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Article

Risk Prediction for Gastric Cancer Using GWAS-Identifie Polymorphisms, Helicobacter pylori Infection and Lifestyle-Related Risk Factors in a Japanese Population

by
Naoyo Ishikura
1,2,
Hidemi Ito
3,4,*,
Isao Oze
1,
Yuriko N. Koyanagi
3,
Yumiko Kasugai
1,
Yukari Taniyama
3,
Yukino Kawakatsu
1,
Tsutomu Tanaka
5,
Seiji Ito
6,
Masahiro Tajika
5,
Yasuhiro Shimizu
5,
Yasumasa Niwa
7 and
Keitaro Matsuo
1,8
1
Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya 464-8681, Japan
2
Department of Pathology, International Goodwill Hospital, Yokohama 245-0006, Japan
3
Division of Cancer Information and Control, Aichi Cancer Center Research Institute, Nagoya 464-8681, Japan
4
Division of Descriptive Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya 466-0065, Japan
5
Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya 464-8681, Japan
6
Department of Gastrointestinal Surgery, Aichi Cancer Center Hospital, Nagoya 464-8681, Japan
7
Aichi Cancer Center Hospital, Nagoya 464-8681, Japan
8
Division of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya 466-0065, Japan
*
Author to whom correspondence should be addressed.
Cancers 2021, 13(21), 5525; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13215525
Submission received: 1 September 2021 / Revised: 27 October 2021 / Accepted: 30 October 2021 / Published: 3 November 2021
(This article belongs to the Section Cancer Epidemiology and Prevention)
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Abstract

:

Simple Summary

Gastric cancer remains the major cancer in Japan and worldwide. It is expected that practical intervention strategies for prevention, such as personalized approaches based on genetic risk models, will be developed. Here, we developed and validated a risk prediction model for gastric cancer using genetic, biological, and lifestyle-related risk factors. Results showed that the combination of selected GWAS-identified SNP polymorphisms and other predictors provided high discriminatory accuracy and good calibration in both the derivation and validation studies; however, the contribution of genetic factors to risk prediction was limited. The greatest contributor to risk prediction was ABCD classification (Helicobacter pylori infection-related factor).

Abstract

Background: As part of our efforts to develop practical intervention applications for cancer prevention, we investigated a risk prediction model for gastric cancer based on genetic, biological, and lifestyle-related risk factors. Methods: We conducted two independent age- and sex-matched case–control studies, the first for model derivation (696 cases and 1392 controls) and the second (795 and 795) for external validation. Using the derivation study data, we developed a prediction model by fitting a conditional logistic regression model using the predictors age, ABCD classification defined by H. pylori infection and gastric atrophy, smoking, alcohol consumption, fruit and vegetable intake, and 3 GWAS-identified polymorphisms. Performance was assessed with regard to discrimination (area under the curve (AUC)) and calibration (calibration plots and Hosmer–Lemeshow test). Results: A combination of selected GWAS-identified polymorphisms and the other predictors provided high discriminatory accuracy and good calibration in both the derivation and validation studies, with AUCs of 0.77 (95% confidence intervals: 0.75–0.79) and 0.78 (0.77–0.81), respectively. The calibration plots of both studies stayed close to the ideal calibration line. In the validation study, the environmental model (nongenetic model) was significantly more discriminative than the inclusive model, with an AUC value of 0.80 (0.77–0.82). Conclusion: The contribution of genetic factors to risk prediction was limited, and the ABCD classification (H. pylori infection-related factor) contributes most to risk prediction of gastric cancer.

