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ABSTRACT The human gut hosts a diverse community of bacteria referred to as the gut microbiome. We investigated the association between the relative abundance of gastric microbiota and

gastric cancer (GC) risk in a Korean population. The study participants included 268 GC patients and 288 controls. DNA was extracted from gastric biopsies, and 16S rRNA gene analysis was

performed. Unconditional logistic regression models were used to observe the associations. Of the participants, those who had the highest level (highest tertile) of relative _Helicobacter

pylori_ and _Propionibacterium acnes_ abundances showed a significantly higher risk for GC after adjusting for potential confounding variables (odds ratio (OR) = 1.86, 95% confidence

interval (CI) = 1.17–2.97, p for trend = 0.017 and OR = 4.77, 95% CI = 2.94–7.74, p for trend <0.001, respectively). Subjects who carried _Prevotella copri_ had a significantly higher

risk of GC than noncarriers (OR = 2.54, 95% CI = 1.42–4.55, p for trend = 0.002). There was a lower risk of GC in subjects carrying _Lactococcus lactis_ than in noncarriers (OR = 0.21, 95%

CI = 0.10–0.44, p for trend <0.001). _H. pylori, P. acnes_ and _P. copri_ are strong risk factors, whereas _L. lactis_ is a protective factor, for GC development in Koreans. Further

microbiome studies are warranted to verify the findings of the current study. SIMILAR CONTENT BEING VIEWED BY OTHERS MICROBIAL COMPOSITION OF GASTRIC LESIONS: DIFFERENCES BASED ON

_HELICOBACTER PYLORI_ VIRULENCE PROFILE Article Open access 21 November 2024 META-ANALYSIS OF MUCOSAL MICROBIOTA REVEALS UNIVERSAL MICROBIAL SIGNATURES AND DYSBIOSIS IN GASTRIC

CARCINOGENESIS Article Open access 09 June 2022 DYSBIOTIC CHANGE IN GASTRIC MICROBIOME AND ITS FUNCTIONAL IMPLICATION IN GASTRIC CARCINOGENESIS Article Open access 11 March 2022 INTRODUCTION

Gastric cancer (GC) ranks as the fifth leading cancer type, and it has been identified as one of the main causes of cancer-related deaths in the world1. The incidence of GC in eastern Asia,

including Korea, is the highest worldwide, which is over 4 times higher than the rates in Western Europe2. It has been reported that the age-adjusted incidence rate of GC was 33.8 per

100,000 in Korea3. According to a prediction of cancer incidence and mortality in Korea, GC accounts for a remarkable proportion of the overall cancer burden because it is the second most

common type of cancer among Koreans4. Recent studies about the human microbiome demonstrate a surge in interest in the context of disease, particularly in gastrointestinal cancers5. It is a

known fact that there are various types of bacteria in different body sites, which are colloquially referred to as normal flora. This microbiota has the potential to maintain human health by

interacting with the human body and can be considered pathological for the development of certain diseases5. Due to the complex and dynamic nature of the human gastrointestinal microbiota,

it is recently considered as a metabolically active organ and the complex nature of it evidently regulates gastrointestinal homeostasis by interacting with immune cells6. The normal flora in

the gastrointestinal tract supports several processes, including the host mucosal immune response, energy metabolism, pathogen elimination, and cancer development7. It is widely implicated

that human gut bacteria play a crucial role in the etiology of gastrointestinal cancers, particularly GC due to dysbiosis8. Dysbiosis is a condition in which there is an imbalance in the

gastrointestinal microbiota, which consequently leads to several pathological conditions, specifically GC. Furthermore, gastric microbial community profiling revealed that dysbiosis of gut

microbiota is associated with GC or precancerous lesions9. The relationship between particular microbial pathogens and carcinogenesis has been the subject of exploration in the context of

systems epidemiology. A considerable number of studies have focused on individual pathogens, such as _Helicobacter pylori (H. pylori)_, and their ability to initiate disease conditions, such

as gastritis or GC10. Of the numerous risk factors associated with GC occurrence, _H. pylori_ infection plays a pivotal role11. It is an established fact that _H. pylori_ infection is

widespread in East Asia, which is associated with GC development12. Thus, it has been extensively suggested that reduction in chronic _H. pylori_ infection is a useful strategy for

preventing GC. Notably, the identification of specific microbial species that are associated with various disease conditions, particularly cancers pertaining to the gastrointestinal tract,

has been achieved with the expansion of advance sequencing technologies. It has been suggested that colonization of non-_H. pylori_ bacteria in the stomach can also stimulate GC risk13.

Thus, understanding how dysbiosis influences host metabolic reactions and inflammatory responses is critical to defining the roles specific components of the microbiota play in

carcinogenesis. However, there is a paucity of data regarding the association between the gastric microbiota and GC. As it is well documented that the composition of the microbiota shapes

immune responses at local and systemic levels, as well as inflammatory signaling related to GC development, it is necessary to investigate the gastric microbiota-associated alterations that

may influence GC development. Moreover, it is necessary to explore the evidence related to this association based on epidemiological studies because various experimental approaches have

already been employed to elucidate the microbiota profiles of GC patients14. Therefore, in the present case-control study, we aimed to investigate the association between the relative

abundance of the gastric microbiota components and the risk of GC in a Korean population. RESULTS GENERAL CHARACTERISTICS OF THE STUDY POPULATION Table 1 presents the general characteristics

of the study participants with and without GC. The proportion of current smokers was higher in the patient group (29.1%) than in the control group (17.7%), and the patients were more likely

to have a family history of GC (p = 0.003). The patients engaged in exercise less regularly (p < 0.001), were less educated (p < 0.001), and exhibited lower employment rates (p = 

