars17561N=17N=280.342
CC11(24.4%)21(46.7%)
CA6(13.3%)7(15.6%)
AA0(0%)0(0%)
IL1Brs1143627N=17N=280.115
AA1(2.2%)9(20.0%)
AG11(24.4%)14(31.1%)
GG5(11.1%)5(11.1%)
IL1Brs16944N=17N=280.274
GG2(4.4%)9(20.0%)
GA10(22.2%)14(31.1%)
AA5(11.1%)5(11.1%)
IL1Brs1143634N=17N=280.316
GG15(33.3%)27(60.0%)
GA2(4.4%)1(2.2%)
AA0(0%)0(0%)
IL1RNrs419598N=14N=240.731
TT13(34.2%)21(55.3%)
CT1(2.6%)2(5.3%)
CC0(0%)1(2.6%)
IL21Rrs2189521N=17N=280.505
TT11(24.4%)18(40.0%)
CT6(13.3%)8(17.8%)
CC0(0%)2(4.4%)
IL4rs2243250N=17N=280.108
CC2(4.4%)1(2.2%)
CT10(22.2%)10(22.2%)
TT5(11.1%)17(37.8%)
IL4rs2227284N=17N=280.057
TT6(13.3%)20(44.4%)
GT10(22.2%)7(15.6%)
GG1(2.2%)1(2.2%)
IL4RArs1801275N=16N=280.030
AA15(34.1%)18(40.9%)
GA1(2.3%)10(22.7%)
GG0(0%)0(0%)
IL6rs1800796N=17N=280.814
GG2(4.4%)5(11.1%)
CG7(15.6%)12(26.7%)
CC8(17.8%)11(24.4%)
PIGRrs291097N=17N=280.462
GG13(28.9%)23(51.1%)
GA4(8.9%)5(11.1%)
AA0(0%)0(0%)
PIGRrs291102N=17N=280.605
GG12(26.7%)20(44.4%)
GA5(11.1%)8(17.8%)
AA0(0%)0(0%)
TNFrs1799964N=17N=280.571
TT7(15.6%)16(35.6%)
CT8(17.8%)10(22.2%)
CC2(4.4%)2(4.4%)
TNFrs1800629N=17N=28NA
GG0(0%)0(0%)
GA17(37.8%)28(62.2%)
AA0(0%)0(0%)
TNFRSF1Ars4149570N=16N=270.347
CC2(4.7%)8(18.6%)
CA8(18.6%)13(30.2%)
AA6(14.0%)6(14.0%)
TNFSF7rs7259857N=17N=280.462
TT15(33.3%)23(51.1%)
CT2(4.4%)5(11.1%)
CC0(0%)0(0%)
TNFrs361525N=17N=280.407
GG14(31.1%)25(55.6%)
GA3(6.7%)3(6.7%)
AA0(0%)0(0%)
TNFrs1800630N=17N=280.761
CC10(22.2%)19(42.2%)
CA6(13.3%)7(15.6%)
AA1(2.2%)2(4.4%)
TNFrs1799724N=17N=280.498
CC15(33.3%)21(46.7%)
CT1(2.2%)5(11.1%)
TT1(2.2%)2(4.4%)
COX-2rs5275N=17N=280.496
AA13(28.9%)20(44.4%)
GA4(8.9%)8(17.8%)
GG0(0%)0(0%)
COX-2rs20417N=17N=280.378
CC16(35.6%)28(62.2%)
CG1(2.2%)0(0%)
GG0(0%)0(0%)
BCL2rs2279115N=17N=280.333
GG8(17.8%)19(42.2%)
GT7(15.6%)6(13.3%)
TT2(4.4%)37(6.7%)

Association of 28 inflammation-associated gene SNPs with radiotherapy sensitivity of HNSCC patients.

Compared with those with other genotypes, HNSCC patients carrying the IL-4RA rs1801275 AA wild-type genotype (40.9%) were more sensitive to radiotherapy (P=0.030).

Association of Clinicopathological Parameters With Radiotherapy Sensitivity of HNSCC Patients

We further analyzed the potential correlations between clinicopathological parameters and radiotherapy sensitivity of HNSCC patients. We found that age ≤ 60 years, non-smoker status, and normal levels of SCC were associated with increased radiotherapy sensitivity of HNSCC patients (P=0.033; P=0.033; P=0.030, respectively) (Table 5). There were no significant differences observed in the correlation analysis between other clinicopathological parameters and radiotherapy sensitivity in HNSCC patients.

Table 5

CharacteristicsNon-sensitivitySensitivityP value
Age0.033
Age≤60619
Age>60119
Gender0.277
Female411
Male1317
T stage0.440
1-2812
3-4615
N stage0.646
Negative11
Positive1427
M stage0.265
Negative1525
Positive03
Clinical stage0.552
I–II22
III–IV1426
Smoking0.033
No619
Yes119
Drinking0.384
No1020
Yes78
Family history of cancer0.869
No1322
Yes46
SCC0.030
Normal917
Increased51
CEA0.474
Normal810
Increased10
CYFRA0.197
Normal13
Increased42
EBV0.800
Negative311
Positive01
Blood type0.900
A36
B33
AB11
O22

Association of clinicopathological parameters with radiotherapy sensitivity of HNSCC patients.

We found that age ≤ 60 years, non-smoker status, and normal levels of SCC were associated with increased radiotherapy sensitivity of HNSCC patients (P=0.033; P=0.033; P=0.030, respectively).