Graphical Abstract

1. Introduction

Gastric cancer is the second most common cancer [1] and is the third leading cause of cancer death in men and women [2] in Japan. Despite dramatic declines in incidence and mortality rates in the last several decades, it still confirms its status as a major public health issue in this country. Epidemiological evidence for the development of gastric cancer has been accumulating, and Helicobacter pylori (H. pylori) infection is now confirmed to be a convincing risk factor for gastric cancer in Japanese [3,4], in addition to the subsequent chronic atrophic gastritis that follows H. pylori infection [5]. Stratification by a combination of H. pylori infection and atrophic gastritis, known as ABCD classification, was associated with gastric cancer risk in case–control studies [4,6] and well predicted the incidence of gastric cancer in prospective studies [7,8,9,10,11,12,13,14]. In contrast, consumption of fruits and vegetables is recognized as a protective factor in gastric cancer. A meta-analysis of global data showed that fruit and vegetable consumption is associated with a significant reduction in gastric cancer risk [15]. With regard to tobacco, an association with tobacco smoking has been clearly established worldwide [16], including Japan [17], and 11% of gastric cancer cases may be attributed to it [16]. Similarly, alcohol drinking is recognized as a cause of gastric cancer. A large pooled analysis found an association between heavy alcohol drinking and risk of gastric cancer [18].
Recently, genome-wide association studies (GWASs) have enabled genetic discoveries in various types of cancer, including gastric cancer. For example, a single-nucleotide polymorphism (SNP), prostate stem cell antigen (PSCA)-rs2294008, was found to confer susceptibility to gastric cancer risk both in Japan [19,20] and worldwide [21,22,23,24]. This effect was confirmed in a meta-analysis [25]. In addition, GWASs have identified a number of loci that confer susceptibility to gastric cancer, including mucin 1 (MUC1)-rs4072037 [26,27], phospholipase C epsilon 1 (PLCE1)-rs2274223 [27], protein kinase AMP-activated alpha 1 catalytic subunit (PRKAA1)-rs10074991 [26], PRKAA1-rs13361707 [28], Unc-5 family C-terminal like (UNC5CL)-rs2294693 [26], leucine-rich repeat and fibronectin type-III domain containing 2 (LRFN2)-rs2494938 [29], dynein axonemal heavy chain 11 (DNAH11)-rs2285947 [29], zinc finger and BTB domain-containing 20 (ZBTB20)-rs9841504 [28], ASH1-like histone lysine methyltransferase (ASH1L)-rs80142782 [30], LINC02161-rs7712641 [30], ABO, alpha 1-3-N-acetylgalactosaminyltransferase and alpha 1-3-galactosyltransferase (ABO)-rs7849280 [31], cut-like homeobox 2 (CUX2)-rs6490061 [31], and defensin beta 121 (DEFB121)-rs2376549 [31]. These advances in molecular epidemiological findings have the potential to impact cancer prevention. To our knowledge, however, their contribution to the prevention of gastric cancer at the population level in combination with environmental factors has not been evaluated.
In this study, we examined a risk prediction model using these GWAS-identified SNPs and several risk factors of gastric cancer for possible use in distinguishing people at high and low risk of gastric cancer in personalized prevention settings.

2. Materials and Methods

2.1. Study Population

Two independent case–control studies were conducted to develop a risk prediction model. The study subjects were selected from the participants of the Hospital Epidemiology Research Program at Aichi Cancer Center (HERPACC)-2 (2001–2005) for the derivation study and HERPACC-3 (2005–2013) for the validation study. The frameworks of HERPACC-2 and HERPACC-3 have been described elsewhere [32,33,34]. Briefly, all first-visit outpatients aged 20–79 were recruited to participate in the HERPACC-2 and -3. They were asked to fill in a questionnaire on lifestyle information before their first medical examination and provide blood samples. Response rate for enrollment was 97% for subjects in HERPACC-2, of whom half provided blood samples. In HERPACC-3, 66.4% of participants responded to the questionnaire, of whom 62% provided blood samples. In each study, cases were histologically diagnosed with gastric cancer, and controls were confirmed to have no cancer and no history of neoplasm. Controls were randomly selected and individually matched by age (± 5 years) and sex at a case–control ratio of 1:2 in the derivation study and 1:1 in the validation study. As a result, the present analysis included 696 cases/1392 controls in the derivation study and 795 cases/795 controls in the validation study. Written informed consent was obtained from all participants. The study was approved by the institutional ethics committee of Aichi Cancer Center.

2.2. Assessment of Helicobacter pylori Infection and Gastric Atrophy

All cases were examined for plasma IgG level for H. pylori using a commercially available direct enzyme-linked immunosorbent assay kit (‘E Plate “Eiken” H. pylori Antibody’; Eiken Kagaku, Tokyo, Japan). This kit is commonly used in medical studies in Japan [4,35]. A positive status for H. pylori infection was defined as an anti-H. pylori IgG antibody level > 10 U/mL in serum [4,35]. Serum pepsinogens (PG) were measured by chemiluminescence enzyme immunoassay, and gastric mucosal atrophy was defined by a PG I value ≤ 70 ng/mL and PG I/PG II ≤ 3 ng/mL [36,37]. We applied the ABCD classification [38,39], using the combination of statuses of H. pylori infection (Hp) and gastric atrophy (GA) defined by pepsinogen levels. Participants were classified into four groups: Group A [Hp(−) GA(−)], Group B [Hp(+) GA(−)], Group C [Hp(+) GA(+)], and Group D [Hp(−) GA(+)]).

2.3. Information on Lifestyle Risk Factors

To select lifestyle factors, we referred to Development and Evaluation of Cancer Prevention Strategies in Japan [3] and extracted risk/preventive factors for gastric cancer. In this matrix, smoking and H. pylori infection are certain risk factors, and vegetable and fruit intake are possible preventative factors for both men and women. Cereal intake (possible risk factor) and salt intake (almost certain risk factor) are omitted from lifestyle risk factors, as they cannot be estimated by our food frequency questionnaire.
Information on lifestyle factors was collected by a self-administered questionnaire. Smoking status was classified into three categories of never smoker, former smoker, and current smoker, with former smokers defined as those who had quit at least 1 year before study enrolment. Alcohol consumption status was classified into four categories: never, low, moderate, and heavy. Those who seldom or never drank were defined as never drinkers. Low drinking was defined as consumption on 4 days or fewer per week, moderate drinking as consumption of less than 46 g of ethanol on 5 days or more per week, and heavy drinking as consumption of more than 46 g ethanol on 5 days or more per week. Information on family history of gastric cancer was obtained in the two categories of yes and no regarding a history of gastric cancer in any first-degree relative. Consumption of fruits and vegetables was determined using a food frequency questionnaire, which included 43 single food items in eight frequency categories [40]. The food frequency questionnaire was validated using a 3-day weighed dietary record as standard, which showed that reproducibility and validity were satisfactory [40,41]. Participants were divided into three groups based on the distribution of fruit and vegetable consumption among controls in the derivation study (tertiles).

