Tumor necrosis factor-α induced protein 8 polymorphism and risk of non-Hodgkin's lymphoma in a Chinese population: a case-control study.
ABSTRACT Non-Hodgkin's lymphoma (NHL) has been reported to be associated with autoimmune and pro-inflammatory response, and genetic polymorphisms of candidate genes involved in autoimmune and pro-inflammatory response may influence the susceptibility to NHL. To evaluate the role of such genetic variations in risk of NHL, we conducted a case-control study of 514 NHL patients and 557 cancer-free controls in a Chinese population.
We used the Taqman assay to genotype six potentially functional single nucleotide polymorphisms (SNPs) in six previously reported inflammation and immune-related genes (TNF rs1799964T>C, LTA rs1800683G>A, IL-10 rs1800872T>G, LEP rs2167270G>A, LEPR rs1327118C>G, TNFAIP8 rs1045241C>T). Logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (95% CI).
We observed a significantly increased risk of NHL associated with the TNFAIP8 rs1045241C>T polymorphism (adjusted OR = 3.03; 95% CI = 1.68-5.45 for TT vs. CC and adjusted OR = 2.03; 95% CI = 1.53-2.69 for CT/TT vs. CC). The risk associated with the T allele was more evident in subgroups of 40-60 year-old, non-smokers or light-smokers (less than 25 pack-years), and subjects with normal weight or overweight. Risk for both B and T cell non-Hodgkin's lymphoma was elevated for CT/TT genotypes (adjusted OR = 1.95, 95% CI = 1.41-2.70 for B cell NHL and adjusted OR = 2.22, 95% CI = 1.49-3.30 for T cell NHL), particularly for DLBCL (adjusted OR = 2.01, 95%CI = 1.41-2.85) and FL (adjusted OR = 2.53, 95% CI = 1.17-5.45). These risks were not observed for variant genotypes of other five SNPs compared with their common homozygous genotypes.
The polymorphism of TNFAIP8 rs1045241C>T may contribute to NHL susceptibility in a Chinese population. Further large-scale and well-designed studies are needed to confirm these results.
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ABSTRACT: Tumor necrosis factor alpha-induced protein 8 (TNFAIP8) has been recently documented in various malignancies, but its role in non-small cell lung cancer (NSCLC) remains uncertain. In the current study, we investigated the level of TNFAIP8 in NSCLC tissues, adjacent noncancerous lung tissues, healthy lung tissues, CD4+ T cells, and CD8+ T cells by real-time reverse transcription PCR (RT-PCR) and Western blot analysis. Results revealed that the mRNA level of TNFAIP8 was significantly increased in cancer tissue than in healthy lung tissue from donors (p < 0.001). Interestingly, adjacent noncancerous lung tissues also showed higher mRNA level of TNFAIP8 than healthy lung tissue from donors (p < 0.01). Similarly, protein level of TNFAIP8 was elevated in NSCLC tissues and adjacent noncancerous lung tissues. We further analyzed TNFAIP8 expression in CD4+ T cells and CD8+ T cells. Data demonstrated that both mRNA level and protein level were significantly decreased in tumor-infiltrating CD4+ and CD8+ T cells than in peripheral CD4+ and CD8+ T cells. Moreover, patients with advanced stages presented lower protein expression of TNFAIP8 in tumor-infiltrating CD8+ T cells than patients with primary stages (p < 0.05). These results provide evidence that TNFAIP8 plays critical roles in NSCLC and may be used as a therapeutic target for the disease.Tumor Biology 10/2013; · 2.52 Impact Factor
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ABSTRACT: The results from the published studies on the association between leptin (LEP) genetic polymorphism and cancer risk are conflicting. The common A19G (rs2167270) genetic polymorphism has been reported to be functional and may contribute to genetic susceptibility to cancers. However, the association between LEP A19G (rs2167270) genetic polymorphism and cancer risk remains inconclusive. To better understand the role of LEP A19G (rs2167270) genetic polymorphism in global cancer, we conducted this comprehensive meta-analysis encompassing 5,679 cases and 7,443 controls. Overall, the LEP A19G (rs2167270) genetic polymorphism was associated with lower cancer risk. In the stratified analysis, significant associations were found between the LEP A19G (rs2167270) genetic polymorphism and colorectal cancer and non-Hodgkin's lymphoma. For colorectal cancer, there was no significant association of LEP A19G (rs2167270) variant with this disease under heterozygous codominant model [odds ratio (OR) = 1.11 (0.97-1.27)], dominant genetic model [OR = 1.03 (0.91-1.17)], and additive genetic model [OR = 0.94 (0.86-1.03)]; however, there was a marginal association under homozygous codominant model [OR = 0.80 (0.66-0.97)] and recessive genetic model [OR = 0.75 (0.63-0.90)]. For non-Hodgkin's lymphoma, there was a significant association of LEP A19G (rs2167270) variant with the disease under homozygous codominant model [OR = 0.74 (0.59-0.94)], recessive genetic model [OR = 0.76 (0.61-0.94)], and additive genetic model [OR = 0.89 (0.80-0.99)], but not under heterozygous