Association of 28 Inflammation-Associated Gene SNPs With Clinicopathological Parameters of HNSCC Patients

Among the SNPs related to the risk of HNSCC, the heterozygous and dominant model of AKT1 rs1130233 were significantly related to lymph node metastasis and non-distant metastasis. The recessive model of AKT1 rs2494732 was significantly related to male sex, stage III-IV disease, and normal carcinoembryonic antigen (CEA) levels. The IL-1RN rs419598 wild-type genotype was significantly related to stage III-IV disease, the PIGR rs291102 wild-type genotype was significantly related to normal levels of cytokeratin fragment 19 (CYFRA), and the BCL2 rs2279115 wild-type genotype was significantly related to lymph node metastasis. In addition, we found that the IL-1B rs1143627 recessive model was significantly related to normal levels of SCC, the IL-4 rs2243250 mutant, dominant model, and recessive model were significantly related to lymph node metastasis, and the IL-4 rs2227284 dominant model was significantly related to lymph node metastasis. Furthermore, the IL-6 rs1800796 heterozygous genotype and the absence of distant metastases were significantly related, whereas the mutant and recessive model were significantly related to lymph node metastasis. The IL-6 rs1800796 mutant were related to no family history of cancer and the recessive model were significantly related to stage III-IV disease. The TNFRSF1A rs414570 dominant model and recessive model were significantly related to the absence of distant metastases. The TNF rs361525 wild-type genotype was significantly related to stage III-IV disease and the COX-2 rs20417 wild-type genotype was significantly related to lymph node metastasis. The other SNPs showed no significant correlations with clinicopathological parameters. The results of association of significant inflammation-associated gene SNPs with clinicopathological parameters of HNSCC patients are shown in Table 6, and all results are shown in Supplementary Table 2.

Table 6

CharacteristicsAKT1 rs1130233AKT1 rs2494732PIGR rs291097PIGR rs291102
WildHeterozygousP valueMutationP valuePdominancePrecessiveWildHeterozygousP valueMutationP valuePdominancePrecessiveWildHeterozygousP valueMutationP valuePdominancePrecessiveWildHeterozygousP valueMutationP valuePdominancePrecessive
Age0.6750.6610.6340.8060.6490.8620.7400.7240.117NA0.117NA0.7520.2380.6040.242
Age≤60407034136962125200111302
Age>60244824404344897078190
Gender0.1110.8520.2730.1950.6130.0930.2720.0410.584NA0.584NA0.8960.4280.9890.123
Female13371183320538048121
Male518147157986161190141371
T stage0.4100.6010.7060.2610.6720.4760.5560.5180.993NA0.993NA0.7060.2820.8200.274
1-22853201149428913077240
3-4263723739397511066181
N stage0.0340.3270.0550.8210.3930.9020.5890.2920.478NA0.478NA0.9730.4980.9560.497
Negative12351353423556047140
Positive455831145860116180102301
M stage0.0460.1040.0510.7370.3330.1450.1970.1710.342NA0.342NA0.6710.7840.7000.777
Negative578843199179167220146411
Positive110406912301140
Clinical stage0.0650.6250.1260.5100.8790.1700.4580.0310.439NA0.439NA0.7340.5560.7950.550
I-II11331173118515042130
III-IV487038146774136200121331
Smoking0.4860.2080.3110.2930.8490.9800.9090.8150.618NA0.618NA0.3170.1360.2150.149
No28583211565110612089272
Yes366026125655108150100220
Drinking0.3811.0000.5330.5590.5220.4340.4580.6510.497NA0.497NA0.1190.1660.0770.188
No32672914605511216096312
Yes3251299525110211093180
Family history of cancer0.7970.1910.7591.0000.2790.9180.5600.0610.486NA0.486NA0.7860.5080.7010.513
No529452208693178210155412
Yes122463261336603480
SCC0.9090.7310.8190.7370.4550.4660.4480.8610.073NA0.073NA0.1280.6430.1560.605
Normal253920839367410065171
Increased61061111016601480
CEA0.3790.1250.1550.1470.9780.1890.5390.0360.897NA0.897NA0.6620.6520.5850.668
Normal163418631305810049162
Increased330150510510
CYFRA0.9010.7820.8490.8030.2000.3930.2330.8240.082NA0.082NA0.041NA0.041NA
Normal49338514201420
Increased4104112511701080
EBV0.5390.4000.8770.1830.8140.8620.8291.0000.635NA0.635NA1.000NA1.000NA
Negative111274111527302550
Positive213123510510
Blood type0.3340.6120.3070.8440.2690.6540.4160.5490.183NA0.183NA0.5330.7070.6650.669
A10231041821358031120
B91976121734102951
AB75336614201040
O9191132314354029101

Association of significant inflammation-associated gene SNPs with clinicopathological parameters of HNSCC patients.

Among the SNPs related to the risk of HNSCC, the heterozygous and dominant model of AKT1 rs1130233 were significantly related to lymph node metastasis and non-distant metastasis (P=0.034, P=0.046). The recessive model of AKT1 rs2494732 was significantly related to male sex, stage III-IV disease, and normal carcinoembryonic antigen (CEA) levels (P=0.041, P=0.031, P=0.036). The IL-1RN rs419598 wild-type genotype was significantly related to stage III-IV disease, the PIGR rs291102 wild-type genotype and dominance model were significantly related to normal levels of cytokeratin fragment 19 (CYFRA) (P=0.041).

