Comprehensive microbial network analysis of gastric microbiome reveal key species affecting gastric carcinogenesis

Abstract

Gastric cancer continues to be one of the most significant cancers globally. Changes in microbiome composition and diversity have been implicated in gastric carcinogenesis; however, little is known about microbial community interactions and keystone species that drive these changes toward carcinogenesis. The aim of this work was to elucidate essential microbial interactions and community drivers that affect gastric carcinogenesis. We mined and utilized publicly available gastric microbiome data of adult human gastric biopsies with specific gastric diseases (gastric cancer, gastritis (chronic, superficial, atrophic), intestinal metaplasia) and healthy controls. We analysed the combined dataset using standard microbiome data analytics. Network topological properties of the various networks for gastric conditions revealed that the clustering coefficient decreased with increasing carcinogenesis. Higher modularity and lower averaged path were observed in the healthy control group as compared to the pre-cancerous and cancer groups. Sucinnate-producing and utilizing species, Bacteroides coprocola, Blautia wexlerae, and Phascolarctobacterium succinatutens, were also identified as important hubs in the precancerous conditions (gastritis and intestinal metaplasia) while nitrate-reducing bacteria Rothia mucilaginosa, Haemophilus haemolyticus, Haemophilus parainfluenzae, and Veillonella dispar were revealed as hubs in the gastric cancer group. Haemophilus parainfluenzae, which is an opportunistic oral pathogen, was identified as a potential network connector in three gastric conditions (healthy, intestinal metaplasia, and gastric cancer). Sphingobium fontiphilum and Rothia mucilaginosa were also revealed as keystone species for the control and cancer groups, respectively. The results from the clustering revealed distinct clusters in different gastric networks, indicating that microbial communities formed different functional groups during carcinogenesis. The random forest models also revealed pathogenic species Fusobacterium nucleatum be highly discriminative between healthy controls and gastric cancer groups.