0.037) and lower monthly incomes (p < 0.001) than the controls. The proportion of _H. pylori_ infection among the patients (99.6%) was higher than among the controls (93.4%). The patients

had a higher energy intake than the controls (p < 0.001). The Shannon index, which represents the alpha diversity, was significantly higher in the controls than in the patients (p < 

0.001). COMPARISON OF RELATIVE ABUNDANCE AND DIVERSITY INDICES Table 2 presents the mean relative abundance comparison results between the patients with GC and controls. We found that

patients with GC had a higher relative abundance of _Helicobacteraceae, Propionibacteriaceae_, and _Prevotellaceae_ than the healthy subjects at the family level (p adjusted for false

discovery rate (FDR) = 0.025, <0.001, 0.013, respectively). At the genus level, the relative abundances of _Helicobacter, Propionibacterium_, and _Prevotella_ were higher in the patients

than in the controls (p adjusted for FDR = 0.025, <0.001, 0.013, respectively) whereas the relative abundance of _Lactococcus_ was higher in the controls than in the patients (p adjusted

for FDR < 0.001). Regarding the species level, we found that the patients had higher relative abundances of _H. pylori, Propionibacterium acnes_ (_P. acnes_), and _Prevotella copri_ (_P.

copri_) than the controls (p adjusted for FDR = 0.035, 0.002, and 0.049, respectively), while the relative abundance of _Lactococcus lactis_ (_L. lactis_) was higher in the healthy controls

than in the patients (p adjusted for FDR = 0.003). Figure 1A presents a box plot for the Shannon index for the entire study population. We observed that the Shannon index was significantly

higher in the controls than in the patients (p < 0.001) in the entire study population. Figure 1B shows the box plots for the Shannon index by sex. We found that the Shannon index was

significantly higher in controls than in male or female patients (p = 0.013 and p < 0.001, respectively). Figure 2 represents a principal coordinates analyses (PCoA) plot of the

Bray-Curtis distance based on the operational taxonomic units (OTU) relative abundance table at the species level. The blue triangles represent the controls, while the red dots represent the

patient. The blue and red ellipses represent cases in which 95% of the data belong to the controls and patients at a 95% significance level. The 2-D plot of the first two principal

coordinates shows a marked divergence between the GC patients and the healthy controls. The total diversity captured by the first two principal coordinates was 26.4%. The microbiota

composition of the patients with GC was significantly different from that of the healthy controls (analysis of similarity (ANOSIM) R = 0.006, p = 0.030) according to the Bray-Curtis

dissimilarity measure. ASSOCIATION OF CANDIDATE SPECIES WITH GC RISK AND EVALUATION OF THEIR ABUNDANCE FOR GC DIAGNOSIS Table 3 shows the association between the relative abundance of

candidate bacterial species and GC risk. The subjects, who had a higher relative abundance of _H. pylori_ showed a significantly higher risk of GC in model II than the subjects who had a

lower relative abundance (odd ratio (OR) = 1.86, 95% confidence interval (CI) = 1.17–2.97, p-trend = 0.017). The females who had a higher relative abundance of _H. pylori_ showed a

significantly higher risk of GC than the females who had a lower relative abundance (OR = 3.36, 95% CI = 1.41–8.00, p-trend = 0.008). A positive association between the relative abundance of

_H. pylori_ and GC was observed in males, although the results were not significant. Those who had a higher relative abundance of _P. acnes_ showed a significantly higher risk for GC than

those in the lowest relative abundance group among the entire study population (OR = 4.48, 95% CI = 2.79–7.21, p-trend ≤ 0.001), in males (OR = 3.03, 95% CI = 1.62–5.66, p-trend ≤ 0.001) and

females (OR = 8.19, 95% CI = 3.41–19.65, p-trend ≤ 0.001). Regarding _P. copri_, those who carried this species showed a significantly higher risk of GC than the noncarriers among the

entire population (OR = 2.39, 95% CI = 1.36–4.19) and in males (OR = 2.78, 95% CI = 1.32–5.85). The subjects who carried _L. lactis_ showed a significantly lower risk of GC than the

noncarriers among the entire population (OR = 0.19, 95% CI = 0.10–0.39), in males (OR = 0.22, 95% CI = 0.08–0.57) and in females (OR = 0.18, 95% CI = 0.05–0.60) (Table 3). Based on principal

component analysis (PCA), two linear combinations of four species were obtained using Eigen value greater than 1.00. _H. pylori_ and _P. acnes_ were identified as dominant bacterial species

in components 1 and 2, respectively, based on the principal component loadings. Approximately 63.0% of the variation was explained by the first two principal components. According to the

linear discriminant analysis, the coefficients for the linear discriminants for PC1 (_H. pylori_ dominant) and PC2 (_P. acnes_ dominant) were −0.07 and 0.97, respectively. We investigated

whether the combination of these four bacterial species could demonstrate better predictive ability using a receiver operating characteristic (ROC) curve and area under curve (AUC) analyses.