Discussion

In this study, we report for the first time an association of 28 polymorphisms with HNSCC risk and radiotherapy sensitivity in a population of individuals from the Liaoning Province of China. We found that carriers of the AKT1 rs1130233 TT genotype, dominance model (CT+TT vs. CC), recessive model (TT vs. CT+CC), and the AKT1 rs2494732 CC genotype had a reduced risk of HNSCC (P<0.05), whereas those with the PIGR rs291097 GA genotype, dominance model (GA+ AA vs. GG), and PIGR rs291102 dominance model (GA+ AA vs. GG) showed increased risk of HNSCC (P<0.05). In addition, we found that the IL-1RN rs419598, IL-21R rs2189521, and BCL2 rs2279115 genotypes were associated with reduced HNSCC risk, while the TNF rs1800630 genotype was associated with increased HNSCC risk. These findings provide experimental evidence to support these genes or SNPs as potential biomarkers of specific types of HNSCC.

It is estimated that infectious diseases and chronic inflammation account for approximately 25% of cancer-causing factors (). Inflammation may act at multiple stages of disease development to disrupt tissue homeostasis, induce aberrant proliferative responses, modulate the tumor microenvironment, and compromise immune surveillance (50–52). Inflammatory cells and related signaling molecules can also be used by tumors to facilitate progression and metastasis by generating a favorable microenvironment, as well as promoting genetic instability and angiogenesis (53). Inflammatory physiological changes, such as oxidative stress, exert downstream genotoxic effects (54). When sustained over extended periods, these changes promote the emergence of cancer-initiating mutations (55). Genetic variations in inflammation-related genes potentially complement prediction of HNSCC risk. Gene polymorphisms are a common genetic variant. The most common polymorphic form is a base difference, termed a single nucleotide polymorphism ().

AKT, the v-AKT murine thymoma viral oncogene homolog, maps to human chromosome 14q32.32 and encodes a 56-kDa protein, comprising 480 amino acids (56). AKT is an important effector of the PI3K/AKT/MTOR signaling pathway, and genetic mutations or abnormal protein expression can alter a variety of cellular processes including migration, proliferation, growth, and survival (57). AKT SNPs are reported to be associated with susceptibility to various cancer types, such as nasopharyngeal carcinoma (NPC), OSCC, non-small cell lung cancer, pancreatic ductal adenocarcinoma, and GC via effects on protein expression and transcriptional activity (, 36, 56, 58–60). Zhang et al. reported that the AKT1 rs1130233 and rs2494732 AA genotypes were associated with a significantly increased susceptibility to NPC risk in a Chinese population (36). Another study also reported an association between the AKT1 polymorphism and cancer metastasis (58). Collectively, these observations indicate that our findings of associations existing between AKT1 SNPs and the risk of HNSCC are biologically relevant.

PIGR is a member of the immunoglobulin superfamily and transports immunoglobulin A (IgA) onto mucosal surfaces (61). PIGR has been described as a putative cancer biomarker in a few studies on various cancers, the majority of which indicate an association between low PIGR expression and more aggressive disease (61). Individuals carrying the PIGR rs291097 T allele have a higher risk of NPC in Guangdong Province, China (). The PIGR rs291102 genotype is a missense mutation changing alanine to valine near an endoproteolytic cleavage site. This variant could alter the efficiency of PIGR to release the IgA-EBV complex and consequently increase the susceptibility of populations in endemic areas to develop NPC (). Chen et al. reported that the risk of HNSCC may be associated with SNPs in the BCL2 promoter region (43). Some scholars consider that TNF-α SNPs (rs1800629, rs1799724, rs1800630, and rs1799964) may individually or, more likely, jointly affect individual susceptibility to HPV16-associated OSCC, particularly squamous cell carcinoma of the oropharynx (SCCOP) in never smokers (38). Our results are similar to the abovementioned findings, which suggests that inflammatory-related gene SNPs are closely related to the risk of HNSCC in different populations and different cases.

Following stratified analyses, we found that the IL-1RN rs419598 TT genotype and dominance model (CT+ CC vs. TT) were associated with reduced HNSCC risk in individuals older than 60 years of age. However, in those age 60 and younger, the AKT1 rs1130233 TT genotype and dominance model (CT+TT vs. CC), the IL-21R rs2189521 CT genotype and dominance model (CT+ CC vs. TT), and the BCL2 rs2279115 recessive model (TT vs. GT+GG) were associated with reduced HNSCC risk. In addition, in men, the AKT1 rs1130233 TT genotype and dominance model (CT+TT vs. CC) and the BCL2 rs2279115 TT genotype and recessive model (TT vs. GT+GG) were associated with reduced HNSCC risk. In women, however, the IL-21R rs2189521 CT genotype and dominance model (CT+ CC vs. TT) were associated with reduced HNSCC risk. Additionally, the PIGR rs291097 GA genotype and dominance model (GA+AA vs. GG) and the TNF rs1800630 AA genotype were associated with increased HNSCC risk in women. These genes are all inflammatory-related genes, and these results suggest that inflammatory-related gene SNPs are closely related to the risk of HNSCC patients.