codominant model [OR = 0.95 (0.82-1.10)] and dominant genetic model [OR = 0.91 (0.79-1.04)]. Moreover, a significantly decreased cancer risk was found in recessive genetic model among Latin American population. When stratified by study design, significantly elevated susceptibility to cancer was not found among any studies. No significantly differences in genotype method and sample size in cases were found among genotypes. These findings suggest that the LEP A19G (rs2167270) genetic polymorphism may decrease the susceptibility to cancers in colorectal cancer and non-Hodgkin's lymphoma, when assuming a homozygote codominant model and a recessive genetic model among Latin American population. The phenomenon also indicates that the SNP functions as a recessive mutation, which needs to be verified or linked with functional studies.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine. 05/2014;
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ABSTRACT: Here we correlated tumor necrosis factor-α-induced protein 8 (TNFAIP8) mRNA expression with clinicopathological parameters, and investigated the involvement of TNFAIP8 over-expression in platinum resistance of epithelial ovarian cancer (EOC). The status of TNFAIP8 protein was evaluated by Western blot analysis (n = 25) and immunohistochemistry (n = 134). TNFAIP8 mRNA expression was assessed with real-time polymerase chain reaction in fresh frozen EOC tissues (n = 40). TNFAIP8 over-expression at both mRNA and protein levels in platinum-resistant disease was clearly higher than that in platinum-sensitive disease (P < .05). Platinum resistance was independently correlated with residual tumor size (P = .025), ascites (P = .027) and TNFAIP8 over-expression (P = .003). In particular, TNFAIP8 over-expression was correlated with platinum resistance in EOCs with optimal cytoreduction (P = .001). TNFAIP8 mRNA expression was strongly associated with residual tumor size (P = .019). In conclusion, our findings indicate that TNFAIP8 over-expression is an independent predictor of platinum resistance and may be a potential biomarker for targeted therapy.Human pathology 01/2014; · 3.03 Impact Factor
Tumor Necrosis Factor-a Induced Protein 8
Polymorphism and Risk of Non-Hodgkin’s Lymphoma in
a Chinese Population: A Case-Control Study
Yan Zhang1,2, Meng-Yun Wang2,3, Jing He2,3, Jiu-Cun Wang4,5, Ya-Jun Yang4,5, Li Jin4,5, Zhi-Yu Chen1,2,
Xue-Jun Ma2,6, Meng-Hong Sun2,7, Kai-Qin Xia2,3,7, Xiao-Nan Hong1,2*, Qing-Yi Wei3,8, Xiao-
1Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, China, 2Department of Oncology, Shanghai Medical College, Fudan University,
Shanghai, China, 3Cancer Research Laboratory, Fudan University Shanghai Cancer Center, Shanghai, China, 4Ministry of Education Key Laboratory of Contemporary
Anthropology and State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China, 5Fudan-Taizhou Institute of Health Sciences,
Taizhou, Jiangsu, China, 6Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China, 7Department of Pathology, Fudan University
Shanghai Cancer Center, Shanghai, China, 8Department of Epidemiology, the University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
Background: Non-Hodgkin’s lymphoma (NHL) has been reported to be associated with autoimmune and pro-inflammatory
response, and genetic polymorphisms of candidate genes involved in autoimmune and pro-inflammatory response may
influence the susceptibility to NHL. To evaluate the role of such genetic variations in risk of NHL, we conducted a case-
control study of 514 NHL patients and 557 cancer-free controls in a Chinese population.
Method: We used the Taqman assay to genotype six potentially functional single nucleotide polymorphisms (SNPs) in six
previously reported inflammation and immune-related genes (TNF rs1799964T.C, LTA rs1800683G.A, IL-10 rs1800872T.G,
LEP rs2167270G.A, LEPR rs1327118C.G, TNFAIP8 rs1045241C.T). Logistic regression models were used to estimate odds
ratios (ORs) and 95% confidence intervals (95% CI).
Results: We observed a significantly increased risk of NHL associated with the TNFAIP8 rs1045241C.T polymorphism
(adjusted OR=3.03; 95% CI=1.68–5.45 for TT vs. CC and adjusted OR=2.03; 95% CI=1.53–2.69 for CT/TT vs. CC). The risk
associated with the T allele was more evident in subgroups of 40–60 year-old, non-smokers or light-smokers (less than 25
pack-years), and subjects with normal weight or overweight. Risk for both B and T cell non-Hodgkin’s lymphoma was
elevated for CT/TT genotypes (adjusted OR=1.95, 95% CI=1.41–2.70 for B cell NHL and adjusted OR=2.22, 95% CI=1.49–
3.30 for T cell NHL), particularly for DLBCL (adjusted OR=2.01, 95%CI=1.41–2.85) and FL (adjusted OR=2.53, 95% CI=1.17–
5.45). These risks were not observed for variant genotypes of other five SNPs compared with their common homozygous
Conclusions: The polymorphism of TNFAIP8 rs1045241C.T may contribute to NHL susceptibility in a Chinese population.
Further large-scale and well-designed studies are needed to confirm these results.