2.4. Examination of the GWAS-Identified Polymorphisms

We conducted literature searches through PubMed (https://0-www-ncbi-nlm-nih-gov.brum.beds.ac.uk/pubmed) on 1 November 2017, to select GWAS-identified polymorphisms for evaluation in this study. We selected fourteen gastric cancer-susceptible SNPs reported in previous GWASs, namely PSCA-rs2294008 [21], MUC1-rs4072037 [27], PLCE1-rs2274223 [27], PRKAA1-rs10074991 [26], PRKAA1-rs13361707 [28], UNC5CL-rs2294693 [26], LRFN2-rs2494938 [29], DNAH11-rs2285947 [29], ZBTB20-rs9841504 [28], ASH1L-rs80142782 [30], LINC02161-rs7712641 [30], ABO-rs7849280 [31], CUX2-rs6490061 [31], and DEFB121-rs2376549 [31], as candidate genetic factors for risk prediction. DNA in the buffy coat fraction of each participant was extracted using a QIAmp DNA blood mini kit (Qiagen K.K., Tokyo, Japan). The selected SNPs were genotyped using TaqMan Single Nucleotide Polymorphism Genotyping Assays (Applied Biosystems, Foster City, CA, USA). The quality of genotyping in our laboratory is routinely assessed by statistical evaluation using the Hardy–Weinberg test and re-genotyping of 5% of randomly sampled subjects.

2.5. Statistical Analysis

To create a risk prediction model, we selected established environmental and lifestyle factors of gastric cancer (smoking (never, former and current), alcohol consumption (never, moderate, high–moderate, and heavy), energy-adjusted fruit and vegetable intake (in tertiles among controls in the derivation study), family history of gastric cancer (first-degree relative), and ABCD classification (in indicator variables, A, B, C, and D)). We examined the impact of each risk factor by conditional logistic regression. Age as continuous, sex, family history of gastric cancer, and referral pattern were included as adjusted factors in the model. Subjects with an unknown status for these variables were assigned dummy variables for the missing categories and included in the analysis. To assess the specific impact of a selected factor, we estimated the odds ratios (ORs) and corresponding 95% confidence intervals (CI) using uni- and multivariable conditional logistic regression models in the derivation study. For genetic factors, we evaluated the impact of each polymorphism by OR, 95% CI, and p-value adjusted for age and sex in both studies. These were calculated using the per-allele model of conditional logistic regression. To create risk prediction models, we selected polymorphisms with a value of p < 0.01 in the derivation study as risk predictors.
Performance of the risk prediction model was assessed in both the derivation study (as “internal validation”) and the validation study (as “external validation”) using standard methods for measurement of discrimination and calibration [42]. Discriminability was assessed by calculating the area under the curve (AUC) in the receiver-operating characteristic (ROC) curve, commonly known as the concordance (c) statistic. In the ROC, sensitivity is shown on the y-axis and false positive rate on the x-axis; a straight line in ROC indicates random classification of cases and controls, with a minimum AUC of 0.5. An AUC value of 1 corresponds to perfect classification, while values of 0.7 and 0.8 rate the model as having acceptable discrimination ability and above 0.8 as having excellent discrimination ability [43]. The AUC values were compared using the method of DeLong et al. [44]. The calibration of the models was assessed by the Hosmer–Lemeshow goodness-of-fit statistic and calibration plots. Subjects were divided into subgroups by decile of predicted probability. The Hosmer–Lemeshow statistic is computed based on a χ2-test, which compares the observed frequencies with the predicted frequencies in the ten groups; a nonsignificant p-value indicates good calibration, whereas a significant p-value indicates disagreement between the predicted and observed outcomes. In a calibration plot, the mean predicted probability was plotted against the mean observed probability for each decile. Ideally, the predicted probability equals the observed probability, so perfect predictions should lie on the 45° line [42]. In addition, with perfect calibration, the estimated calibration slope equals 1 [45]. A slope below 1.0 reflects overfitting of the model [46], which indicates the need to shrink the regression coefficients [42].
All analyses were performed using Stata/SE 14 (Stata Corp, College Station, TX, USA).