An analysis using two linear combinations of four bacterial species showed 79.7% sensitivity and 67.1% specificity. The AUC was 77.7% indicating that there is a 77.7% chance that the model

will be able to distinguish between positive and negative classes (Supplementary Fig. S1). The best cutoff point was chosen as 0.492 in order to find a balance between sensitivity and

specificity. DISCUSSION In this case-control study involving 556 participants (268 patients and 288 controls), we observed that the relative abundances of the _H. pylori_, _P. acnes_ and _P.

copri_ species were significantly higher in the patients than in the controls, whereas the relative abundance of _L. lactis_ was significantly higher in the controls than in the patients.

Generally, the subjects who had a high relative abundance of the _H. pylori_, _P. acnes_ and _P. copri_ species showed a significantly higher risk of GC. In contrast, those who had a high

relative abundance of _L. lactis_ showed a significantly lower risk of GC. A significantly higher Shannon index was observed in the controls than in the patients. The ROC and AUC analysis

results suggest that the identified four candidate bacterial species will be clinically useful for the identification of GC in Koreans. _H. pylori_ infection is the strongest single risk

factor for GC, specifically in countries where _H. pylori_ infection is endemic15. Reduced gastric acidity caused by chronic _H. pylori_ infection lowers nutrient availability and local

innate immunity responses16. Corpus-dominant infection leads to gastric mucosal atrophy, with an increase in gastric pH due to the loss of acid-producing parietal cells17. Due to the high

relative abundance of _H. pylori_, our data represents a unique bacterial profile of the Korean population by virtue of the fact that the Korean people have been exposed to a similar diet

for a long period of time18. In contrast, a study suggested that _H. pylori_ colonization or stomach anatomic sites does not influence the gastric microbiota composition, although it

differed between paired nonmalignant and tumor tissues19. In our study population, we observed that _H. pylori_ had the highest mean relative abundance compared with other bacterial species,

specifically in GC patients. The results were similar at the family (_Helicobacteraceae_) and genus (_Helicobacter_) levels. A study that focused on the molecular characterization of the

human stomach microbiota in GC patients concluded that _H. pylori_ is the dominant member of the nonmalignant gastric tissue microbiota in many GC patients20. Interestingly, a study

conducted in Colombia revealed that the gastric microbiome composition is considerably different between people but observed a significant correlation with town of origin, although a

significant correlation between the _H. pylori_ phylogeographic population and microbial composition has not been identified21. Thus, the identification of the biological role of _H. pylori_

needs to be investigated in clinical practice. The critical role of _H. pylori_ in GC pathogenesis has been well documented. The carcinogenic potential of _H. pylori_ can be unraveled due

to the effect from two _H. pylori_ related virulence factors namely, vacuolating cytotoxin A (VacA) and cytotoxin associated gene A (CagA)22. _H. pylori_ evasion can be stimulated because of

the immunosuppressive activities of VacA which eventually leads to enhance gastric tumor survival15. The protein CagA can enter gastric epithelial cells for undergoing phophorylation15,

leading to structural changes of cells, including cell scattering, elongation22, and resistance to apoptosis23. Because of changes in physiological and immunological environments in stomach

due to the aforementioned _H. pylori_ related pathological issues, composition of the gastric microbiota can be altered resulting in a proinflammatory condition15. Studies showed a strong

association between proinflammatory cytokines polymorphisms and an increased risk of developing _H. pylori_-associated GC24,25,26. In addition to the carcinogenic role of _H. pylori_,

mucosal atrophy plays a critical role in GC pathogenesis23. Evidence from our study showed that a higher relative abundance of _H. pylori_ increases the risk of GC. Although it has been

widely accepted that there is no direct influence of _H. pylori_ to the adults’ gastric microbiota composition, few studies have noted that there is an effect owing to the fact that those

who carry _H. pylori_ are feasible to have higher abundance of _Spirochetes_, _Acidobacteria_, and non-_Helicobacter Proteobacteria_ and have comparatively lower abundances of

_Actinobacteria_, _Bacteroidetes_, and _Fermicutes_ phyla than uninfected adults27,28. Our analysis supports the theory that a higher relative abundance of _H. pylori_ increases the risk of

GC in males although the results are not statistically significant. This finding may be due to our limited sample size, although we had a relatively large overall sample. The Shannon index

box plot shows that the alpha diversity was significantly higher in the controls than in the GC patients in our study sample. A similar bacterial diversity has been noted in a study in which

there is a higher Shannon index in healthy subjects than in GC patients29. Such a change in bacterial composition may be the predominant causes of gastric atrophy resulting in GC even

though the number of _H. pylori_ bacteria decreases due to atrophy28. Additionally, based on our PCoA plot, the bacterial composition at the species level of our study was markedly divergent

between the GC patients and control subjects. This finding also resembles the finding observed by Li, T. H. _et al_.29, indicating that there is a clear divergence between GC patients and

healthy subjects in PCoA analyses. Regarding _P. acnes_, our study found that individuals with a high relative abundance of _P. acnes_ showed an increased risk of GC. A study based on

comparative microbial community profiling of human stomach biopsies, found that overabundance of _P. acnes_ is a cause of lymphocytic gastritis (LyG)30. Our study findings also show the mean

relative abundance of _P. acnes_ is higher in GC patients than in the controls. The same results were found for both the family (_Propionibacteriaceae_) and genus (_Propionibacterium_)

levels. It is notable that _P. acnes_, a classic skin bacterium that causes acne, has been recently identified as a gastric microbiota31. Furthermore, LyG caused by _P. acnes_ can enhance GC

development by producing proinflammatory cytokines such as IL 1530. It is important to mention that the gut brain skin hypothesis indicates there is a complex interrelationship between acne