Citation: Zhang Y, Wang M-Y, He J, Wang J-C, Yang Y-J, et al. (2012) Tumor Necrosis Factor-a Induced Protein 8 Polymorphism and Risk of Non-Hodgkin’s
Lymphoma in a Chinese Population: A Case-Control Study. PLoS ONE 7(5): e37846. doi:10.1371/journal.pone.0037846
Editor: Jose Angel Martinez Climent, University of Navarra, Center for Applied Medical Research, Spain
Received January 16, 2012; Accepted April 26, 2012; Published May 29, 2012
Copyright: ? 2012 Zhang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by the funding from National Natural Science Foundation of China (30870985, 30973391) and the Ministry of Health
(201002007). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org (XYZ); email@example.com (XNH)
Non-Hodgkin’s lymphoma (NHL) incidence rates have been
increasing in both developed and developing countries with about
355,900 new cases in the world annually . In China, the most
common subtype of NHL is diffuse large B cell lymphoma
(DLBCL), whereas follicular lymphoma (FL) is less common than
in Western countries. Extranodal lesions and T/NK cell NHLs
(eg. Extranodal NK/T-cell lymphoma) appear to be more
common in China . However, the exact causes of NHL remain
largely unknown. Some evidence has showed that immune
dysfunction may be one of the risk factors , and single
nucleotide polymorphisms (SNPs) in immune and inflammatory
response genes may play an important role in lymphomagenesis
TNF/LTA and IL-10 genes code for immunoregulatory
cytokines that can mediate inflammation, apoptosis and Th1/
Th2 balance [6,7], and they may be good candidate genes for
studying lymphomagenesis. The cytokines TNF-a and LT-a are
thought to influence lymphomagenesis through up-regulation of
pro-inflammatory and anti-apoptotic signals, possibly via the NK-
kB pathway . Some evidence also showed that polymorphisms
PLoS ONE | www.plosone.org1 May 2012 | Volume 7 | Issue 5 | e37846
in IL-10 may regulate TNF-a levels and thus contribute to
activation of the NK-kB pathway . A pooled analysis including
7,999 NHL cases and 8,452 controls from 14 case-control studies
was carried out by InterLymph Consortium, which showed that
LTA 252A?G (rs909253), IL-1-3575T?A (rs1800890), and partic-
ularly TNF-308G?A (rs1800629) were associated with an increase
risk of DLBCL in non-Hispanic white populations . Purdue et
al. reported that IL-10-3575T?A and TNF-863C?A (rs1800630)
were associated with an elevated risk of DLBCL in an Australian
case-control study . A recent genome-wide association study
(GWAS) of FL has identified additional two variants in the 6p21
chromosomal region , which is the TNF gene location,
suggesting that genetic variants in these regions may influence
The tumor necrosis factor-a induced protein 8 (TNFAIP8)
family are newly identified proteins that are important for
inflammation and immune homeostasis . They play roles in
anti-inflammation by negatively regulating T cell receptor (TCR)
and Toll-like receptor (TLR) signaling . But the association
between TNFAIP8 polymorphisms and NHL risk has not been
reported so far, particularly in Chinese populations.
The circulating levels of adipocytokines, including adiponectin,
resistin and leptin may also alter immune system function and
chronic inflammatory response. Leptin has pro-inflammatory
properties and stimulates the growth of certain cancer cells as
well as circulating pro-inflammatory cytokines, such as TNF-a and
interleukin . Associations between NHL and polymorphisms
in the leptin (LEP) and leptin receptor (LEPR) gene have also been
reported. Skibola et al. found that the LEP19G allele was
associated with an increased risk of NHL, particularly FL .
A similar result was reported by a UK study, in which the LEPR
223ArgArg genotype was shown to be associated with an increased
risk of FL among women .
To test the hypothesis that polymorphisms in inflammation and
immune-related genes (such as TNF, LTA, IL-10, LEP, LEPR and
TNFAIP8) may be associated with susceptibility to NHL, we
conducted a case-control study in a Chinese population and
genotyped six potentially functional SNPs in the above-mentioned
Materials and Methods
The study was approved by the Institutional Review Board of
Fudan University Shanghai Cancer Center. Participation was
voluntary. All participants singed a written informed consent, and
all clinical investigation was conducted according to the principles
expressed in the Declaration of Helsinki consent.
The study population was identified from histologically
confirmed NHL cases diagnosed and treated between June 2005
and September 2011 at Fudan University Shanghai Cancer
Center. All patients came from the Eastern China, including
Shanghai, Jiangsu Province and the surrounding regions. Enroll-
ment criteria included the following: Chinese Han ethnicity, HIV
antibody negative, and without a diagnosis of second primary
malignancy. All cases were classified and reviewed according to
the 2008 WHO classification of tumors of haematopoietic and
lymphoid tissues . Of the 732 eligible cases, 514 (70.2%)
consented to participate in the study and provided blood samples.
Additionally, 557 cancer-free control subjects were all selected
from Taizhou Longitudinal Study (TZL) at the same time period.
All the control subjects were frequency matched to the cases on
age (according to cases’ age groups by every 5 years) and sex. The
cancer-free controls were genetically unrelated ethnic Han
Chinese who were not selected from family members of patients
and had no blood relationship. All participants were all from
Eastern China, and there was a major geographical overlap
between the cases and controls. TZL was initiated in July, 2007, in
Taizhou, Jiangsu province of China, as described previously .
