Identification of Gastric Microbiota Biomarker for Gastric Cancer

Gastric cancer (GC) is one of the most commonly diagnosed malignancies worldwide, posing a significant public health challenge. Recent studies have highlighted the role of an aberrant gastric microbiota in the onset and progression of GC. A distinct cluster of bacteria, including Peptostreptococcus, Streptococcus, and Parvimonas, has been associated with gastric atrophy and intestinal metaplasia (IM), which are precursor conditions to GC. However, the gastric microbiome is highly dynamic and influenced by various factors such as diet, xenobiotics, proton pump inhibitors, physiological changes, and host genetics. This study aims to identify specific gastric microbiota biomarkers that can serve as diagnostic tools for GC, providing insights into the underlying mechanisms of the disease.

The study included data from 60 patients (mean age 55 ± 13 years; 58.3% male) who presented at the First Affiliated Hospital of Nanjing Medical University from February 2017 to March 2018. The cohort comprised 17 patients with chronic gastritis (CG), 13 patients with IM, and 30 individuals with GC. The study was approved by the hospital’s Ethics Committee, and all participants provided written informed consent. Gastric biopsy samples were obtained from the gastric antrum and corpus of CG patients, while targeted biopsies were taken from patients with IM or GC.

The gut bacteria community structure was examined by sequencing the V3-V4 region of 16S rRNAs in gastric tissue samples using the Illumina MiSeq platform. The microbiota community structure was analyzed using the Quantitative Insights Into Microbial Ecology (QIIME) pipeline, with high-quality sequences processed to determine the taxonomy of representative operational taxonomic units (OTUs). The UCLUST classifier with the Greenegenes reference dataset was used for taxonomic classification. Linear discriminant analysis effect size (LEfSe) and Wilcoxon test were employed to identify taxa with significantly different relative abundance between groups. A leave-one-out cross-validation (LOOCV) model was used to validate the diagnostic value of the identified biomarkers.

The study compared the microbiota structure of the CG, IM, and GC groups in terms of alpha and beta diversity. The Chao Index and Shannon diversity index were both significantly elevated in the GC group compared to the CG and IM groups, indicating greater microbial diversity in GC patients. Beta diversity, calculated using principal coordinates analysis (PCoA), revealed significant differences in microbiota composition between GC patients and those with CG or IM.

To further analyze the diagnostic potential of the gastric microbiome, the CG and IM groups were combined into a non-GC group. LEfSe analysis identified 21 OTUs that were significantly altered in the GC group and 6 OTUs in the non-GC group. A LOOCV model was used to differentiate GC samples from non-GC samples, identifying a 19-OTU biomarker set. This set included Barnesiellaceae, Phascolarctobacterium, Bacteroides uniformis, Clostridium, Trabulsiella, Lachnospira, Roseburia, Prevotella copri, Butyricimonas, Deinococci, Prevotella, Deinococcales, Thermi, Prevotellaceae, Alcaligenaceae, Dialister, Ruminococcus, Sutterella, and Bifidobacteriaceae. Predicted values higher than 0.55 indicated a high risk for GC, while values lower than 0.55 were indicative of a low risk. The LOOCV model achieved an area under the curve (AUC) value of 89.3%, with a sensitivity of 83.33%, specificity of 90%, a false-positive rate of 10%, and a false-negative rate of 16.67%.

Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used to predict the functional potential of the gastric microbiota. The abundance of 29 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was significantly enriched in the GC samples, most of which were associated with metabolism. These included primary bile acid biosynthesis, secondary bile acid biosynthesis, sphingolipid metabolism, biosynthesis of ansamycins, butirosin and neomycin biosynthesis, cyanoamino acid metabolism, drug metabolism, flavone and flavonol biosynthesis, fructose and mannose metabolism, glycosaminoglycan degradation, glycosphingolipid biosynthesis, pentose and glucuronate interconversions, steroid hormone biosynthesis, and nucleotide metabolism. Interestingly, the abundance of folate biosynthesis and gastric acid secretion pathways was significantly decreased in the GC samples.

The study also compared its findings with previous research. LEfSe analysis has been used in other studies to identify GC-associated microbial taxa and construct diagnostic models using microbial dysbiosis indices. Another study used a sparse compositional correlation (SparCC) algorithm to perform correlation analysis, which is robust against compositional effects influenced by the diversity and sparsity of correlation in human microbiome datasets. Notably, Clostridium spp. and Prevotella spp. identified in this study were also reported in previous LEfSe analyses, while B. uniformis and P. copri were reported in studies using the SparCC algorithm. The LOOCV model used in this study offers advantages over these approaches, including the use of nearly all data points in the training model to decrease bias and the absence of random factors to ensure reproducibility. However, LOOCV is computationally expensive and time-consuming.

The signaling pathways of GC-associated microbiota are largely unknown. According to the KEGG analysis, primary and secondary bile acid biosynthesis pathways were enriched in the GC group. The bile acid receptor Takeda-G protein receptor-5 (TGR5) is overexpressed in GC cells and promotes epithelial-mesenchymal transition, a process associated with cancer progression. TGR5 is also linked to decreased survival in patients with gastric adenocarcinomas. Interestingly, a positive correlation was found between bile acid concentration and the grade of atrophy/IM in Helicobacter pylori (H. pylori)-positive patients, suggesting that bile acid may play a significant role in H. pylori-related gastritis and GC.

In conclusion, this study demonstrates that specific gastric microbiota species can serve as useful diagnostic biomarkers for GC, although further validation in larger cohorts is necessary. The identification of key bacterial species and signaling pathways associated with GC development provides valuable insights into the underlying mechanisms of the disease. The findings underscore the potential of the gastric microbiome as a target for diagnostic and therapeutic interventions in GC.

doi.org/10.1097/CM9.0000000000001081

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