Altered Gastric Microbiota and Inflammatory Cytokine Responses in Patients with Helicobacter pylori -Negative Gastric Cancer.
Han-Na KimMin-Jeong KimJonathan Patrick JacobsHyo-Joon YangPublished in: Nutrients (2022)
The role of the gastric mucosal microbiome in Helicobacter pylori -negative gastric cancer (GC) remains unclear. Therefore, we aimed to characterize the microbial alterations and host inflammatory cytokine responses in H. pylori -negative GC. Gastric mucosal samples were obtained from 137 H. pylori -negative patients with GC ( n = 45) and controls (chronic gastritis or intestinal metaplasia, n = 92). We performed 16S rRNA gene sequencing ( n = 67), a quantitative reverse transcription-polymerase chain reaction to determine the relative mRNA expression levels of TNF (tumor necrosis factor), IL1B (interleukin 1 beta), IL6 (interleukin 6), CXCL8 (C-X-C motif chemokine ligand 8), IL10 (interleukin 10), IL17A (interleukin 17A), TGFB1 (transforming growth factor beta 1) ( n = 113), and the correlation analysis between sequencing and expression data ( n = 47). Gastric mucosal microbiota in patients with GC showed reduced diversity and a significantly different composition compared to that of the controls. Lacticaseibacillus was significantly enriched, while Haemophilus and Campylobacter were depleted in the cancer group compared to the control group. These taxa could distinguish the two groups in a random forest algorithm. Moreover, the combined relative abundance of these taxa, a GC microbiome index, significantly correlated with gastric mucosal IL1B expression, which was elevated in the cancer group. Overall, altered gastric mucosal microbiota was found to be associated with increased mucosal IL1B expression in H. pylori -negative GC.
Keyphrases
- helicobacter pylori
- helicobacter pylori infection
- poor prognosis
- ulcerative colitis
- transforming growth factor
- gas chromatography
- epithelial mesenchymal transition
- machine learning
- transcription factor
- squamous cell carcinoma
- long non coding rna
- single cell
- binding protein
- pseudomonas aeruginosa
- mass spectrometry
- genome wide
- escherichia coli
- deep learning
- dna methylation
- microbial community
- artificial intelligence
- childhood cancer
- antibiotic resistance genes