After signing a written informed consent, all cases and controls
were asked to provide a blood sample of about 2 ml and
completed a questionnaire. Cases were asked about information
including age, sex, ethnicity, status of smoking and drinking, the
height and weight before diagnosis. The controls were also asked
to recall the same questions prior to the recruitment date. A total
of 514 cases and 557 controls provided blood samples, but 121
(23.5%) cases and 22 (3.9%) controls did not provide information
about smoking and drinking, and 91 (17.7%) cases failed to give
the exact information of height and weight before diagnosis.
We first screened five frequently reported (TNF, LTA, IL-10,
LEP, LEPR) and one newly identified (TNFAIP8) inflammation
and immune-related genes from published papers. We then
searched SNPs in these six genes by NCBI dbSNP database
(http://www.ncbi.nlm.nih.gov/) for all populations, and used
HapMap database (http://hapmap.ncbi.nlm.nih.gov/) to focus on
CHB (Han Chinese in Beijing, China) population. SNPinfo
(http://snpinfo.niehs.nih.gov/) website was used as a tool to
predict SNP functions. Potentially functional SNPs were defined to
fit at least three of following four criteria: (1) SNPs located at the
two ends of these genes, such as 59 near gene, 59 untranslated
region (UTR), 39 near gene or 39UTR; (2) the minor allele
frequency (MAF) was$5% in the Hapmap CHB population. (3)
SNP variation may affect transcription factor binding sites (TFBS)
activity in the putative promoter region (here defined as 2-kb
upstream from the first exon). (4) SNP may affect the microRNA-
binding sites activity. Then eight SNPs were identified (TNF
rs1799964T.C, LTA rs1800683G.A, IL-10 rs1800872T.G,
rs11064A.G). However, the linkage disequilibrium (LD) analysis
revealed that LEP rs2167270G.A and LEP rs4728096 T.C were
in quite high LD (r2=0.93), and TNFAIP8 rs1045241C.T and
rs11064A.G were also in LD with r2=0.85. As a result, we
selected following six potentially functional and representative
rs1800872T.G, LEP rs2167270G.A, LEPR rs1327118C.G,
TNFAIP8 rs1045241C.T) for further genotyping.
Genomic DNA was extracted from each blood sample by using
the Qiagen Blood DNA Mini Kit (Qiagen Inc., Valencia, CA)
according to the manufacturer’s instructions. DNA purity and
concentration were determined by spectrophotometric measure-
ment of absorbance at 260 and 280 nm by a UV spectrophotom-
eter (Nano Drop Technologies, Inc., Wilmington, DE) and all are
All TaqMan assays for this study including the pre-designed
SNP-genotyping assay mix containing PCR primers and probes
were purchased from ABI (Applied Biosystems, Foster City, CA).
Genotyping were conducted on the ABI 7900HT detection system
(Applied Biosystems). To ensure the accuracy of genotyping
results, four negative controls (no DNA) and four duplicated
TNFAIP8 Polymorphism and NHL Risk
PLoS ONE | www.plosone.org2 May 2012 | Volume 7 | Issue 5 | e37846
samples were included in each of the 384-well plates for the quality
control. The assays were repeated for 5% of the samples, and the
results were 100% concordant. The analyzed fluorescence results
were then auto-called into the genotypes using the built-in SDS2.2
software of the system.
BMI was calculated as weight (kg) divided by the square of the
height (m). In this study, we used the BMI cutoff points as
suggested by the Cooperative Meta-Analysis Group of Working
Group on Obesity in China . If BMI,18.5 kg/m2, the
18.5#BMI#24.0 kg/m2was defined as normal weight and
BMI.24.0 kg/m2was defined as overweight. Smoking status
was divided into smokers and non-smokers by whether or not they
had smoked for more than one year. Those who drank alcoholic
beverages at least once a week for one year or more were defined
as alcohol users, while the others were non-users. Differences in
the distributions of the alleles and genotypes as well as
demographic characters, smoking status, alcohol use and BMI
between the cases and controls were evaluated by the Chi-square
test. The Hardy–Weinberg equilibrium (HWE) of genotype
distribution in the controls was tested by a goodness-of-fit Chi-
square test. Unconditional univariate and multivariate logistic
regression models were applied to calculate crude and adjusted
odds ratios (ORs) and 95% confidence intervals (95% CI). We
used the multiple imputation (MI) method by SAS 9.1 software to
handle missing covariate information. All covariates were imput-
ed, when calculated adjusted ORs, according to the distributions
of observed values of either cases or controls. All statistical tests
were two-sided, and P,0.05 was considered statistically signifi-
cant. All analyses were performed using SAS Software, version 9.1
(SAS Institute, Cary, NC).