From our research data, the correlation between various genotypes and the risk of HNSCC may be related to the differences in the distribution of different clinicopathological parameters. We also compared the genotype distribution of these polymorphisms in HNSCC patients with different clinicopathological parameters. We found that the heterozygous and dominant models of the AKT1 rs1130233 polymorphism were significantly related to non-distant metastasis. This phenomenon may indicate that the carrier of AKT1 rs1130233 dominance model has a low risk of cancer and is not prone to distant metastasis, which may indicate they have a long survival time. The IL-1RN rs419598 wild-type genotype was significantly related to stage III-IV disease, the PIGR rs291102 wild-type genotype was significantly related to normal levels of CYFRA, and the BCL2 rs2279115 wild-type genotype was significantly related to lymph node metastasis. These results suggest that individuals with the IL-1RN rs419598, or BCL2 rs2279115 polymorphisms showed a significant reduction in HNSCC risk progression, whereas those with the PIGR rs291102 dominance model had increased HNSCC risk. In addition, we found that different genotypes of some SNPs are significantly correlated with different clinicopathological parameters, such as IL-1B rs1143627, IL-4 rs2243250, and IL-4 rs2227284, IL-6 rs1800796, TNFRSF1A rs414570, TNF rs361525, COX-2 rs20417, whereas other SNPs showed no significant correlations with clinicopathological parameters in our data.

Recently, studies on the relationships between genetic polymorphisms and radiotherapy sensitivity have been reported. For example, gene polymorphisms of Wnt/beta-catenin may be novel prognostic factors for NPC patients treated with RT (62). The authors observed that the catenin beta 1 gene (CTNNB1) rs1880481 and rs3864004 polymorphisms, as well as the glycogen synthase kinase 3 beta gene (GSK3beta) rs3755557 polymorphism, were significantly associated with a poorer efficacy of RT in NPC patients (63). However, the relationship between SNPs in inflammation-related genes and the risk of HNSCC has not been reported. In this study, we found that HNSCC patients carrying the IL-4RA rs1801275 AA wild-type genotype were more sensitive to radiotherapy compared with other patients. We also analyzed the relationships between clinicopathological parameters and radiotherapy sensitivity. Age ≤ 60 years, non-smoker status, and normal levels of SCC were found to be associated with increased radiotherapy sensitivity of HNSCC patients. We expect that these results may help guide radiotherapy and concurrent radiotherapy and chemotherapy treatment plans. However, this was only a correlation study, and the support of basic science experiments is necessary.

In our study, the 28 inflammation-related gene polymorphisms we screened were previously reported in various cancers, and several SNPs have been reported in HNSCC (, , , 34–36, 39, 42, 64, 65). Drobin et al reported the correlation and possible mechanism of VEGFA rs69947 with breast cancer and HNSCC radiotherapy sensitivity. The authors proposed that this SNP may affect protein expression, which would impact biological processes such as blood vessel growth, inflammatory cell infiltration, the immune response, DNA repair, oxidative stress and hypoxia (66). These changes may underlie the differences in correlation and sensitivity among patients. TNF-α is a cytokine that is secreted during the inflammatory process accompanying RTH and during cancer development. An SNP in the TNF-α promoter region can potentially affect the function or expression of this cytokine and thus modulate the risk of occurrence and intensity of OM and shortening of overall survival (). To explore these possibilities, further studies are required using a larger sample size and additional in vitro and in vivo experimental analyses.

The present study has some limitations. First, the sample size was relatively small, especially for the HNSCC case group. Our results need further confirmation in larger populations. Second, only HNSCC risk was analyzed in this study. Analysis of prognostic parameters, such as overall survival and progression-free survival, is also warranted. Last, functional experiments are required to elucidate the underlying disease mechanism responsible for our observations.

In summary, we found that the AKT1 rs1130233 TT and dominance model (CT+TT vs. CC) genotypes, as well as the rs2494732 CC genotype, were associated with reduced risk of HNSCC. The PIGR rs291097 GA and dominance model (GA+AA vs. GG) genotypes, as well as the rs291102 dominance model (GA+AA vs. GG), were associated with increased risk of HNSCC. We also found that the IL-4RA rs1801275 AA genotype was significantly correlated with increased radiotherapy sensitivity of HNSCC patients. In addition, age ≤ 60 years, non-smoker status, and normal levels of SCC were found to be associated with increased radiotherapy sensitivity of HNSCC patients. We expect that future data from a larger population sample will support our results and be used to guide the comprehensive treatment and prognosis of HNSCC patients. Further investigation is needed to elucidate the molecular mechanisms governing our findings.

Funding

This work was supported by grants from the Doctoral Science and Technology Research Startup Fund Project of Liaoning Province of China (2019-BS-275), the Science and Technology Fund Project of Liaoning Province of China (20180550318), and Key Laborotary of Tumor Radiosensitization and Normal Tissue Radioprotection of Liaoning Province (2018225102).

Statements

Data availability statement

The data that support the findings of our study have been deposited into CNGB Sequence Archive (CNSA) of China National GeneBank DataBase (CNGBdb) with accession number CNP0001819.

Ethics statement

The studies involving human participants were reviewed and approved by the Human Ethics Committee of Liaoning Cancer Hospital. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author contributions

YL and XL designed the study. HY was responsible for case screening. XK, LC, YS, and AM treated HNSCC patients. YZ was mainly for clinical information collection. YL and LZ processed, analysed data, and wrote the paper. All authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2021.651632/full#supplementary-material

References

  • 1

    Fernandez-MateosJSeijas-TamayoRKlainJCABorgononMPPerez-RuizEMesiaRet al. Analysis of Autophagy Gene Polymorphisms in Spanish Patients With Head and Neck Squamous Cell Carcinoma. Sci Rep (2017) 7:6887. doi: 10.1038/s41598-017-07270-0