3. Results

The two case–control studies were largely comparable (Table 1). The proportion of current smokers was higher in cases than controls in both (42.2% and 30.9% in the derivation study and 31.7% and 22.8% in the validation study, respectively), as was the prevalence of H. pylori infection (82.2% and 55.6 in the derivation study and 71.7% and 41.9% in the validation study, respectively). Cases were more likely to have daily fruit and vegetable consumption than controls in both studies. Alcohol consumption and family history showed no apparent difference between cases and controls.
Table 2 shows associations of selected lifestyle-related or biological factors in our prediction model, namely smoking, alcohol consumption, fruit and vegetable intake, and the ABCD classification with gastric cancer risk. We observed a statistically significant association with each selected factor in both studies, with the exception of alcohol consumption and fruit and vegetable intake. The results of the validation study and meta-analysis are presented in Table S1.
Table 3 presents the association between 14 polymorphisms and gastric cancer risk. We selected three polymorphisms, namely rs4072037, rs2294008 and rs7849280, with values of p < 0.01, to develop a risk prediction model. The results of the validation study and meta-analysis are presented in Table S2.
Next, we assessed the performance of the prediction model (Table 4 and Table 5; Figure 1 and Figure 2). The discriminative abilities in the validation study were similar to those in the derivation study. The inclusive model provided acceptable discrimination in both the derivation and validation studies with AUC values of 0.7677 (0.7465–0.789) and 0.7823 (0.7694 chromosome 0.814), respectively (Table 4 and Figure 1). In the derivation study, the inclusive model had a statistically significantly higher discriminatory ability than the other genetic and nongenetic models (p = 4.74 × 10−53). In the validation study, however, the environmental model was significantly more discriminative than the inclusive model, with an AUC value of 0.7925 (0.7705–0.815). The calibration analysis of the inclusive model revealed reasonably good agreement between the observed and predicted number of gastric cancer cases in groups defined by deciles of predicted risk distribution in both the derivation (p for Hosmer–Lemeshow test = 0.445) and validation studies (p = 0.116) (Table 5). Moreover, the calibration plots of the inclusive model stayed close to the ideal calibration line throughout the risk spectrum in all data sets of both studies (Figure 2), and all of their calibration slopes were close to 1.0.