Characteristics of the Study Population
There were 514 NHL cases and 557 cancer-free controls
included in this study, whose DNA samples were available. The
frequency distributions of demographic and some selected
characteristics of the participants are shown in Table 1. There
was no statistical difference in the distributions of age and sex
between cases and controls because of frequency matching by
design. The mean age was 49.3 years for the cases (614.1; range,
15–85) and 49.6 years for the controls (613.5; range, 20–85;
P=0.895), and 64.0% of the cases and 63.4% of the controls were
male (P=0.830). The controls were more likely to be smokers and
alcohol users (P,0.0001 and P=0.0001, respectively) and have
higher BMI (P,0.0001) compared with the cases. Consequently,
smoking status, alcohol use and BMI were adjusted for in the
subsequent multivariate logistic regression analyses. Of the 514
cases, 336 (65.4%) were B cell lymphoma, and 178 (34.6%) were T
cell and natural killer (NK) cell lymphoma. After stratified, 233
(45.3%), 52 (10.1%), 51 (9.9%), 146 (28.4%), 32 (6.2%) were
DLBCL, FL, other B cell lymphoma, NK/T cell lymphoma, and
other T cell lymphoma, respectively. Of all cases, 322 (62.6%) had
an Ann Arbor Stage of I–II and 192 (37.4%) had a later Ann
Arbor Stage of III–IV.
Association between Selected SNPs and Risk of NHL
Genotype distributions of the selected six SNPs in cases and
controls and their associations with NHL risk are presented in
Table 2. All observed genotype distributions among controls
were in agreement with the Hardy–Weinberg equilibrium
(P=0.285 for rs1799964, P=0.777 for rs180683, P=0.872 for
rs1800872, P=0.733 for rs2167270, P=0.559 for rs1327118,
P=0.420 for rs1045241). Significant difference in the genotype
frequencies was observed between the cases and controls for
TNFAIP8 rs1045241C.T (P,0.0001). When the rs1045241CC
genotype was used as the reference, the CT heterozygous, TT
homozygous and combined CT/TT genotypes were associated
Table 1. Characteristics of Non-Hodgkin’s lymphoma cases
and cancer-free controls.
Variables Cases No. (%)Controls No. (%) P-valuea
All subjects 514 (100) 557 (100)
Median (Range) 50.5 (15–85)51.0 (20–85)
,40 129 (25.10)131 (23.52)
40–60 269 (52.33)299 (53.68)
.60 116 (22.57)127 (22.80)
Male329 (64.0)353 (63.4)
Female 185 (36.0)204 (36.6)
Smoker 101 (25.7)232 (43.4)
Non-smoker 292 (74.3)303 (56.6)
0 292 (74.3)302 (56.4)
0–2564 (16.3) 171 (32.0)
.2537 (9.4)62 (11.6)
Yes58 (14.8) 134 (25.0)
No335 (85.2)401 (75.0)
,18.529 (6.9)21 (3.8)
18.5–24.0 253 (59.8)273 (49.0)
.24.0141 (33.3) 263 (47.2)
B cell lymphoma 336 (65.4)–
FL 52 (10.1)–
Other B cell
T cell lymphoma 178 (34.6)–
NK/T 146 (28.4)–
Other T cell
Ann Arbor Stage–
aP value of the comparison with a two-sided Chi-square test.
TNFAIP8 Polymorphism and NHL Risk
PLoS ONE | www.plosone.org3May 2012 | Volume 7 | Issue 5 | e37846
with significantly increased risk of NHL (adjusted OR=1.88;
95% CI=1.39–2.53 for CT heterozygous genotype, adjusted
OR=3.03; 95% CI=1.68–5.45 for TT homozygous genotype,
adjusted OR=2.03; 95% CI=1.53–2.69 for CT/TT genotype)
after adjustment for age, sex, BMI, smoking and drinking status.
However, no significantly altered NHL risk was found for variant
genotypes of the other five SNPs compared with their common
genotypes. We also evaluated the combined effect of all the six
SNPs. We had divided the subjects into seven groups according
to the number of combined variant genotypes. The ‘‘0’’ risk
genotype group was used as the reference, and unconditional
logistic regression models were applied to calculate OR and
95%CI for each group. But we did not find any significant
association between the combined effect of these six SNPs and
risk of NHL (data not shown).
We further evaluated the association between the six candidate
SNPs and risk of NHL by subgroups of age, sex, smoking status,
alcohol use, BMI, common subtypes and Ann Arbor stage. No
statistical significances were found for the SNPs except TNFAIP8
rs1045241 C.T. The stratified analysis results of TNFAIP8
rs1045241 C.T are presented in Table 3. In general, an
increased risk associated with rs1045241 CT/TT genotypes was
more evident in subgroups of 40–60 year-old individuals (adjusted
OR=1.90, 95% CI=1.35–2.68) or smoked less than 25 pack-
years (adjusted OR=2.45, 95% CI=1.32–4.54), and normal
weight (adjusted OR=1.76, 95% CI=1.23–2.52) or overweight
groups (adjusted OR=2.07, 95% CI=1.32–3.25). Moreover, the
patients with rs1045241 CT/TT genotypes were associated with
Table 2. Genotypes distributions of the selected functional polymorphisms among NHL cases and cancer-free controls and their
associations with NHL risk.