and gut dysfunction mediated by the brain which has been recently validated by the microbiome studies32. This is supported by the fact that frequent associations of both anxiety and

depression and gastrointestinal distress occur with the occurrence of acne. Such condition can be a causative factor for releasing neuropeptides from the enteroendocrine cells due to the

production of neurotransmitters, such as serotonin, norepinephrine and acetylcholine by normal flora. These chemicals can trigger both intestinal and systemic inflammation which eventually

leads to GC development by increasing the gut permeability that allow for cross talk between the gut and skin32. Recent research in humans found that the localized and systemic diseases,

including periodontitis, bacterial vaginosis, rheumatoid arthritis, metabolic disorders, and low grade systemic inflammation can be caused by the overabundance of _Prevotella_ species at

mucosal sites33. _In vitro_ study suggested that _Prevotella_ has remarkable capability in driving T helper type 17 (Th17) immune responses which evidently propose the association between

increased _Prevotella_ abundance and augmented Th1733. Further, the ability of _Prevotella_ in producing redox proteins with an increased resistance to the host has also been proposed34.

Based on the current study findings, it was observed that the subjects who carried _P. copri_ had a significantly higher risk of GC than the subjects who did not carry _P. copri_.

Additionally, there was significantly higher mean _P. copri_ abundance in the GC patients than in the controls. It has been strongly suggested that _P. copri_ induces inflammatory conditions

in the human body, leading to the development of several types of diseases, including GC34. According to previous study on animal models, it has been shown that the overall bacterial

composition can be affected by long term _H. pylori_ infection35. A relative abundance of the _Prevotella_ genera was found in studies conducted with patients without _H. pylori_

infection36. Thus, _H. pylori_-induced changes in gastric microflora can be attributed to various factors and can lead to gastric atrophy, increased gastric pH, and the colonization of the

stomach by transient bacteria35. However, the role of _P. copri_ in GC development needs to be further investigated. _L. lactis_ was observed as a beneficial bacterium in the current study

because the subjects who carried _L. lactis_ had a lower GC risk. A study has revealed that there is a strong antiproliferative activity of the cytoplasmic fraction of _L. lactis_ upon human

colon cancer cells37. Furthermore, a study investigating the antiproliferative effects of the cytoplasmic fraction of _L. lactis_ on a human stomach cancer cell line revealed that there is

an inhibitory effect on cell proliferation with _L. lactis_ treatment in a time and dose dependent manner38. _L. lactis_ caused G0/G1 cell cycle arrest, which was associated with an increase

in p53 and p21 expression, a reduction in cyclin D1 expression, and retinoblastoma protein phosphorylation, thereby inducing apoptosis. Additionally, it has been noted that the _L. lactis_

bacterium has a probiotic effect in the human gut and can result in beneficial effects for human gastrointestinal health, including the prevention of gastrointestinal cancers38.

Interestingly, we observed that the mean relative abundance of _L. lactis_ was higher in healthy controls than in the GC patients in our study population. We observed that two linear

combinations (_H. pylori_ and _P. acnes_ dominant) of the four identified candidate bacterial species can be considered predictive of GC, therefore, representing a potential diagnostic

marker. Together, our data indicated that _H. pylori_ and _P. acnes_ were highly abundant in the GC patients and positively identified GC with 79.7% sensitivity. It is evident that the

abundance of _H. pylori_ becomes lower due to the succession of microbial species as GC locally advances14. In contrast, our data indicated that GC patients have significantly high

abundances of _H. pylori_. A possible explanation is that we collected the biopsy samples from patients with early gastric cancer in which bacterial succession had not progressed. A study

conducted in Taiwan concluded that regardless of the biological roles of _Clostridium_, _Fusobacterium_ and _Lactobacillus_ in oncogenesis, the overabundance of these microbes serves as a

diagnostic tool for GC14. Thus, it can be suggested that if there is a bacteria species that can promote the GC occurrence, the eradication of this bacteria is useful for decreasing GC

incidence. The human gut microbiome plays a critical role in gastrointestinal cancers39. A report on the next steps in studying the human microbiome and health in prospective studies pointed

out that the importance of continuing and expanding of the recent microbiome research40. Despite the utilization of optimized techniques for sample collection, processing and storage,

supplementary methodological approach is required specifically in the epidemiological context due to the inherent limitations of epidemiological studies. As the potential limitations in

case-control studies of the microbiome and cancer, changes of the composition of the microbiome due to dietary factors, selection and recall bias have been emerged. It has been recommended

that biorepositories for human samples establish a similar collection method to serve as a basis for nested case-control studies. In addition, conducting short interventions in human and

animal studies can identify the important effects of diet on the microbiome, particularly the fecal microbiome40. Furthermore, managing optimal storage conditions soon after a sample is

collected is necessary to reduce the bias arising from the quick changes in the genetic components of the microbes41,42. Our study has both strengths and limitations. A major strength of our

study is that the sample size was relatively large with 268 GC patients and 288 healthy controls, which provides sufficient statistical power to detect the relevant associations between the

gastric microbiota and GC risk. Additionally, several possible confounding variables were taken into consideration that are possible risk factors for GC development, including age, smoking,

family history of GC, regular exercise, education, occupation, income and total energy intake throughout the analysis. However, our study has potential limitations. In general, the presence

of bias associated with a hospital based case-control study, including selection bias and recall bias should be raised. As this is not a prospective study, the associations between

microbiome and GC can occur without a causal relation to GC because patients with early GC have altered microbiomes because of atrophy progression. Additionally, we only measured a single

sample for our microbial measurements, and it has been shown that microbiome measurements at multiple time points could result in more precise exposure estimates43. However, it is important

to emphasize that there are ethical issues in repeating biopsies in those with normal gastric histology and healthy subjects. In conclusion, _H. pylori, P. acnes_ and _P. copri_ are strong

risk factors, whereas _L. lactis_ is a protective factor, for GC development in Koreans. The identified four candidate bacterial species will be clinically useful for the identification of