4. Discussion

In this study, we developed a risk prediction model of gastric cancer using a combination of genetic, biological, and lifestyle-related risk factors. In the derivation study, discriminatory ability was slightly improved in the inclusive model, which consisted of both genetic and biological and lifestyle-related factors, than in the models that included only biological and lifestyle-related risk factors (environmental model). In the validation study, however, the environmental model was more discriminating than the inclusive model. The addition of genetic factors (SNPs) improved the performance of the risk prediction model only slightly, which suggests that genetic factors are less useful for risk prediction.
This study represents the first attempt to combine genetic, biological, and lifestyle-related risk factors in the prediction of gastric cancer risk. Several previous risk prediction models for gastric cancer were investigated in large-scale population-based cohort studies in Japan, but these did not include genetic factors. Namely, Charvat et al. developed a prediction model to estimate an individual’s risk of gastric cancer in Japan using a combination of age, sex, smoking, salted food consumption, family history of gastric cancer, and the ABCD classification [12], while Iida et al. developed a model in a cohort study in Japan using a combination of age, sex, combination of anti-H. pylori antibody and atrophic gastritis, hemoglobin A1c, smoking, drinking, and obesity [13]. In addition, Cai et al. recently developed a gastric cancer risk prediction rule in China based on a combination of age, sex, PG I/II ratio, gastrin-17 level, H. pylori infection, pickled food, and fried food. These models showed good performance, but did not include genetic factors [47].
Here, we selected MUC1-rs4072037, PSCA-rs2294008, and ABO-rs7849280 as genetic risk factors. MUC1-rs4072037 was identified in GWASs [26,27] and replicated in case–control studies [30,48] in East Asian countries. The membrane mucin MUC1 is a ligand for H. pylori in the stomach, and the SNP rs4072037 is known to determine a splicing acceptor site in the second exon of MUC1 [49]. MUC1-rs4072037 is an independent risk factor that influences tumor recurrence and disease-related death in diffuse-type gastric cancer, but not in intestinal-type gastric cancer [48]. PSCA-rs2294008 is a GWAS-identified susceptibility polymorphism for gastric cancer both in Japan and worldwide [19,20,21,22,24,25,50]. PSCA is expressed in differentiating gastric epithelial cells, shows a cell proliferation inhibitory effect in vitro, and is frequently downregulated in gastric cancer. PSCA-rs2294008 is a functional SNP that influences the transcriptional activity of the PSCA promoter; the T allele significantly suppresses its transcription activity, thus affecting susceptibility to diffuse-type gastric cancer [21]. ABO-rs7849280 was identified in a Japanese GWAS [31]. An association between blood type A and gastric cancer has been previously reported [51,52]. Tanikawa et al. revealed that the AA blood type has a higher frequency of the G allele of ABO-rs7849280 than other types. The risk G allele was associated with higher ABO mRNA expression, whereas ABO mRNA expression was significantly suppressed in H. pylori-infected stomach [31]. ABO-rs7849280 is a key regulator of host-bacterial interactions of H. pylori-related diseases and gastric cancer.
Among environmental and biological factors, the ABCD classification showed a particularly high AUC value. Consistent with previous studies, we found that H. pylori infection and gastric atrophy substantially impacted gastric cancer risk. Their contribution to risk prediction was considerable, with AUCs 0.7354 and 0.7885 in the ABCD classification in the derivation and validation studies, respectively. Group D is negative for H. pylori infection but confers a high risk of gastric cancer. It is well known that H. pylori can no longer survive when atrophy has severely progressed or in the metaplastic intestinal mucosa induced by H. pylori infection [13], and production of anti-H. pylori antibodies in these conditions may be reduced. Therefore, although the subjects in Group D were not positive for H. pylori infection, most had been previously infected and were therefore also at high risk of developing gastric cancer, such as those in Group C.
The addition of novel GWAS-identified susceptibility loci may contribute to improving the performance of risk models. To date, however, the degree of such improvement has remained unclear. For example, in Szulkin et al.’s prostate cancer risk prediction study [53], a polygenic risk score for 65 established susceptibility variants provided an area under the curve (AUC) of 0.67, and the addition of 68 new variants increased the AUC to 0.68. In a similar study of the development of polygenic risk scores for prediction of breast cancer [54], the AUC of the prospective study was 0.603 with 77 SNPs, 0.630 with 313 SNPs, and 0.636 with 3820 SNPs. These findings suggest that the addition of SNPs may improve performance, albeit only to a limited degree.
In our present study, we also investigated the effects of increasing the number of SNPs. We selected SNPs with values of p < 0.05 in the derivation study (rs2294008, rs4072037, rs7849280, rs10074991, rs2294693, rs80142782) and then used the six SNPs selected to construct a new genetic model. As a supplementary explanation, rs13361707 also has a value of p < 0.05. Since both rs10074991 and rs13361707 are SNPs for PRKAA1 and in linkage disequilibrium, we chose rs10074991, which has a smaller p-value. Results showed an improvement in the AUC of the inclusive model in both the derivation (0.7728) and validation studies (0.7871). However, even with the inclusion of these six SNPs, the AUC of the environmental model was higher than that of the inclusive model in the validation study. In addition, in calibration using the Hosmer–Lemeshow test, p = 0.018 in the validation study, and the calibration could not be performed. Accordingly, although the addition of genetic factors had a positive effect on improving validation ability, these were not as great as expected due to the large impact of H. pylori infection and ABCD classification. For cancers that are significantly affected by one environmental factor, such as H. pylori in gastric cancer, the contribution of genetic risk factors to risk prediction may be limited.
Our study has several strengths. First, it was relatively large, and information was available on genetics, as well as H. pylori infection status, serologically defined gastric atrophy, and lifestyle characteristics. This allowed us to provide reliable estimates of risk factor effects and the performance of the model. Second, the constructed model was validated in a different dataset. Third, potential confounding by age and sex were considered by matching. Fourth, the allele frequencies of each SNP in the controls of this study were similar to that reported in HapMap JPT (available at http://0-www-ncbi-nlm-nih-gov.brum.beds.ac.uk/snp, accessed on 10 June 2020), warranting the comparability of our results for genetic factors with those in general populations in Japan.
Several limitations should also be noted. First, our lifestyle factors were obtained in a retrospective manner, and H. pylori/atrophy information was obtained in a cross-sectional setting. Validation of the model in prospective studies is clearly warranted, and until then, application in prospective settings requires caution. Second, H. pylori infection and gastric mucosal atrophy status were defined by serological tests. Cutoff levels for defining negativity of serum anti-H. pylori antibody titers are reported to be too high [55], and H. pylori infection status might have been wrongly classified. If so, this might have introduced status misclassification, which would nevertheless have been nondifferential. Accordingly, the impact of these factors may have been underestimated. Third, although salt intake is known as a “probable” risk factor for gastric cancer [3] and the attributable fraction of salt intake is not negligible [56], it was not considered in the study. The addition of salt information might improve model performance, and should therefore be considered for future studies. Finally, residual confounding by known and unknown factors in the model might be present.

5. Conclusions

We developed and validated a risk prediction model for gastric cancer using genetic, biological, and lifestyle-related factors in Japanese. The contribution of genetic factors to risk prediction was limited due to the large impact of the ABCD classification (H. pylori infection-related factor).

Supplementary Materials

The following are available online at https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/cancers13215525/s1, Table S1: Associations of epidemiological and clinical risk factors in stomach cancer (validation study and meta-analysis), Table S2: Associations with Asian GWAS-identifed susceptibility polymorphism in stomach cancer risk (validation study and meta-analysis).

Author Contributions

Conceptualization, H.I. and K.M.; methodology, N.I., H.I., Y.N.K. and K.M.; software, N.I., H.I. and Y.N.K.; validation, H.I.; formal analysis, N.I.; investigation, Y.K. (Yumiko Kasugai); resources, T.T, S.I., M.T., Y.S. and Y.N.; data curation, I.O., Y.N.K., Y.T. and Y.K. (Yukino Kawakatsu); writing—original draft preparation, N.I.; writing—review and editing, H.I. and K.M.; visualization, N.I.; supervision, H.I. and K.M.; project administration, H.I.; funding acquisition, H.I. and K.M. All authors have read and agreed to the published version of the manuscript.

