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Although intestinal microbial dysbiosis was confirmed to be associated with many chronic diseases and health status through complicated interaction with the host, the effect on gastric cancer was less studied. In this study, we sequenced the 16S rRNA and 18S rRNA genes of fecal bacteria and fungi, respectively, in 134 gastric cancer patients and 58 healthy controls matched by age and gender. Propensity score matching (PSM) was adopted for adjusting diet habits and lifestyle, and 44 patients and 44 healthy controls (matching population) were enrolled. Serum antibody to H. pylori and metabolites of the matching population were detected. The positive rates of antibody to H. pylori between the patients and the control group did not reach the statistical difference. LEfSe analysis indicated that bacteria were more stable than fungi when adjusting diet and lifestyle. Veillonella, Megasphaera, and Prevotella 7 genus and Streptococcus salivarius subsp. Salivarius, Bifidobacterium dentium, and Lactobacillus salivarius species in bacteria were related to the risk of gastric cancer and showed a good diagnostic value in distinguishing the patients from healthy controls. Streptococcus mitis showed a risk effect for gastric cancer; however, the effect turned into be protective after PSM. Serum L-alanine, L-threonine, and methionol were positively associated with Veillonella and Streptococcus and several fungi genus. Overall, our findings indicated that fecal microbiome constitution alteration may be associated with gastric cancer through influencing the amino acid metabolism.

How gastric cancer (GC) incidence is associated with changes in the gastric microbiome has not been firmly established. The present study therefore aims to investigate the microbial communities present within the gastric mucosa of patients with superficial gastritis (SG) or GC. Paired tumor and paracancerous samples of the gastric mucosa were collected from 18 patients being surgically treated for GC and from 32 patients with SG being treated via gastroscopy. The gastric microbiome in these samples was then profiled via 16S rRNA sequencing, with a linear discriminant analysis effect size (LEfSe) approach used to identify and compare different bacteria, and with PICRUSt used for predictive functional analyses. GC patients exhibited a distinct gastric microbiota profile from that observed in SG patients. These changes were evident in both tumor and paracancerous tissues from GC patients. Specifically, we found that 6 bacterial genera were specifically enriched in GC tissue samples relative to SG samples, while 18 genera were depleted in these same samples. Based on the differential abundance of these bacteria, we were able to calculate microbial dysbiosis index (MDI) values, which were significantly higher in GC patients than in SG patients. In addition, MDI values were negatively correlated with gastric Shannon index and were positively correlated with relative Helicobacter spp. abundance. Importantly, these MDI values were readily able to discriminate between GC and SG patient samples. Functional analysis suggested that GC patients were more likely to harbor a nitrosating microbial community. GC patients exhibited a gastric microbiome profile distinct from that observed in SG patients, with these differences being evident in both tumor and paracancerous tissues. Differences in the relative abundance of Helicobacter spp. may be the primary driver of gastric dysbiosis in GC patients.

Background Pathogens capable of impacting gastrointestinal tract tumor development are located in the oral cavity, but whether these oral bacteria are able to colonize the gastric mucosa in gastric cancer (GC) patients and whether Helicobacter pylori infection can influence this process remains to be established. Methods Microbial 16S rDNA deep sequencing was conducted to characterize bacteria present in paired gastric mucosa and tongue coating samples in 27 patients with superficial gastritis (SG) and 11 GC patients. Results While the overall composition of the gastric mucosa and tongue coating microbiomes differed substantially, certain bacteria were present in both of these communities. The co-occurrence of bacteria between the tongue coating and gastric mucosa differed significantly between SG and GC patients. Of the 15 most abundant shared oral bacteria genera (the core shared oral bacteria), which were associated with differences in microbiota composition between these tongue coating and gastric mucosa, three were enriched in the gastric mucosa of GC patients relative to SG patients, whereas, 12 were depleted in GC patient samples. Furthermore, the prevalence and relative abundance of these core shared oral bacteria in the gastric mucosa were also linked to H. pylori infection status, and the core shared oral bacteria were also associated with the overall composition of the gastric mucosal microbiome. Conclusions Helicobacter pylori infections are linked to the co-occurrence of bacteria in the oral microbiome and the gastric mucosal microbiome. Ectopic colonization of oral microbes may be a primary driver of H. pylori-induced gastric microbial dysbiosis in patients with GC.

Gastric cancer (GC) is one of the most common cancers, and the noninvasive diagnostic methods for monitoring GC are still lacking. Growing evidence shows that human microbiota has potential value for identifying digestive diseases. The present study aimed to explore the association of the tongue coating microbiota with the serum metabolic features and inflammatory cytokines in GC patients and seek a potential, noninvasive biomarker for diagnosing GC. The tongue coating microbiota was profiled by 16S rRNA and 18S rRNA genes sequencing technology in the original population with 181 GC patients and 112 healthy controls (HCs). Propensity score matching method was used to eliminate potential confounders including age, gender, and six lifestyle factors and a matching population with 66 GC patients and 66 HCs generated. Serum metabolomics profiling was performed by ultra-performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) in the matching population. Random forest model was constructed for the diagnosis of GC. Linear discriminant analysis effect size (LEfSe) revealed that the differential bacterial taxa between GC patients and HCs in the matching population were similar to that in the original population, while the differential fungal taxa between GC patients and HCs dramatically changed before and after PSM. By random forest analysis, the combination of six bacterial genera (Peptostreptococcus, Peptococcus, Porphyromonas, Megamonas, Rothia, and Fusobacterium) was the optimal predictive model to distinguish GC patients from HCs effectively, with an area under the curve (AUC) value of 0.85. The model was verified with a high predictive potential (AUC = 0.76 to 0.96). In the matching population, eighteen specific HCs-enriched bacterial genera (Porphyromonas, Parvimonas, etc.) had negative correlations with lysophospholipids metabolites, and three of them had also negative correlations with serum IL-17α. The alteration of tongue coating microbiota had a possible linkage with the inflammations and metabolome, and the tongue coating bacteria could be a potential noninvasive biomarker for diagnosing GC, which might be independent of lifestyle.

Background: Although oral hygiene and health have long been reported to be associated with increased risk of gastric cancer (GC), the direct relationship of oral microbes with the risk of GC have not been evaluated fully. We aimed to test whether tongue coating microbiome was associated with GC risk. Methods: Pyrosequencing of 16S rRNA gene of tongue coating microbiome was used in 57 newly diagnosed gastric adenocarcinomas and 80 healthy controls. Benjamini-Hochberg (BH) was applied for multiple comparison correction. Co-abundance group (CAGs) analysis was adopted. Results: We found that higher relative abundance of Firmicutes, and lower of Bacteroidetes were associated with increased risk of GC. In genus level, Streptococcus trended with a higher risk of GC, the four other genera (Neisseria, Prevotella, Prevotella7, and Porphyromonas) were found to have a decreased risk of GC. Different from overall GC and non-cardia cancer, Alloprevotella and Veillonella trended with the higher risk of cardia cancer. Finally, we analyzed the microbiota by determining CAGs and six clusters were identified. Except the Cluster 2 (mainly Streptococcus and Abiotrophia), the other clusters had an inverse association with GC. Of them, the Cluster 6 (mainly Prevotella and Prevotella7 etc) had a relatively good classification power with 0.76 of AUC. Conclusion: Microbiome in tongue coating may have potential guiding value for early detection and prevention of GC.