Gut microbes on the risk of advanced adenomas

• Published in: BMC microbiology Vol. 24; no. 1; pp. 264 - 14
• Main Authors: Jing, Zhuang, Zheng, Wu, Jianwen, Song, Hong, Shen, Xiaojian, Yu, Qiang, Wei, Yunfeng, Yin, Xinyue, Wu, Shuwen, Han, Feimin, Zhao
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 18.07.2024; BioMed Central; BMC
• Subjects: Adenoma - microbiology; Adenoma - virology; Advanced adenomas; Aged; Algorithms; Bacteria; Bacteria - classification; Bacteria - genetics; Bacteria - isolation & purification; Biological diversity; Biomarkers; Cancer; Care and treatment; Colonoscopy; Colorectal cancer; Colorectal Neoplasms - microbiology; Colorectal Neoplasms - virology; Comparative analysis; Composition; Data mining; Diagnosis; Enteroviruses; Escherichia coli; Feces; Feces - microbiology; Feces - virology; Female; Gastrointestinal Microbiome - genetics; Genomes; Gut bacteria; High-Throughput Nucleotide Sequencing; Homeostasis; Hospitals; Humans; Inflammatory bowel disease; Informed consent; Irritable bowel syndrome; Lipids; Machine Learning; Male; Metagenomic sequencing; Methods; Microbiota (Symbiotic organisms); Microorganisms; Middle Aged; Patient compliance; Patients; Prediction model; Prediction models; Prevention; Rectal polyps; Risk factors; Software; Tumors; Typing; Veganism; Viruses; Viruses - classification; Viruses - genetics; Viruses - isolation & purification; Viruses - pathogenicity; China; Enteroviruses; Gut bacteria; Advanced adenomas; Prediction model; Metagenomic sequencing
• DOI: 10.1186/s12866-024-03416-z
• ISSN: 1471-2180

More than 90% of colorectal cancer (CRC) arises from advanced adenomas (AA) and gut microbes are closely associated with the initiation and progression of both AA and CRC. To analyze the characteristic microbes in AA. Fecal samples were collected from 92 AA and 184 negative control (NC). Illumina HiSeq X sequencing platform was used for high-throughput sequencing of microbial populations. The sequencing results were annotated and compared with NCBI RefSeq database to find the microbial characteristics of AA. R-vegan package was used to analyze α diversity and β diversity. α diversity included box diagram, and β diversity included Principal Component Analysis (PCA), principal co-ordinates analysis (PCoA), and non-metric multidimensional scaling (NMDS). The AA risk prediction models were constructed based on six kinds of machine learning algorithms. In addition, unsupervised clustering methods were used to classify bacteria and viruses. Finally, the characteristics of bacteria and viruses in different subtypes were analyzed. The abundance of Prevotella sp900557255, Alistipes putredinis, and Megamonas funiformis were higher in AA, while the abundance of Lilyvirus, Felixounavirus, and Drulisvirus were also higher in AA. The Catboost based model for predicting the risk of AA has the highest accuracy (bacteria test set: 87.27%; virus test set: 83.33%). In addition, 4 subtypes (B1V1, B1V2, B2V1, and B2V2) were distinguished based on the abundance of gut bacteria and enteroviruses (EVs). Escherichia coli D, Prevotella sp900557255, CAG-180 sp000432435, Phocaeicola plebeiuA, Teseptimavirus, Svunavirus, Felixounavirus, and Jiaodavirus are the characteristic bacteria and viruses of 4 subtypes. The results of Catboost model indicated that the accuracy of prediction improved after incorporating subtypes. The accuracy of discovery sets was 100%, 96.34%, 100%, and 98.46% in 4 subtypes, respectively. Prevotella sp900557255 and Felixounavirus have high value in early warning of AA. As promising non-invasive biomarkers, gut microbes can become potential diagnostic targets for AA, and the accuracy of predicting AA can be improved by typing.