  • 2

    HashibeMBrennanPBenhamouSCastellsagueXChenCCuradoMPet al. Alcohol Drinking in Never Users of Tobacco, Cigarette Smoking in Never Drinkers, and the Risk of Head and Neck Cancer: Pooled Analysis in the International Head and Neck Cancer Epidemiology Consortium. J Natl Cancer Inst (2007) 99:777–89. doi: 10.1038/bdj.2007.638

  • 3

    PiaoYLiYXuQLiuJWXingCZXieXDet al. Association of MTOR and AKT Gene Polymorphisms With Susceptibility and Survival of Gastric Cancer. PloS One (2015) 10:e0136447. doi: 10.1371/journal.pone.0136447

  • 4

    LeiboviciDGrossmanHBDinneyCPMillikanRELernerSWangYet al. Polymorphisms in Inflammation Genes and Bladder Cancer: From Initiation to Recurrence, Progression, and Survival. J Clin Oncol (2005) 23:5746–56. doi: 10.1200/JCO.2005.01.598

  • 5

    LuHOuyangWHuangC. Inflammation, a Key Event in Cancer Development. Mol Cancer Res (2006) 4:221–33. doi: 10.1158/1541-7786.MCR-05-0261

  • 6

    OhSSChangSCCaiLCordon-CardoCDingBGGreenlandSet al. Single Nucleotide Polymorphisms of 8 Inflammation-Related Genes and Their Associations With Smoking-Related Cancers. Int J Cancer (2010) 127:2169–82. doi: 10.1002/ijc.25214

  • 7

    St JohnMADohadwalaMLuoJWangGLeeGShihHet al. Proinflammatory Mediators Upregulate Snail in Head and Neck Squamous Cell Carcinoma. Clin Cancer Res (2009) 15:6018–27. doi: 10.1158/1078-0432.CCR-09-0011

  • 8

    ZhuYXuYWeiYLiangWLiaoMZhangL. Association of IL-1B Gene Polymorphisms With Nasopharyngeal Carcinoma in a Chinese Population. Clin Oncol (R Coll Radiol) (2008) 20:207–11. doi: 10.1016/j.clon.2008.01.003

  • 9

    KietthubthewSWickliffeJSriplungHIshidaTChonmaitreeTAuWW. Association of Polymorphisms in Proinflammatory Cytokine Genes With the Development of Oral Cancer in Southern Thailand. Int J Hyg Environ Health (2010) 213:146–52. doi: 10.1016/j.ijheh.2010.01.002

  • 10

    TamandaniDMSobtiRCShekariMKaurSHuriaA. Impact of Polymorphism in IL-1RA Gene on the Risk of Cervical Cancer. Arch Gynecol Obstet (2008) 277:527–33. doi: 10.1007/s00404-007-0504-4

  • 11

    DeansCRose-ZerilliMWigmoreSRossJHowellMJacksonAet al. Host Cytokine Genotype is Related to Adverse Prognosis and Systemic Inflammation in Gastro-Oesophageal Cancer. Ann Surg Oncol (2007) 14:329–39. doi: 10.1245/s10434-006-9122-9

  • 12

    WangYLinLXuHLiTZhouYDanHet al. Genetic Variants in AKT1 Gene Were Associated With Risk and Survival of OSCC in Chinese Han Population. J Oral Pathol Med (2015) 44:45–50. doi: 10.1111/jop.12211

  • 13

    HirunsatitRKongruttanachokNShotelersukKSupiyaphunPVoravudNSakuntabhaiAet al. Polymeric Immunoglobulin Receptor Polymorphisms and Risk of Nasopharyngeal Cancer. BMC Genet (2003) 4:3. doi: 10.1186/1471-2156-4-3

  • 14

    FanQJiaWHZhangRHYuXJChenLZFengQSet al. Correlation of Polymeric Immunoglobulin Receptor Gene Polymorphisms to Susceptibility of Nasopharyngeal Carcinoma. Ai Zheng (2005) 24:915–8.

  • 15

    FuJLiZLiN. The Association Between COX-2 Gene Rs5275 Polymorphism and Nasopharyngeal Carcinoma Risk. Pathol Res Pract (2018) 214:1579–82. doi: 10.1016/j.prp.2018.07.028

  • 16

    MurataM. Inflammation and Cancer. Environ Health Prev Med (2018) 23:50. doi: 10.1186/s12199-018-0740-1

  • 17

    HuangSHO’SullivanB. Overview of the 8th Edition TNM Classification for Head and Neck Cancer. Curr Treat Options Oncol (2017) 18:40. doi: 10.1007/s11864-017-0484-y

  • 18

    PowrozekTMlakRBrzozowskaAMazurekMGolebiowskiPMalecka-MassalskaT. Relationship Between TNF-Alpha -1031T/C Gene Polymorphism, Plasma Level of TNF-Alpha, and Risk of Cachexia in Head and Neck Cancer Patients. J Cancer Res Clin Oncol (2018) 144:1423–34. doi: 10.1007/s00432-018-2679-4

  • 19

    CuiXBWangDDZhangHYLiTTJinTTPengHet al. Tumor Necrosis Factor-Alpha Gene 308G/a Polymorphism is Not Associated With Esophageal Squamous Cell Carcinoma Risk in Kazakh Patients. Int J Clin Exp Pathol (2015) 8:9293–9.