P Crude OR (95% CI)P Adjusted ORa(95% CI)P
TNF rs1799964T.C 0.590b
TT 315 61.3358 64.31.00 1.00
CT183 35.6182 32.71.14 (0.89–1.48)0.3051.10 (0.82–1.47) 0.531
CC16 3.1173.0 1.07 (0.53–2.15) 0.8500.75 (0.33–1.70)0.495
CT/CC199 38.7199 35.7 0.312c
1.14 (0.89–1.46) 0.312 1.06 (0.80–1.41)0.667
LTA rs1800683G.A 0.238b
GG125 24.3161 28.91.00 1.00
AG27553.5 28050.31.26 (0.95–1.68) 0.1081.30 (0.94–1.81)0.112
AA11422.2 11620.81.27 (0.89–1.79)0.1851.30 (0.88–1.94) 0.188
AG/AA389 75.739671.1 0.090c
1.26 (0.96–1.66)0.090 1.30 (0.96–1.78)0.094
IL-10 rs1800872T.G 0.279b
TT226 44.0 26948.3 1.00 1.00
GT 228 44.3 23542.21.15 (0.90–1.49)0.2671.23 (0.92–1.65)0.158
GG 6011.7 539.51.35 (0.89–2.03) 0.154 1.46 (0.92–2.31)0.106
GT/GG288 56.0288 51.7 0.156c
1.19 (0.94–1.51) 0.1561.28 (0.97–1.68) 0.083
LEP rs2167270G.A 0.801b
GG 322 62.6338 60.71.001.00
AG 16632.3 19034.10.92 (0.71–1.19)0.5120.93 (0.69–1.25)0.631
AA26 5.129 5.20.94 (0.54–1.63)0.8290.92 (0.48–1.76)0.792
AG/AA 19237.4219 39.30.509c
0.92 (0.72–1.18) 0.5090.93 (0.70–1.23)0.607
CC 39075.9 41274.01.001.00
CG11622.613624.40.90 (0.68–1.20)0.4720.93 (0.67–1.29)0.645
GG81.59 1.60.94 (0.36–2.46)0.8981.19 (0.39–3.68)0.760
CG/GG 12424.1 14526.00.472c
0.90 (0.69–1.19)0.473 0.94 (0.68–1.29)0.705
CT 18035.0 15628.01.50 (1.15–1.95)0.0031.88 (1.39–2.53)
TT41 8.020 3.62.67 (1.53–4.65) 0.00053.03 (1.68–5.45) 0.0002
CT/TT 22143.0 17631.6 0.0001c
1.63 (1.27–2.10) 0.00012.03 (1.53–2.69)
Statistically significant results (P,0.05) are highlighted in bold.
aORs were obtained from logistic regression models with adjustment for age, sex, smoking status, alcohol use and BMI.
bTwo-sided Chi-square test for distribution of three genotypes.
cTwo-sided Chi-square test for distribution of combined genotypes.
TNFAIP8 Polymorphism and NHL Risk
PLoS ONE | www.plosone.org4 May 2012 | Volume 7 | Issue 5 | e37846
risk of both B and T cell lymphoma (adjusted OR=1.95, 95%
CI=1.41–2.70 and adjusted OR=2.22, 95% CI=1.49–3.30,
respectively). After stratifying by histological subtypes, we observed
increased risk for DLBCL (adjusted OR=2.01, 95%CI=1.41–
2.85) and FL (adjusted OR=2.53, 95%CI=1.17–5.45), but no
significant association was found for NK/T cell lymphoma
(adjusted OR=0.79, 95%CI=0.27–2.33). However, after we
verified homogeneity assumption by using a Chi square-based Q-
test. The results indicated that an increased NHL risk associated
with CT/TT genotypes was particularly more pronounced only in
subgroups of 40–60 year-old (P=0.03), and patients with a later
Ann Arbor stage of III–IV (P=0.04).
Genetic polymorphisms in immune-related genes that regulate
the immune and inflammation response may play an important
role in the incidence of NHL [19,20]. In this case-control study,
we reported that TNFAIP8 rs1045241C.T was significantly
associated with an increased risk of NHL in a Chinese population.
Stratified analyses revealed that subgroups of 40–60 years, non-
smokers or light-smokers (#25 pack-years), subjects with normal
weight or overweight were more likely to have been diagnosed
with NHL, especially the subtypes of DLBCL and FL. According
to epidemiologic data about NHL in China, the main subtypes of
lymphoma are DLBCL, FL and NK/T lymphoma, and other
subtypes have a small sample size, and therefore we only analyzed
the dominant subtypes of the cases. These results support the
Table 3. Stratification analysis of the association between TNFAIP8 rs1045241C.T and NHL risk.