Funding

This study is supported by Grants-in-Aid for Scientific Research from the Ministry of Education, Science, Sports, Culture and Technology of Japan (Priority Areas of Cancer (No. 17015018), Innovative Areas (No. 221S0001), and JSPS KAKENHI Grants (15K08792, 16H06277, 26253041, 15H02524, 18K10037, 19K10659)), by Grant-in-Aid for the Third-Term Comprehensive 10-year Strategy for Cancer Control from the Ministry of Health, Labor and Welfare of Japan and by AMED under Grant Number JP20ck0106553.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the institutional ethics committee of Aichi Cancer Center (Protocol Code: 2020-2-24 and 2020-2-25; approved on 5 February 2021).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data are not publicly available due to ethical and data security requirements.

Acknowledgments

The authors are grateful to the doctors, nurses, and technical and administrative staff of Aichi Cancer Center for daily administration of the HERPACC study. We are greatly indebted to the staff of the Aichi Cancer Center Hospital for their support and helpful discussion.

Conflicts of Interest

The authors declare no conflict of interest.

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Cancers 13 05525 g001 550
Figure 1. Area under the curve (AUC) of the derivation (A) and validation (B) studies. In the derivation study, the inclusive model had a statistically significantly higher discriminatory ability than the other models. In the validation study, however, the environmental model was significantly more discriminative than the inclusive model.
Figure 1. Area under the curve (AUC) of the derivation (A) and validation (B) studies. In the derivation study, the inclusive model had a statistically significantly higher discriminatory ability than the other models. In the validation study, however, the environmental model was significantly more discriminative than the inclusive model.
Cancers 13 05525 g001
Cancers 13 05525 g002 550
Figure 2. Calibration slope in the derivation (A) and validation (B) study (inclusive model). The calibration plots of the inclusive model stayed close to the ideal calibration line throughout the risk spectrum in all data sets of both studies, and calibration slopes were close to 1.0.
Figure 2. Calibration slope in the derivation (A) and validation (B) study (inclusive model). The calibration plots of the inclusive model stayed close to the ideal calibration line throughout the risk spectrum in all data sets of both studies, and calibration slopes were close to 1.0.
Cancers 13 05525 g002
Table
Table 1. Characteristics of participants.
Table 1. Characteristics of participants.
CharacteristicsDerivation StudyValidation Study
Cases Controls Cases Controls
n = 69633.33%n = 139266.67%n = 79550.00%n = 79550.00%
SexMale52074.71040 74.7 590 74.2 590 74.2
Female176 25.3 352 25.3 205 25.8 205 25.8
Age (years)<4034 4.9 74 5.3 34 4.3 35 4.4
40–4972 10.3 174 12.5 62 7.8 77 9.7
50–59244 35.1 436 31.3 212 26.7 201 25.3
60–69210 30.2 458 32.9 319 40.1 310 39.0
>70136 19.5 250 18.0 168 21.1 172 21.6
Smoking statusNever222 31.9 538 38.7 277 34.8 321 40.4
Former180 25.9 423 30.4 263 33.1 292 36.7
Current294 42.2 430 30.9 252 31.7 181 22.8
Unknown0 0.0 1 0.1 3 0.4 1 0.1
Alcohol consumptionNever228 32.8 469 33.7 284 35.7 268 33.7
Low250 35.9 558 40.1 253 31.8 309 38.9
Moderate113 16.2 209 15.0 122 15.4 114 14.3
Heavy86 12.4 130 9.3 130 16.4 101 12.7
Unknown19 2.7 26 1.9 6 0.8 3 0.4
Fruit/vegetable intake aHigh intake259 37.2 448 32.2 317 39.9 278 35.0
Moderate intake215 30.9 477 34.3 251 31.6 249 31.3
Low intake 220 31.6 458 32.9 227 28.6 268 33.7
Unknown2 0.3 9 0.7 0 0.0 0 0.0
Family history of gastric cancerYes153 22.0 244 17.5 196 24.7 136 17.1
No543 78.0 1148 82.5 599 75.4 659 82.9
H.pylori (Hp) IgG testNegative124 17.8 618 44.4 225 28.3 462 58.1
Positive572 82.2 774 55.6 570 71.7 333 41.9
Gastric atrophy (GA) defined by PG testingNegative261 37.5 910 65.4 426 53.6 637 80.1
Positive435 62.5 482 34.6 369 46.4 158 19.9
Unknown0 0.0 0 0.0
ABCD classificationGroup A [Hp(−) GA(−)]689.7755840.1 150 18.9 446 56.1
