Combined Non-Invasive Prediction and New Biomarkers of Oral and Fecal Microbiota in Patients With Gastric and Colorectal Cancer

Chaoyang Zhang, Asheng Hu, Jingxing Li, Fangfang Zhang, Pei Zhong, Yaxian Li, Yongxiang Li

There is no information on the commonality and specificity of oral and fecal microbiota in patients with gastric cancer (GC) and colorectal cancer (CRC). The high-throughput 16S rRNA gene V4 region sequencing was used to perform bioinformatics analysis of oral, fecal, and tissue microbiota in GC (76 subjects), CRC (53), and healthy controls (HC, 70). Furthermore, we determined the microbial characteristics of each part, constructed and verified three classifiers for GC and CRC, and evaluated curves of receiver operating characteristic and precision-recall with probability of disease. Compared to HC, the microbial richness and diversity of GC and CRC decreased in oral cavity and increased in stool; additionally, these indexes in GC tissue were higher than those in CRC tissue. In GC and CRC patients, Haemophilus, Neisseria, Faecalibacterium, and Romboutsia were significantly reduced compared to the relative abundance value of oral or fecal bacterial genera in the HC group, while the Streptococcus, Gemella, Escherichia-Shigella, and Fusobacterium were significantly increased. The oral and tissue microbiota have similar and abundant shared bacterial networks. The single and combined microbial detection have good AUC values based on POD indices for predicting GC, CRC, and gastrointestinal (GI) cancers (GC and CRC). This study is the first to examine the characteristics of oral, fecal, and tumor microbiota in GC and CRC patients, and the similarities and differences in their microbial changes are reported. These oral or fecal bacteria (Haemophilus, Neisseria, Faecalibacterium, Romboutsia, Streptococcus, Gemella, Escherichia-Shigella, and Fusobacterium) may be involved in tumor evolution as potentially characteristic genera. In addition, both oral and fecal microbial detection may provide a solid theoretical foundation for the non-invasive prediction of these cancers.