CT+ +TT (cases/
n%n% Crude Adjustedb
All subjects221/176 43.0/31.6293/381 57.0/68.40.0001 1.63(1.27–2.10) 2.03(1.53–2.69)
Age (year) 0.030
,40 56/4243.3/32.1 73/8956.6/67.9 0.2591.63(0.98–2.70) 1.43(0.77–2.67)
, ,0.0001 2.13(1.50–3.03) 2.99(2.03–4.43)
.60 46/53 39.7/41.770/74 60.3/58.30.8400.92(0.55–1.53) 0.94(0.51–1.72)
Male 143/10643.5/30.0 186/24756.5/70.00.00031.79(1.31–2.45) 2.09(1.46–3.01)
Female78/7042.2/34.3 107/13457.8/65.7 0.0061.39(0.92–2.10) 1.91(1.21–3.04)
Never 137/10146.9/33.7 155/201 53.1/66.30.0008 1.76(1.26–2.45)1.90(1.35–2.68)
#25 pack-year 31/47 48.4/27.533/12451.6/72.5 0.00242.48(1.37–4.49) 2.45(1.32–4.54)
.25 pack-year 18/2148.6/33.9 19/41 51.4/66.10.145 1.85(0.81–4.25)2.06(0.85–5.02)
Missing35/7 28.9/31.886/15 71.1/68.2–––
Yes29/3850/28.4 29/9650/71.6 0.0042.53(1.34–4.78)2.66(1.36–5.19)
No 157/13146.9/32.7 178/27053.1/67.3
, ,0.00011.82(1.35–2.45) 1.88(1.37–2.58)
,18.5 9/431.0/19.120/1769.0/80.90.3401.91(0.50–7.33) 1.62(0.37–7.05)
18.5–24.0 117/9346.2/34.1 136/18053.8/65.9 0.00441.67(1.17–2.37)1.76(1.23–2.52)
.24.0 70/7949.6/30.0 71/18450.4/70.0
Missing 25/027.5/0 66/072.5/0–––
Ann Arbor Stage 0.040
I– II137/176 42.5/31.6185/38157.5/68.40.0011.60(1.21–2.13)1.89(1.38–2.59)
III–IV 84/17643.7/31.6 108/38156.3/68.40.0021.68(1.20–2.36) 2.28(1.51–3.46)
B cell NHL140/17641.7/31.6 196/38158.3/68.40.0021.55(1.17–2.05)1.95(1.41–2.70)
DLBCL 100/17642.9/31.6133/38157.1/68.4 0.0021.63(1.19–2.23) 2.01(1.41–2.85)
FL 29/17655.8/31.6 23/38144.2/68.4 0.00042.73(1.53–4.85)2.53(1.17–5.45)
T cell NHL81/17645.5/31.6 97/381 54.5/68.40.0007 1.81(1.28–2.55)2.22(1.49–3.30)
NK/T 11/17621.6/31.6 40/38178.4/68.40.137 0.59(0.30–1.19)0.79(0.27–2.33)
Statistically significant results (P,0.05) are highlighted in bold.
aP value of the comparison with a two-sided Chi-square test.
bORs were obtained from logistic regression models with adjustment for age, sex, smoking status, alcohol use and BMI.
cP value of the heterogeneity assumption with a Chi-square-based Q-test.
TNFAIP8 Polymorphism and NHL Risk
PLoS ONE | www.plosone.org5 May 2012 | Volume 7 | Issue 5 | e37846
hypothesis that some polymorphism in inflammation and immune-
related genes may be associated with risk of NHL and its common
TNFAIP8, also known as GG2-1, MDC-3.13, SCC-S2, is located
on chromosome 5 (5q23.1). It was first identified in a human head
and neck squamous cell carcinoma (HNSCC) cell line .
Recently, it has been reported to be associated with oncogenesis,
immunity, and inflammation in several studies [11,12,22].
TNFAIP8 mRNA over-expression has been found in various
malignant cell lines, such as breast cancer , non-small cell lung
cancer , and esophageal squamous cell carcinoma .
Evidence showed that TNFAIP8 expression was upregulated by
TNF-a induced NF-kB pathway activation in cancer cell lines,
which can inhibit caspase-8 and reduce apoptosis [26,27]. Tumor
necrosis factor-a induced protein 8-like 2 (TIPE2), a member of
the TNFAIP8 family, was originally identified as a gene
abnormally expressed in the inflamed spinal cord of mice with
experimental autoimmune encephalomyelitis . TIPE2-deficent
mice were more likely to suffer from chronic inflammation diseases
and multiple organ inflammation . In humans, the abnormal
expression of TIPE2 was associated with systemic autoimmunity
, diabetic nephropathy , and hepatitis B . These
studies supported that TIPE2 plays an important role in
maintaining immune homeostasis.
However, fewer studies have focused on the association between
polymorphisms of TNFAIP8 and NHL risk. The SNP TNFAIP8
rs1045241C.T included in the present study is located on the
39UTR of TNFAIP8, and the SNP function prediction shows that
it may affect the microRNA-binding sites activity (http://snpinfo.
niehs.nih.gov/snpfunc.htm). Some evidence has indicted that a
genetic polymorphism in a microRNA target site influence
transcriptional and post-transcriptional gene expression in cancers
. Though we found that rs1045241C.T was associated with
the risk of NHL, the exact mechanisms of this relationship
remained unknown. Further functional studies have been
conducting to confirm our results.