  • 20

    SinghPKBograJChandraGAhmadMKGuptaRKumarVet al. Association of TNF-Alpha (-238 and -308) Promoter Polymorphisms With Susceptibility of Oral Squamous Cell Carcinoma in North Indian Population. Cancer Biomark (2015) 15:125–31. doi: 10.3233/CBM-140444

  • 21

    HsuHJYangYHShiehTYChenCHKaoYHYangCFet al. Role of Cytokine Gene (Interferon-Gamma, Transforming Growth Factor-Beta1, Tumor Necrosis Factor-Alpha, Interleukin-6, and Interleukin-10) Polymorphisms in the Risk of Oral Precancerous Lesions in Taiwanese. Kaohsiung J Med Sci (2014) 30:551–8. doi: 10.1016/j.kjms.2014.09.003

  • 22

    ZhangCSturgisEMZhengHZafereoMEWeiQLiG. TNF-Alpha Promoter Polymorphisms and Risk of Recurrence in Patients With Squamous Cell Carcinomas of the Nonoropharynx. Int J Cancer (2014) 135:1615–24. doi: 10.1002/ijc.28793

  • 23

    MendesSOdos SantosMPeterleGTMaia LdeLSturEAgostiniLPet al. HIF-1alpha Expression Profile in Intratumoral and Peritumoral Inflammatory Cells as a Prognostic Marker for Squamous Cell Carcinoma of the Oral Cavity. PloS One (2014) 9:e84923. doi: 10.1371/journal.pone.0084923

  • 24

    Al-toubMAlmusaAAlmajedMAl-NbaheenMKassemMAldahmashAet al. Pleiotropic Effects of Cancer Cells’ Secreted Factors on Human Stromal (Mesenchymal) Stem Cells. Stem Cell Res Ther (2013) 4:114. doi: 10.1186/scrt325

  • 25

    de JesusGPRibeiroFAde MouraCFGolluckeAPOshimaCTRibeiroDA. Anti-Tumor Activity of Grape Juice Concentrate in the Rat Tongue Two-Stage Initiation-Promotion Protocol Induced by 4-Nitroquinoline 1-Oxide. Toxicol Mech Methods (2014) 24:276–83. doi: 10.3109/15376516.2014.881944

  • 26

    ErdoganMKaradenizMOzbekMOzgenAGBerdeliA. Interleukin-10 Gene Polymorphism in Patients With Papillary Thyroid Cancer in Turkish Population. J Endocrinol Invest (2008) 31:750–4. doi: 10.1007/BF03349252

  • 27

    ChiangSLChenPHLeeCHKoAMLeeKWLinYCet al. Up-Regulation of Inflammatory Signalings by Areca Nut Extract and Role of Cyclooxygenase-2 -1195G>a Polymorphism Reveal Risk of Oral Cancer. Cancer Res (2008) 68:8489–98. doi: 10.1158/0008-5472.CAN-08-0823

  • 28

    de LuisDASagradoMGVallejoLACarcedoLMIzaolaOCuellarLet al. Influence of G308A Polymorphism of Tumor Necrosis Factor-Alpha Gene on Inflammatory Markers in Postsurgical Head and Neck Cancer Patients With Early Enteral Nutrition. Nutrition (2007) 23:529–32. doi: 10.1016/j.nut.2007.04.011

  • 29

    PuXWangLChangJYHildebrandtMAYeYLuCet al. Inflammation-Related Genetic Variants Predict Toxicity Following Definitive Radiotherapy for Lung Cancer. Clin Pharmacol Ther (2014) 96:609–15. doi: 10.1038/clpt.2014.154

  • 30

    MlakRPowrozekTBrzozowskaAHoma-MlakIMazurekMGolebiowskiPet al. The Relationship Between TNF-Alpha Gene Promoter Polymorphism (- 1211 T > C), the Plasma Concentration of TNF-Alpha, and Risk of Oral Mucositis and Shortening of Overall Survival in Patients Subjected to Intensity-Modulated Radiation Therapy Due to Head and Neck Cancer. Support Care Cancer (2019) 28(2):531–40. doi: 10.1007/s00520-019-04838-6

  • 31

    WuMYHuangSJYangFQinXTLiuDDingYet al. Detection of Nasopharyngeal Carcinoma Susceptibility With Single Nucleotide Polymorphism Analysis Using Next-Generation Sequencing Technology. Oncotarget (2017) 8:52708–23. doi: 10.18632/oncotarget.17085

  • 32

    BrzozowskaAPowrozekTHoma-MlakIMlakRCiesielkaMGolebiowskiPet al. Polymorphism of Promoter Region of TNFRSF1A Gene (-610 T > G) as a Novel Predictive Factor for Radiotherapy Induced Oral Mucositis in HNC Patients. Pathol Oncol Res (2018) 24:135–43. doi: 10.1007/s12253-017-0227-1

  • 33

    BoaventuraPDuraesCMendesACostaNRChoraIFerreiraSet al. IL6-174 G>C Polymorphism (Rs1800795) Association With Late Effects of Low Dose Radiation Exposure in the Portuguese Tinea Capitis Cohort. PloS One (2016) 11:e0163474. doi: 10.1371/journal.pone.0163474

  • 34

    SousaHBastosMJRibeiroJOliveiraSBredaECatarinoRet al. 5’UTR +24T>C CR2 is Not Associated With Nasopharyngeal Carcinoma Development in the North Region of Portugal. Oral Dis (2016) 22:280–4. doi: 10.1111/odi.12436

  • 35

    SousaHMesquitaLRibeiroJCatarinoRBredaEMedeirosR. Polymorphisms in Host Immune Response Associated Genes and Risk of Nasopharyngeal Carcinoma Development in Portugal. Immunobiology (2016) 221:145–52. doi: 10.1016/j.imbio.2015.09.015