Group B [Hp(+) GA(−)]19327.7335225.3 276 34.7 191 24.0
Group C [Hp(+) GA(+)]37954.4542230.3 294 37.0 142 17.9
Group D [Hp(−) GA(+)]568.05604.3 75 9.4 16 2.0
Referral pattern to hospitalPatient’s discretion12618.142330.3911214.0912615.85
Family recommendation11917.122916.4513516.98668.3
Referral from another clinic30844.2536726.3629436.9825031.45
Secondary screening after primary screening13519.433323.9218723.526933.84
Other30.4380.5740.570.88
Unknown50.72322.3637.92779.69
a Third quantile of fruits/vegetables is the third quantile of intake in the control group. As tertiles for HERPACC2 and HERPACC3 were calculated separately, cutoff values differ between the derivation study (HERPACC2) and validation study (HERPACC3), as follows. Derivation study: high intake (197.43 g/day or more), moderate intake (109.35–197.43 g/day), and low intake (less than 109.35 g/day). Validation study: high intake (197.59 g/day or more), moderate intake (109.41–197.59 g/day), and low intake (less than 109.41 g/day).
Table
Table 2. Associations of epidemiological and clinical risk factors in stomach cancer (derivation study).
Table 2. Associations of epidemiological and clinical risk factors in stomach cancer (derivation study).
CharacteristicsDerivation Study
Model 1Model 2
OR a (95% CI)OR b (95% CI)
Smoking c
NeverReference
Former1.16 (0.88–1.53)1.07 (0.78–1.47)
Current1.89 (1.47–2.44)1.81 (1.34–2.46)
p for trend1.75 × 10−7 2.59 × 10−5
Alcohol consumption c
NeverReference Reference
Low0.94 (0.74–1.18)0.93 (0.72–1.21)
Moderate1.12 (0.84–1.50)1.01 (0.72–1.41)
Heavy1.40 (1.00–1.96)1.29 (0.86–1.94)
p for trend3.38 × 10−2 2.30 × 10−1
Fruit and vegetable intake c
Highest tertileReference Reference
Middle tertile0.95 (0.76–1.20)0.88 (0.67–1.14)
Lowest tertile1.22 (0.96–1.54)1.01 (0.77–1.33)
p for trend9.32 × 10−2 9.05 × 10−1
H. pylori infection (Hp)
NegativeReference Reference
Positive3.57 (2.84–4.47)2.54 (1.96–3.28)
p for trend3.57 × 10−28 1.08 × 10−12
Gastric atrophy (GA) c
NegativeReference Reference
Positive3.29 (2.69–4.03)2.54 (2.01–3.20)
p for trend8.22 × 10−31 4.00 × 10−15
ABCD stratification c
(Hp and GA)A: Hp-Negative; GA-NegativeReference Reference
B: Hp-Positive; GA-Negative4.45 (3.23–6.12)4.42 (3.15–6.18)
C: Hp-Positive; GA-Positive7.67 (5.64–10.43)8.09 (5.81–11.25)
D: Hp-Negative; GA-Positive8.28 (5.21–13.17)8.84 (5.38–14.53)
p for trend1.21 × 10−38 4.27 × 10−36
OR, odds ratio; CI, confidence interval. a Crude OR by the conditional logistic regression model. For Fruit and vegetable intake, ORs were adjusted by energy intake. b For smoking and fruit and vegetable intake, odds ratios were calculated by a conditional logistic regression model adjusted for age at first visit and family history of gastric cancer, smoking status, drinking habit (alcohol drinking (ethanol (g/day)), energy-adjusted fruit and vegetable intake, energy intake, H. pylori infection, gastritis atrophy, and referral pattern. For H. pylori infection, gastric atrophy, and ABCD classification, odds ratios were calculated by a conditional logistic regression model adjusted for age at first visit and family history of gastric cancer, smoking status, drinking habit, energy-adjusted fruit and vegetable intake, energy intake, and referral pattern. c Subjects with unknown status are excluded from each analysis.
Table
Table 3. Associations with Asian GWAS-identified susceptibility polymorphism in stomach cancer risk (derivation study).
Table 3. Associations with Asian GWAS-identified susceptibility polymorphism in stomach cancer risk (derivation study).
ReferenceChromosomePositionGenes in/near RegionSNPGenotype
Risk/Non-Risk Alleles		Derivation Study
(Case/Control = 696/1372)
Risk Allele Frequency in ControlsOR a (95%CI), per Allele
p
Abnet CC, et al. [27]1q22155192276MUC1rs4072037G/A0.824 1.35(1.13–1.61)
1.07 × 10−3
Abnet CC, et al. [27]10q2394306584PLCE1rs2274223G/A0.744 1.09(0.94–1.26)
2.62 × 10−1
Hu N, et al. [26]5p13.140790449PRKAA1rs10074991G/A0.444 1.18(1.04–1.35)
1.34 × 10−2
Hu N, et al. [26]6p21.141037763UNC5CLrs2294693C/T0.234 1.16(1.00–1.35)
5.19 × 10−2