GC in Koreans. Further microbiome studies, such as a prospective study that has the capability to infer causality, are warranted to confirm the findings of the current study. Furthermore,

the study can be expanded by evaluating the impact of diet on the bacterial composition of the gastric mucosa and by including other races and ethnicities, specifically other East Asian

populations, to improve the generalizability of the results. The identification of the gastric bacterial composition can also serve as a readily accessible, noninvasive biomarker for the

identification of GC risk44. Moreover, conducting GC related pathway and functional studies based on gastric microbiome data is warranted, as most cancer related pathways are yet to be

discovered45,46. MATERIALS AND METHODS STUDY SUBJECTS Participants were recruited at the National Cancer Center Hospital in Korea between March 2011 and December 2014. Individuals who had

been histologically confirmed at the Center for Gastric Cancer as having early GC within the preceding three months were included in the patient group. Early GC was defined as an invasive

carcinoma confined to the mucosa and/or submucosa, regardless of lymph node metastasis status. Patients who had been diagnosed with diabetes mellitus or had a history of cancer within the

past five years, advanced GC, or severe systemic or mental diseases, as well as women who were pregnant or breastfeeding, were excluded. The control group was selected from individuals

undergoing health-screening examinations at the Center for Cancer Prevention and Detection at the same hospital. Individuals in the control group with a history of cancer, diabetes mellitus,

gastric ulcers, and _H. pylori_ treatment were excluded. The final sample of 556 participants was composed of 268 patients and 288 controls (men, 353; women, 203). All study protocols were

conducted according to the Declaration of Helsinki principles. This study was approved by the Institutional Review Board of the National Cancer Center (IRB number: NCCNCS-11-438). Written

informed consent was obtained from all participants. DATA COLLECTION Five gastric mucosa biopsy samples were collected from each study participant following the Sydney system after endoscopy

and examination of the stomach. A biopsy sample in the greater curvature, at least 3 cm away from each tumor, was used for the metagenomics analysis. The _H. pylori_ infection status was

determined by a rapid urease test, a serological test and histological evaluation. Regarding the rapid urease test, one biopsy sample was taken from the greater curvature of the corpus. Four

biopsy samples were collected from the lesser curvature of the corpus and antrum for histological evaluation. The _H. pylori_ status was determined via Wright-Giemsa staining of the biopsy

specimens by a pathologist who specialized in GC. A current infection was defined as at least one positive test result in the rapid urease test or histological evaluation of four biopsy

sites47. Participants were asked to complete a self-administered questionnaire. Demographic, lifestyle, physical activity, and medical history data were collected from the participants.

Total energy intake was obtained from the semiquantitative food frequency questionnaire (SQFFQ), which has been previously reported as a reliable and valid questionnaire48. _H. pylori_

infection was assessed by a rapid urease test and histological evaluation. DNA EXTRACTION DNA was extracted from the biopsy samples using the MagAttract DNA Blood M48 kit (Qiagen, Hilden,

Germany) and BioRobot M48 automatic extraction equipment (Qiagen), according to the manufacturers’ instructions. METAGENOMIC 16S RRNA GENE SEQUENCING Input gDNA (12.5 ng) was amplified with

16S rRNA gene V3-V4 primers, and a subsequent limited cycle amplification step was performed to add multiplexing indices and Illumina sequencing adapters. The final products were normalized

and pooled using PicoGreen, and the library sizes were verified using the LabChip GX HT DNA High Sensitivity Kit (PerkinElmer, Massachusetts, USA). Then, we sequenced using the MiSeq™

platform (Illumina, San Diego, USA). Each sequenced sample was prepared according to the Illumina 16S rRNA gene Metagenomic Sequencing Library protocols. DNA quantification and quality were

measured by PicoGreen and Nanodrop analyses, respectively. The 16S rRNA genes were amplified using 16S rRNA gene V3-V4 primers for the 288 control samples and the 268 GC patient samples. The

primer sequences are as follows: 16S rRNA gene V3-V4 primer. 16S rRNA gene Amplicon PCR Forward Primer. 5′ TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG. 16S rRNA gene Amplicon PCR

Reverse Primer. 5′ GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC. Preprocessed reads from each sample were used to calculate the number of OTUs. The number of OTUs was determined

by clustering the sequences from each sample using a 97% sequence identity cut-off using quantitative insights into microbial ecology (QIIME) software (v.1.8.0). Taxonomic abundance was

counted with a National Center for Biotechnology Information (NCBI) database using a confidence threshold of 0.8 derived from the preprocessed reads for each sample. The microbial

composition was normalized using the values calculated from the taxonomic abundance count divided by the number of preprocessed reads for each sample to obtain the relative abundance.