  • 36

    ZhangXChenXZhaiYCuiYCaoPZhangHet al. Combined Effects of Genetic Variants of the PTEN, AKT1, MDM2 and P53 Genes on the Risk of Nasopharyngeal Carcinoma. PloS One (2014) 9:e92135. doi: 10.1371/journal.pone.0092135

  • 37

    PuXHildebrandtMALuCRothJAStewartDJZhaoYet al. Inflammation-Related Genetic Variations and Survival in Patients With Advanced non-Small Cell Lung Cancer Receiving First-Line Chemotherapy. Clin Pharmacol Ther (2014) 96:360–9. doi: 10.1038/clpt.2014.89

  • 38

    JinLSturgisEMZhangYHuangZSongXLiCet al. Association of Tumor Necrosis Factor-Alpha Promoter Variants With Risk of HPV-Associated Oral Squamous Cell Carcinoma. Mol Cancer (2013) 12:80. doi: 10.1186/1476-4598-12-80

  • 39

    FanQHeJFWangQRCaiHBSunXGZhouXXet al. Functional Polymorphism in the 5’-UTR of CR2 is Associated With Susceptibility to Nasopharyngeal Carcinoma. Oncol Rep (2013) 30:11–6. doi: 10.3892/or.2013.2421

  • 40

    GaurPMittalMMohantiBDasS. Functional Variants of IL4 and IL6 Genes and Risk of Tobacco-Related Oral Carcinoma in High-Risk Asian Indians. Oral Dis (2011) 17:720–6. doi: 10.1111/j.1601-0825.2011.01831.x

  • 41

    MittalMKapoorVMohantiBKDasSN. Functional Variants of COX-2 and Risk of Tobacco-Related Oral Squamous Cell Carcinoma in High-Risk Asian Indians. Oral Oncol (2010) 46:622–6. doi: 10.1016/j.oraloncology.2010.06.002

  • 42

    FarhatKHassenEGabboujSBouaouinaNChouchaneL. Interleukin-10 and Interferon-Gamma Gene Polymorphisms in Patients With Nasopharyngeal Carcinoma. Int J Immunogenet (2008) 35:197–205. doi: 10.1111/j.1744-313X.2008.00752.x

  • 43

    ChenKHuZWangLESturgisEMEl-NaggarAKZhangWet al. Single-Nucleotide Polymorphisms At the TP53-Binding or Responsive Promoter Regions of BAX and BCL2 Genes and Risk of Squamous Cell Carcinoma of the Head and Neck. Carcinogenesis (2007) 28:2008–12. doi: 10.1093/carcin/bgm172

  • 44

    GuoWWangNWangYMLiYWenDGChenZFet al. Interleukin-10 -1082 Promoter Polymorphism is Not Associated With Susceptibility to Esophageal Squamous Cell Carcinoma and Gastric Cardiac Adenocarcinoma in a Population of High-Incidence Region of North China. World J Gastroenterol (2005) 11:858–62. doi: 10.3748/wjg.v11.i6.858

  • 45

    XuQYuanYSunLPGongYHXuYYuXWet al. Risk of Gastric Cancer is Associated With the MUC1 568 a/G Polymorphism. Int J Oncol (2009) 35:1313–20. doi: 10.3892/ijo_00000449

  • 46

    BonaparteEPesentiCFontanaLFalconeRPaganiniLMarzoratiAet al. Molecular Profiling of Lung Cancer Specimens and Liquid Biopsies Using MALDI-TOF Mass Spectrometry. Diagn Pathol (2018) 13:4. doi: 10.1186/s13000-017-0683-7

  • 47

    YangXDZhaoSFZhangQLiWWangYXHongXWet al. Gelsolin Rs1078305 and Rs10818524 Polymorphisms Were Associated With Risk of Oral Squamous Cell Carcinoma in a Chinese Han Population. Biomarkers (2016) 21:267–71. doi: 10.3109/1354750X.2015.1134664

  • 48

    SinghalNKumarMKanaujiaPKVirdiJS. MALDI-TOF Mass Spectrometry: an Emerging Technology for Microbial Identification and Diagnosis. Front Microbiol (2015) 6:791. doi: 10.3389/fmicb.2015.00791

  • 49

    EisenhauerEATherassePBogaertsJSchwartzLHSargentDFordRet al. New Response Evaluation Criteria in Solid Tumours: Revised RECIST Guideline (Version 1.1). Eur J Cancer (2009) 45:228–47. doi: 10.1016/j.ejca.2008.10.026

  • 50

    HanahanDWeinbergRA. Hallmarks of Cancer: the Next Generation. Cell (2011) 144:646–74. doi: 10.1016/j.cell.2011.02.013

  • 51

    ElinavENowarskiRThaissCAHuBJinCFlavellRA. Inflammation-Induced Cancer: Crosstalk Between Tumours, Immune Cells and Microorganisms. Nat Rev Cancer (2013) 13:759–71. doi: 10.1038/nrc3611

  • 52

    JinushiM. Yin and Yang of Tumor Inflammation: How Innate Immune Suppressors Shape the Tumor Microenvironments. Int J Cancer (2014) 135:1277–85. doi: 10.1002/ijc.28626

  • 53

    CoussensLMWerbZ. Inflammation and Cancer. Nature (2002) 420:860–7. doi: 10.1038/nature01322

  • 54

    AtsumiTSinghRSabharwalLBandoHMengJArimaYet al. Inflammation Amplifier, a New Paradigm in Cancer Biology. Cancer Res (2014) 74:8–14. doi: 10.1158/0008-5472.CAN-13-2322