Jin G et al. [29]6p21.140568389LRFN2rs2494938G/A0.683 0.98(0.86–1.13)
8.02 × 10−1
Jin G et al. [29]7p15.321544470DNAH11rs2285947G/A0.331 1.09(0.95–1.25)
2.28 × 10−1
Sakamoto H, et al. [21]8q24.3142680513PSCArs2294008T/C0.619 1.42(1.23–1.63)
1.23 × 10−6
Shi Y, et al. [28]3q13.31114643917ZBTB20rs9841504C/G0.800 1.04(0.88–1.23)
6.67 × 10−1
Shi Y, et al. [28]5p13.140791782PRKAA1rs13361707 T/C0.445 1.18(1.03–1.35)
1.45 × 10−2
Wang Z, et al. [30]1q22155515236ASH1Lrs80142782T/C0.965 1.61(1.07–2.43)
2.27 × 10−2
Wang Z, et al. [30]5q14.389607147NArs7712641T/C0.388 1.06(0.93–1.21)
3.94 × 10−1
Tanikawa C, et al. [31]9q34.2133251249ABOrs7849280G/A0.235 1.39(1.20–1.61)
1.13 × 10−5
Tanikawa C, et al. [31]12q24.11-12111335541CUX2rs6490061C/T0.683 1.05(0.91–1.21)
4.98 × 10−1
Tanikawa C, et al. [31]20q11.2131411284DEFB121rs2376549C/T0.268 1.12(0.96–1.29)
1.45 × 10−1
OR, odds ratio; CI, confidence interval. a ORs were adjusted for age and sex.
Table
Table 4. Assessment of performance of the prediction model.
Table 4. Assessment of performance of the prediction model.
Risk FactorsDerivation Study b Validation Study c
Area under the curve (95% CI) a Std. Err. Std. Err.
Genetic factors
rs22940080.6063(0.5816–0.631)0.01260.5800(0.5519–0.608)0.0143
rs78492800.5997(0.5751–0.624)0.01250.5412(0.5128–0.570)0.0145
rs40720370.5889(0.5643–0.614)0.01260.5537(0.5254–0.582)0.0144
3 SNPs (rs2294008 rs7849280 rs4072037,
Genetic Model)		0.6287(0.6039–0.653)0.01260.5673(0.5391–0.596)0.0144
Environmental Factors
Smoking 0.6157(0.5912–0.640)0.01250.5948(0.5669–0.623)0.0142
Alcohol consumption0.5751(0.5503–0.600)0.01270.5484(0.5200–0.577)0.0145
Fruit and vegetable intake0.5822(0.5577–0.607)0.01250.5697(0.5415–0.598)0.0144
ABCD classification0.7354(0.7135–0.757)0.01120.7885(0.7662–0.811)0.0114
4 environmental factors (Smoking + Fruit and Vegetable intake + Alcohol Intake + ABCD Classification, Environmental Model)0.7531(0.7314–0.775)0.01110.7925(0.7705–0.815)0.0112
Inclusive Model
(3 SNPs and 4 environmental factors)		0.7677(0.7465–0.789)0.01080.7823(0.7694–0.814)0.0115
p-values for comparing genetic, environmental, and inclusive models4.74 × 10−53 5.44 × 10−64
p-values for comparing genetic and inclusive models7.35 × 10−28 3.96 × 10−56
p-values for comparing environmental and inclusive models4.64 × 10−4 7.17 × 10−3
a Age at first visit is included in each model. b Range of standard error of AUC is from 0.0108 to 0.0127. c Range of standard errors of AUC is from 0.0112 to 0.0145.
Table
Table 5. Calibration in the deviation and validation study.
Table 5. Calibration in the deviation and validation study.
Calibration of the Inclusive ModelDerivation StudyValidation Study
Hosmer–Lemeshow test (hl) (p-value)9.952(0.445)15.454(0.116)
Calibration slope (95%CI)0.924(0.8390–1.008)0.923(0.8296–1.016)
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Ishikura, N.; Ito, H.; Oze, I.; Koyanagi, Y.N.; Kasugai, Y.; Taniyama, Y.; Kawakatsu, Y.; Tanaka, T.; Ito, S.; Tajika, M.; et al. Risk Prediction for Gastric Cancer Using GWAS-Identifie Polymorphisms, Helicobacter pylori Infection and Lifestyle-Related Risk Factors in a Japanese Population. Cancers 2021, 13, 5525. https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13215525

AMA Style

Ishikura N, Ito H, Oze I, Koyanagi YN, Kasugai Y, Taniyama Y, Kawakatsu Y, Tanaka T, Ito S, Tajika M, et al. Risk Prediction for Gastric Cancer Using GWAS-Identifie Polymorphisms, Helicobacter pylori Infection and Lifestyle-Related Risk Factors in a Japanese Population. Cancers. 2021; 13(21):5525. https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13215525

Chicago/Turabian Style

Ishikura, Naoyo, Hidemi Ito, Isao Oze, Yuriko N. Koyanagi, Yumiko Kasugai, Yukari Taniyama, Yukino Kawakatsu, Tsutomu Tanaka, Seiji Ito, Masahiro Tajika, and et al. 2021. "Risk Prediction for Gastric Cancer Using GWAS-Identifie Polymorphisms, Helicobacter pylori Infection and Lifestyle-Related Risk Factors in a Japanese Population" Cancers 13, no. 21: 5525. https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13215525

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