STATISTICAL ANALYSIS To compare the demographic and lifestyle characteristics between the controls and patients, the chi-square test and Student’s _t_-test were performed for categorical

variables and continuous variables, respectively. We compared the relative abundance of the bacterial taxa (class, family, genus, and species) between patients and controls. FDR was applied

for the multiple comparison correction. The relative abundance of the candidate species was categorized into tertiles based on the relative abundance in the control group. Exceptionally, if

more than one third of the subjects has a relative abundance of zero, that bacterial species was categorized into two groups (noncarriers and carriers) based on the median distribution of

the controls. Noncarriers were defined as the subjects who had a relative abundance of zero. The group with the lowest relative abundance was used as the reference group. The ORs and 95% CIs

were estimated using unconditional logistic regression models. The median values of relative abundance in each tertile category were used as continuous variables to test for trends. The OR

estimates were calculated for the crude model (model I) and model II. Model II was adjusted for age, smoking, first-degree family history of GC, regular exercise, education, occupation,

monthly income, and total energy intake. An association analysis was performed for the male and female groups. Boxplots were drawn for the Shannon index value comparisons between the

patients and controls using the ggplot2 package. A PCoA was performed on a Bray-Curtis dissimilarity based on the OTU relative abundance table for the species level by using the R package’s

“vegan”. Sample clustering in beta diversity analysis was tested using ANOSIM. The statistical significance of the observed R was assessed by 104 permutations9. To determine whether the

candidate bacterial species can be used as diagnostic tool for GC, a ROC curve analysis was performed based on linear discriminant function. Initially, PCA was conducted to obtain two linear

combinations of four variables that explain part of the variance of the model. Then, a linear discriminant function was constructed to distinguish between patients with GC and non-GC. For

constructing the linear discriminant function, 424 observations were selected as training data set whereas 132 observations were selected as test data set. The training data set was used to

calculate the linear discriminant function by using the “MASS” package in R. The model evaluation was performed using the ROC and AUC analysis results. Statistical analyses were conducted by

using SAS version 9.4 software (SAS Inc., Cary, NC, USA) and the R platform (version 3.5.1) (The R Foundation for Statistical Computing, Vienna, Austria). ETHICAL STATEMENT This study was

approved by the Institutional Review Board of the National Cancer Center and all the methods were performed in accordance with the approved guidelines and regulations (IRB number:

NCCNCS-11-438). Written informed consent was obtained from all participants. DATA AVAILABILITY The sequence data are available https://www.ncbi.nlm.nih.gov/nuccore/KEQH0000000000. All other

data used and analyzed during the current study are available from the corresponding author by reasonable request. CHANGE HISTORY * _ 29 OCTOBER 2021 A Correction to this paper has been

published: https://doi.org/10.1038/s41598-021-00573-3 _ REFERENCES * Bray, F. _et al_. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers

in 185 countries. _CA Cancer J Clin._ 68(6), 394–424 (2018). Article  PubMed  Google Scholar  * Ferlay, J. _et al_. GLOBOCAN, Cancer incidence and mortality worldwide: IARC CancerBase No. 10

[Internet]. Lyon, France: International Agency for Research on Cancer; 2013. Available from, http://globocan.iarc.fr, Accessed February 2019. * Jung, K.-W., Won, Y.-J., Kong, H.-J. &

Lee, E. S. Cancer statistics in Korea: incidence, mortality, survival, and prevalence in 2015. _Cancer Res Treat._ 50(2), 303 (2018). Article  PubMed  PubMed Central  Google Scholar  * Jung,

K.-W., Won, Y.-J., Kong, H.-J. & Lee, E. S. Prediction of Cancer Incidence and Mortality in Korea, 2018. _Cancer Res Treat._ 50(2), 317 (2018). Article  PubMed  PubMed Central  Google

Scholar  * Ashton, J. J., Beattie, R. M., Ennis, S. & Cleary, D. W. Analysis and Interpretation of the Human Microbiome. _Inflamm Bowel Dis._ 22(7), 1713–22 (2016). Article  PubMed 

Google Scholar  * Martha, A. _et al_. Microbiota-dependent crosstalk between macrophages and ILC3 promotes intestinal homeostasis. _Science_. 343 (2014). * Garrett, W. S. Cancer and the

microbiota. _Science._ 348, 80–86 (2015). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Thursby, E. & Juge, N. Introduction to the human gut microbiota. _Biochem J._

474(11), 1823–36 (2017). Article  CAS  PubMed  Google Scholar  * Ferreira, R. M. _et al_. Gastric microbial community profiling reveals a dysbiotic cancer-associated microbiota. _Gut._ 67,

226–236 (2018). Article  CAS  PubMed  Google Scholar  * Noto, J. M. & Peek, R. M. Jr. The gastric microbiome, its interaction with Helicobacter pylori, and its potential role in the

progression to stomach cancer. _PLoS Pathogens._ 13(10), e1006573 (2017). Article  PubMed  PubMed Central  CAS  Google Scholar  * Jemal, A. _et al_. Global cancer statistics. _CA Cancer J

Clin._ 61, 69–90 (2011). Article  PubMed  Google Scholar  * Fock, K. M. _et al_. Second asia–pacific consensus guidelines for helicobacter pylori infection. _J Gastroenterol Hepatol._ 24,

1587–1600 (2009). Article  PubMed  Google Scholar  * Li, J. & Perez Perez, G. I. Is there a role for the non-Helicobacter pylori bacteria in the risk of developing gastric cancer? _Int J

