Human microbiota refers to living microorganisms which colonize our body and crucially contribute to the metabolism of nutrients and various physiologic functions. According to recently accumulated evidence, human microbiota dysbiosis in the genital tract or pelvic cavity could be involved in the pathogenesis and/or pathophysiology of endometriosis. We aimed to investigate whether the composition of microbiome is altered in the peritoneal fluid in women with endometriosis. We recruited 45 women with histological evidence of ovarian endometrioma and 45 surgical controls without endometriosis. Following the isolation of extracellular vesicles from peritoneal fluid samples from women with and without endometriosis, bacterial genomic DNA was sequenced using next-generation sequencing of the 16S rDNA V3–V4 regions. Diversity analysis showed significant differences in the microbial community at phylum, class, order, family, and genus levels between the two groups. The abundance of Acinetobacter, Pseudomonas, Streptococcus, and Enhydrobacter significantly increased while the abundance of Propionibacterium, Actinomyces, and Rothia significantly decreased in the endometriosis group compared with those in the control group (p < 0.05). These findings strongly suggest that microbiome composition is altered in the peritoneal environment in women with endometriosis. Further studies are necessary to verify whether dysbiosis itself can cause establishment and/or progression of endometriosis.

Although there is a growing interest in the role of gastric microbiome on the development of gastric cancer, the exact mechanism is largely unknown. We aimed to investigate the changes of gastric microbiome during gastric carcinogenesis, and to predict the functional potentials of the microbiome involved in the cancer development. The gastric microbiome was analyzed using gastric juice samples from 88 prospectively enrolled patients, who were classified into gastritis, gastric adenoma, or early/advanced gastric cancer group. Differences in microbial diversity and composition were analyzed with 16S rRNA gene profiling, using next-generation sequencing method. Metagenomic biomarkers were selected using logistic regression models, based on relative abundances at genus level. We used Tax4Fun to predict possible functional pathways of gastric microbiome involved in the carcinogenesis. The microbial diversity continuously decreased in its sequential process of gastric carcinogenesis, from gastritis to gastric cancer. The microbial composition was significantly different among the four groups of each disease status, as well as between the cancer group and non-cancer group. Gastritis group was differently enriched with genera Akkermansia and Lachnospiraceae NK4A136 Group, whereas the cancer group was enriched with Lactobacillus and Veillonella. Predictive analysis of the functional capacity of the microbiome suggested enrichment or depletion of several functional pathways related to carcinogenesis in the cancer group. There are significant changes in the diversity and composition of gastric microbiome during the gastric carcinogenesis process. Gastric cancer was characterized with microbial dysbiosis, along with functional changes potentially favoring carcinogenesis.

Obesity associated with a Western diet such as a high-fat diet (HFD) is a known risk factor for inflammatory bowel disease (IBD) and colorectal cancer (CRC). In this study, we aimed to develop fecal microbiome data-based deep learning algorithms for the risk assessment of colorectal diseases. The effects of a HFD and a candidate food (Nypa fruticans, NF) on IBD and CRC risk reduction were also evaluated. Fecal microbiome data were obtained from 109 IBD patients, 111 CRC patients, and 395 healthy control (HC) subjects by 16S rDNA amplicon sequencing. IBD and CRC risk assessment prediction models were then constructed by deep learning algorithms. Dietary effects were evaluated based on fecal microbiome data from rats fed on a regular chow diet (RCD), HFD, and HFD plus ethanol extracts or water extracts of NF. There were significant differences in taxa when IBD and CRC were compared with HC. The diagnostic performance (area under curve, AUC) of the deep learning algorithm was 0.84 for IBD and 0.80 for CRC prediction. Based on the rat fecal microbiome data, IBD and CRC risks were increased in HFD-fed rats versus RCD-fed rats. Interestingly, in the HFD-induced obesity model, the IBD and CRC risk scores were significantly lowered by the administration of ethanol extracts of NF, but not by the administration of water extracts of NF. In conclusion, changes in the fecal microbiome of obesity by Western diet could be important risk factors for the development of IBD and CRC. The risk prediction model developed in this study could be used to evaluate dietary efficacy.