In the present study, we did not find any significant association
between the investigated polymorphisms of TNF rs1799964T.C,
rs2167270G.A, LEPR rs1327118C.G and NHL risk in a
Chinese population. However, many pieces of evidence have
shown that other SNPs in these genes like TNF–308 G.A
(rs1800890), LEPR 233Q.R (rs1137101) increased the risk of
NHL, particularly DLBCL in non-Hispanic white populations
[9,14,15,32,33]. Our studies did not cover these SNPs because
their MAF was ,5% in CHB population according to Hapmap
database (http://hapmap.ncbi.nlm.nih.gov/). But Xiao et al.
reported that no association between TNF–308, LTA 252
polymorphisms and histological subtypes, disease stages in Chinese
NHL patients . We believe that this inconsistency may be due
to ethnic and demographic differences between Chinese popula-
tions and white populations as well as the smaller sample size that
may also have led to insufficient statistical power. So our results
need to be replicated in additional studies in different populations
with large sample sizes.
Fewer studies reported an association between LEP 19 A.G,
LEPR 233Q.R and risk of NHL in Chinese populations, though
some previous published studies revealed that obesity was a risk
factor of NHL in developed countries . Obesity may play the
role of inducing lymphomagenesis by the co-effects of LEP 19
A.G, LEPR 233Q.R polymorphisms and immune dysfunction
[14,15,36]. Inconsistently, we found the NHL cases were more
likely to have lower BMI compared with the cancer-free controls
(P,0.0001). One possible reason may due to ethnic difference in
BMI and its contribution to lymphoma. It is likely that populations
of developing and developed countries have different body size,
and Asians have lower BMI than Westerners. Thus the Chinese
standard of obesity was applied to the subjects in this study, but
obesity did not appear closely related to lymphomagenesis.
However, another possibility was that obesity may not a direct
risk factor for NHL in Chinese populations. There may be some
intricate interaction among obesity, diet, hormone, genetics, and
susceptibility to NHL but the present study did not have enough
power to detect such interactions. Further larger, transcontinental
studies are needed to fully explore and elaborate the extent of such
In the stratified analyses, the risk effect of the TNFAIP8
rs1045241 CT/TT genotype was more evident in subgroups of
40–60 year-old, non-smoker or light-smoker, and normal BMI or
overweight. Stratifying by subtypes and stage, the risk effect of
rs1045241 CT/TT genotype was evident for both B and T cell
lymphoma, especially for DLBCL and FL as well as later Ann
Arbor Stage of ?–?. But we noticed that after verification of
homogeneity assumption, only age and stage showed marginally
statistical significance. So, there was no strong evidence of an effect
between the selected covariates and NHL risk. The exact
association between selected covariates and NHL risk needs to
be further investigated by large studies. Up to now, there is still
inconsistence about the effect of smoking, alcohol use, and BMI on
NHL risk, and the mechanisms are multiple. Wang et al. once
reported that autoimmune conditions, obesity, and later birth
order could contribute to lymphomagensis through an alteration
of the proinflammatory pathway, specifically involving common
genetic variants in TNF and IL-10 . Given the results of our
study, NHL development may be of complex process that includes
a great number of events. A single factor would have a limited
effect on the susceptibility. Once the exposure of environmental
factors was accumulated to a certain level, alteration took place in
immune and proinflammatory pathways, and genetic variants or
other unknown events may also participate in the process. On the
other hand, our study was not large enough, and possible selection
bias may exist due to a higher-than expected non-response rate in
case group. Well-designed large studies are needed to test the
hypothesis that TNFAIP8 polymorphisms may influence NHL
susceptibility, particularly by the possible interactions with
environmental risk factors.
Several limitations exist in the present study. First, the sample
size was not large enough, so selection bias may be inevitable, and
the study did not provide enough statistical power to detect gene-
gene and gene-environment interaction. Second, we selected only
one functional SNP for each candidate gene, which restricted
further analysis to identify other potentially important associations.
Moreover, different histological subtypes of NHL, such as
DLBCL, FL, and NK/T cell lymphoma may have different risk
factors and etiologies. We did not have enough information about
environmental exposure from NHL patients, such as use of hair
dye, family history of NHL, occupation and EB-virus infection to
be included in stratified analyses.
In summary, we identified TNFAIP8 rs1045241C.T to be
associated with risk of NHL in a Chinese population, particularly
DLBCL and FL. To the best of our knowledge, this is the first
study to report a TNFAIP8 polymorphism associated with NHL
susceptibility in a Chinese population, which provides additional
evidence that inflammation and immune-related genetic polymor-
phisms play an important role in lymphomagenesis. Further large-
scale well-designed population-based studies are needed to validate
TNFAIP8 Polymorphism and NHL Risk
PLoS ONE | www.plosone.org6 May 2012 | Volume 7 | Issue 5 | e37846
our findings, define the high-risk population and deepen our
understanding of NHL pathogenesis.
We thank Yan-Zi Gu, Huan Chen, Ting-Yan Shi, and Mei-Ling Zhu for
their help in collecting blood samples and DNA extraction, and Yu-Hu
Xin for his assistance in technical support.
Conceived and designed the experiments: XYZ. Performed the experi-
ments: YZ KQX. Analyzed the data: YZ MYW JH. Contributed reagents/
materials/analysis tools: JCW YJY LJ ZYC XJM MHS QYW. Wrote the
paper: YZ XYZ QYM. Supplied clinical information: XNH.
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