Mol Sci._ 19(5), 1353 (2018). Article  ADS  PubMed Central  CAS  Google Scholar  * Hsieh, Y. Y. _et al_. Increased abundance of Clostridium and Fusobacterium in gastric microbiota of

patients with gastric cancer in Taiwan. _Sci Rep._ 8(1), 158 (2018). Article  ADS  PubMed  PubMed Central  CAS  Google Scholar  * Brawner, K. M., Morrow, C. D. & Smith, P. D. Gastric

microbiome and gastric cancer. _Cancer J._ 20(3), 211–6 (2014). Article  CAS  PubMed  PubMed Central  Google Scholar  * Heimesaat, M. M. _et al_. Helicaobacter pylori indicedgastric

immunopathology is associated with distinct microbiota changes in the large intestines of long-term infected Mongolian gerbils. _PLoS One_. 9 (2014). * Gao, J.-J. _et al_. Association

Between Gut Microbiota and Helicobacter pylori-Related Gastric Lesions in a High-Risk Population of Gastric Cancer. _Front Cell Infect Microbiol._ 8, 202 (2018). Article  PubMed  PubMed

Central  CAS  Google Scholar  * Eun, C. S. _et al_. Differences in Gastric Mucosal Microbiota Profiling in Patients with Chronic Gastritis, Intestinal Metaplasia, and Gastric Cancer Using

Pyrosequencing Methods. _Helicobacter._ 19, 407–416 (2014). Article  CAS  PubMed  Google Scholar  * Kamangar, F. _et al_. Helicobacter pylori and oesophageal and gastric cancers in a

prospective study in China. _Br J Cancer._ 96(1), 172 (2007). Article  CAS  PubMed  Google Scholar  * Yu, G. _et al_. Molecular characterization of the human stomach microbiota in gastric

cancer patients. _Front Cell Infect Microbiol._ 7, 302 (2017). Article  PubMed  PubMed Central  CAS  Google Scholar  * Yang, I. _et al_. Different gastric microbiota compositions in two

human populations with high and low gastric cancer risk in Colombia. _Sci Rep._ 6, 18594 (2016). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Rhead, J. L., Letley, D. P.

& Mohammadi, M. A new Helicobacter pylori vacuolating cytotoxin determinant, the intermediate region, is associated with gastric cancer. _Gastroenterol._ 133, 926–936 (2007). Article 

CAS  Google Scholar  * Ohnishi, N., Yuasa, H. & Tanaka, S. Transgenic expression of Helicobacter pylori CagA induces gastrointestinal and hematopoietic neoplasms in mouse. _Proc Natl

Acad Sci._ 105, 1003–1008 (2008). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * El-Omar, E. M. _et al_. Interleukin-1 polymorphisms associated with increased risk of gastric

cancer. _Nature._ 404(6776), 398 (2000). Article  ADS  CAS  PubMed  Google Scholar  * El-Omar, E. M. The importance of interleukin 1beta in Helicobacter pylori associated disease. _Gut._

48(6), 743–7 (2001). Article  CAS  PubMed  PubMed Central  Google Scholar  * Figueiredo, C. _et al_. Helicobacter pylori and interleukin 1 genotyping: an opportunity to identify high-risk

individuals for gastric carcinoma. _J Natl Cancer Inst._ 94(22), 1680–7 (2002). Article  CAS  PubMed  Google Scholar  * Lofgren, J. L., Whary, M. T. & Ge, Z. Lack of commensal flora in

Helicobacter pylori-infected INS-GAS mice reduces gastritis and delays intraepithelial neoplasia. _Gastroenterol._ 140, 210–220 (2011). Article  Google Scholar  * Tan, M. P., Kaparakis, M.

& Galic, M. Chronic Helicobacter pylori infection does not significantly alter the microbiota of the murine stomach. _Appl Environ Microbiol._ 73, 1010–1013 (2007). Article  ADS  CAS 

PubMed  Google Scholar  * Li, T. H. _et al_. Alterations in gastric microbiota after H. Pylori eradication and in different histological stages of gastric carcinogenesis. _Sci Rep._ 7, 44935

(2017). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Montalban-Arques, A. _et al_. Propionibacterium acnes overabundance and natural killer group 2 member D system

activation in corpus-dominant lymphocytic gastritis. _J Pathol._ 240(4), 425–36 (2016). Article  CAS  PubMed  PubMed Central  Google Scholar  * Monstein, H. J., Tiyejung, A. & Kraft, C.

H. Profiling of bacterial flora in gastric biopsies from patients with Helicobacter pylori‐associated gastritis and histologically normal control individuals by temperature gradient gel

electrophoresis and 16S rDNA sequence analysis. _J Med Microbiol._ 49, 817–822 (2000). Article  PubMed  Google Scholar  * Salem, I., Ramser, A., Isham, N., Ghannoum, M.A. The gut microbiome

as a major regulator of the gut-skin axis. _Front Microbiol_. 9 (2018). * Larsen, J. M. The immune response to Prevotella bacteria in chronic inflammatory disease. _Immunol._ 151(4), 363–74

(2017). Article  CAS  Google Scholar  * Wu, J. _et al_. Tongue Coating Microbiota Community and Risk Effect on Gastric Cancer. _J Cancer._ 9(21), 4039 (2018). Article  CAS  PubMed  PubMed

Central  Google Scholar  * Nardone, G. & Compare, D. The human gastric microbiota: Is it time to rethink the pathogenesis of stomach diseases? _United European Gastroenterol J._ 3(3),

255–60 (2015). Article  CAS  PubMed  PubMed Central  Google Scholar  * Li, X.-X. _et al_. Bacterial microbiota profiling in gastritis without Helicobacter pylori infection or non-steroidal

anti-inflammatory drug use. _PLoS One._ 4(11), e7985 (2009). Article  ADS  PubMed  PubMed Central  CAS  Google Scholar  * Kim, J. Y., Woo, H. J., Kim, Y.-S. & Lee, H. J. Screening for

antiproliferative effects of cellular components from lactic acid bacteria against human cancer cell lines. _Biotechnol Lett._ 24(17), 1431–6 (2002). Article  CAS  Google Scholar  * Kim, S.

