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Helicobacter pylori (H. pylori) has been regarded as a definite carcinogenic bacterium for gastric cancer (GC). This multi-omics research was designed to investigate the genetic, microbial, and metabolic changes of GC patients when they are infected with H. pylori. We first mined The Cancer Genome Atlas Stomach Adenocarcinoma (STAD) data to identify the key genes and critical pathways in H. pylori-positive individuals with GC compared to H. pylori-negative individuals with GC. Then, fresh stool samples were collected from GC individuals screened for eligibility, and we analyzed the microbial changes and metabolite alterations between H. pylori-positive and H. pylori-negative GC individuals. Finally, we tried to explore the interaction between key gut flora and metabolite changes in GC patients infected with H. pylori. We identified three genes (GCG, APOA1, and IGFBP1) with significant relevance to H. pylori infection, and the survival monogram based on the three H. pylori-related genes showed good predictive ability for overall survival among GC individuals. 16S rRNA sequencing showed that the abundance of Escherichia-Shigella, Bacteroides, Enterococcus, and Lactobacillus was upregulated in GC cases with H. pylori at the level of genus. There exists a great difference in alpha and beta diversity between H. pylori group and non-H. pylori group. The untargeted metabolome analysis identified 295 significant fecal metabolites, and the levels of penitrem E, auberganol, stercobilinogen, and lys thr are upregulated in the H. pylori group. Finally, correlation analysis showed that there exists a significant correlation between the fecal metabolites and gut bacterial strains. This is the first clinical research to investigate the difference between GC patients with H. pylori and GC patients without H. pylori via multi-omics analysis. 16S rRNA sequencing along with untargeted metabolomics demonstrated decreased microbial diversity and metabolic dysregulation in gastric carcinoma individuals with H. pylori infection.IMPORTANCEThis is the first clinical research to systematically expound the difference between gastric cancer (GC) individuals with Helicobacter pylori and GC individuals without H. pylori from the perspective of multi-omics. This clinical study identified significant genes, microbes, and fecal metabolites, which exhibited nice power for differentiating GC individuals with H. pylori infection from GC individuals without H. pylori infection. This study provides a crucial basis for a better understanding of eradication therapy among the GC population.
Pubmed ID: 37846989 RIS Download
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Curated collection of human metabolite and human metabolism data which contains records for endogenous metabolites, with each metabolite entry containing detailed chemical, physical, biochemical, concentration, and disease information. This is further supplemented with thousands of NMR and MS spectra collected on purified reference metabolites.
View all literature mentionsA public repository of metabolite information as well as tandem mass spectrometry data is provided to facilitate metabolomics experiments. It contains structures and represents a data management system designed to assist in a broad array of metabolite research and metabolite identification. An annotated list of known metabolites and their mass, chemical formula, and structure are available. Each metabolite is linked to outside resources for further reference and inquiry. MS/MS data is also available on many of the metabolites.
View all literature mentionsOrdination methods, diversity analysis and other functions for community and vegetation ecologists.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Algorithm for high-dimensional biomarker discovery and explanation that identifies genes, pathways, or taxa characterizing the differences between two or more biological conditions. The algorithm identifies features that are statistically different among biological classes, then performs additional tests to assess whether these differences are consistent with respect to expected biological behavior. Statistical significance and biological relevance are emphasized.
View all literature mentionsSoftware tool as next generation in LC-MS proteomics data analysis software by Nonlinear Dynamics.
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