  • 55

    BuasMFHeQJohnsonLGOnstadLLevineDMThriftAPet al. Germline Variation in Inflammation-Related Pathways and Risk of Barrett’s Oesophagus and Oesophageal Adenocarcinoma. Gut (2017) 66:1739–47. doi: 10.1136/gutjnl-2016-311622

  • 56

    KimMJKangHGLeeSYJeonHSLeeWKParkJYet al. AKT1 Polymorphisms and Survival of Early Stage non-Small Cell Lung Cancer. J Surg Oncol (2012) 105:167–74. doi: 10.1002/jso.22071

  • 57

    De MarcoCRinaldoNBruniPMalzoniCZulloFFabianiFet al. Multiple Genetic Alterations Within the PI3K Pathway are Responsible for AKT Activation in Patients With Ovarian Carcinoma. PloS One (2013) 8:e55362. doi: 10.1371/journal.pone.0055362

  • 58

    LiQYangJYuQWuHLiuBXiongHet al. Associations Between Single-Nucleotide Polymorphisms in the PI3K-PTEN-AKT-Mtor Pathway and Increased Risk of Brain Metastasis in Patients With non-Small Cell Lung Cancer. Clin Cancer Res (2013) 19:6252–60. doi: 10.1158/1078-0432.CCR-13-1093

  • 59

    AvanAAvanALe LargeTYMambriniAFunelNMaftouhMet al. AKT1 and SELP Polymorphisms Predict the Risk of Developing Cachexia in Pancreatic Cancer Patients. PloS One (2014) 9:e108057. doi: 10.1371/journal.pone.0108057

  • 60

    WangXLinYLanFYuYOuyangXWangXet al. A GG Allele of 3’-Side AKT1 SNP is Associated With Decreased AKT1 Activation and Better Prognosis of Gastric Cancer. J Cancer Res Clin Oncol (2014) 140:1399–411. doi: 10.1007/s00432-014-1663-x

  • 61

    FristedtRGaberAHednerCNodinBUhlenMEberhardJet al. Expression and Prognostic Significance of the Polymeric Immunoglobulin Receptor in Esophageal and Gastric Adenocarcinoma. J Transl Med (2014) 12:83. doi: 10.1186/1479-5876-12-83

  • 62

    BanSKonomiCIwakawaMYamadaSOhnoTTsujiHet al. Radiosensitivity of Peripheral Blood Lymphocytes Obtained From Patients With Cancers of the Breast, Head and Neck or Cervix as Determined With a Micronucleus Assay. J Radiat Res (2004) 45:535–41. doi: 10.1269/jrr.45.535

  • 63

    YuJHuangYLiuLWangJYinJHuangLet al. Genetic Polymorphisms of Wnt/Beta-Catenin Pathway Genes are Associated With the Efficacy and Toxicities of Radiotherapy in Patients With Nasopharyngeal Carcinoma. Oncotarget (2016) 7:82528–37. doi: 10.18632/oncotarget.12754

  • 64

    CaetanoMSZhangHCumpianAMGongLUnverNOstrinEJet al. IL6 Blockade Reprograms the Lung Tumor Microenvironment to Limit the Development and Progression of K-Ras-Mutant Lung Cancer. Cancer Res (2016) 76:3189–99. doi: 10.1158/0008-5472.CAN-15-2840

  • 65

    PasvenskaiteAVilkeviciuteALiutkevicieneRGedvilaiteGLiutkeviciusVUlozaV. Associations of IL6 Rs1800795, BLK Rs13277113, TIMP3 Rs9621532, IL1RL1 Rs1041973 and IL1RAP Rs4624606 Single Gene Polymorphisms With Laryngeal Squamous Cell Carcinoma. Gene (2020) 747:144700. doi: 10.1016/j.gene.2020.144700

  • 66

    DrobinKMarczykMHalleMDanielssonDPapiezASangsuwanTet al. Molecular Profiling for Predictors of Radiosensitivity in Patients With Breast or Head-and-Neck Cancer. Cancers (Basel) (2020) 12(3):753–71. doi: 10.3390/cancers12030753

Summary

Keywords

inflammation-related gene, SNP, HNSCC, risk, radiotherapy sensitivity

Citation

Li Y, Zhu L, Yao H, Zhang Y, Kong X, Chen L, Song Y, Mu A and Li X (2021) Association of Inflammation-Related Gene Polymorphisms With Susceptibility and Radiotherapy Sensitivity in Head and Neck Squamous Cell Carcinoma Patients in Northeast China. Front. Oncol. 11:651632. doi: 10.3389/fonc.2021.651632

Received

10 January 2021

Accepted

07 May 2021

Published

04 June 2021

Volume

11 - 2021

Edited by

Jun-Lin Yi, Chinese Academy of Medical Sciences and Peking Union Medical College, China

Reviewed by

Xin Jiang, The First Hospital of Jilin University, China; Jinsheng Hong, First Affiliated Hospital of Fujian Medical University, China

Updates

Copyright

© 2021 Li, Zhu, Yao, Zhang, Kong, Chen, Song, Mu and Li.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ying Li, ; Xia Li,

†These authors have contributed equally to this work

This article was submitted to Radiation Oncology, a section of the journal Frontiers in Oncology

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Outline

Cite article

Copy to clipboard


Export citation file


Share article

Share on WeChat

Scan with WeChat to share this article

Article metrics