Y., Kim, J. E., Lee, K. W. & Lee, H. J. Lactococcus lactis ssp. lactis inhibits the proliferation of SNU-1 human stomach cancer cells through induction of G0/G1 cell cycle arrest and

apoptosis via p53 and p21 expression. _Ann N Y Acad Sci._ 1171, 270–5 (2009). Article  ADS  PubMed  Google Scholar  * Meng, C., Bai, C., Brown, T.D., Hood, L., Tian, Q. Human gut microbiota

and gastrointestinal cancer. _Genomics Proteomics Bioinformatics_ (2018). * Sinha, R. _et al_. Next steps in studying the human microbiome and health in prospective studies, Bethesda, MD,

May 16–17, 2017. _BMC.Microbiome_ (2018). * Franzosa, E. A. _et al_. Relating the metatranscriptome and metagenome of the human gut. _Proc Natl Acad Sci_. 201319284 (2014). * Ahn, J. _et

al_. Human gut microbiome and risk for colorectal cancer. _J Natl Cancer Inst._ 105(24), 1907–11 (2013). Article  CAS  PubMed  PubMed Central  Google Scholar  * Hayes, R. B. _et al_.

Association of Oral Microbiome With Risk for Incident Head and Neck Squamous Cell Cancer. _JAMA Oncol._ 4(3), 358–65 (2018). Article  PubMed  PubMed Central  Google Scholar  * Ahn, J., Chen,

C. Y. & Hayes, R. B. Oral microbiome and oral and gastrointestinal cancer risk. _Cancer Causes Control._ 23(3), 399–404 (2012). Article  PubMed  PubMed Central  Google Scholar  * Kwon,

M., Seo, S.-S., Kim, M. K., Lee, D. O. & Lim, M. C. Compositional and Functional Differences between Microbiota and Cervical Carcinogenesis as Identified by Shotgun Metagenomic

Sequencing. _Cancers._ 11(3), 309 (2019). Article  CAS  PubMed Central  Google Scholar  * Zhang, B., Xia, C., Lin, Q. & Huang, J. Identification of key pathways and transcription factors

related to Parkinson disease in genome wide. _Mol Biol Rep._ 39(12), 10881–7 (2012). Article  CAS  PubMed  Google Scholar  * Woo, H. D. _et al_. Genome-wide profiling of normal gastric

mucosa identifies Helicobacter pylori-and cancer-associated DNA methylome changes. _Int J Cancer._ 143(3), 597–609 (2018). Article  CAS  PubMed  Google Scholar  * Ahn, Y. _et al_. Validation

and reproducibility of food frequency questionnaire for Korean genome epidemiologic study. _Eur J Clin Nutr._ 61(12), 1435–41 (2007). Article  CAS  PubMed  Google Scholar  Download

references ACKNOWLEDGEMENTS This work was supported by grants from the National Cancer Center, Republic of Korea (Nos 1410260, 1810090, and 1810980). AUTHOR INFORMATION AUTHORS AND

AFFILIATIONS * Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, Goyang-si, 10408, Gyeonggi-do, South Korea Madhawa Neranjan Gunathilake *

Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, Goyang-si, 10408, Gyeonggi-do, South Korea Jeonghee Lee & Jeongseon Kim * Center for Gastric

Cancer, National Cancer Center Hospital, National Cancer Center, Goyang-si, 10408, Gyeonggi-do, South Korea Il Ju Choi & Young-Il Kim * Microbiome Division, Theragen Etex, 145

Gwanggyo-ro, Gyeongtong-gu, Suwon-si, Gyeonggi-do, 16229, South Korea Yongju Ahn & Chanhyeok Park Authors * Madhawa Neranjan Gunathilake View author publications You can also search for

this author inPubMed Google Scholar * Jeonghee Lee View author publications You can also search for this author inPubMed Google Scholar * Il Ju Choi View author publications You can also

search for this author inPubMed Google Scholar * Young-Il Kim View author publications You can also search for this author inPubMed Google Scholar * Yongju Ahn View author publications You

can also search for this author inPubMed Google Scholar * Chanhyeok Park View author publications You can also search for this author inPubMed Google Scholar * Jeongseon Kim View author

publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS J.L., M.N.G., C.P. and Y.A. conducted the formal analysis; M.N.G. wrote the original manuscript draft;

I.J.C., Y.-I.K., Y.A., and J.K. reviewed and edited the manuscript; I.J.C., Y.-I.K., J.K., J.L., C.P. and Y.A. curated the data; I.J.C., Y.-I.K. and J.K., were involved in the study

investigation; I.J.C., Y.-I.K. and J.K. helped with the methodology; J.K. acquired the funding; J.K. was the project administrator; J.K. supervised the project. CORRESPONDING AUTHOR

Correspondence to Jeongseon Kim. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral

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abundance of gastric microbiota and the risk of gastric cancer: a case-control study. _Sci Rep_ 9, 13589 (2019). https://doi.org/10.1038/s41598-019-50054-x Download citation * Received: 15

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