Comparative Analysis of Microbiological Profiles and Antibiotic Resistance Genes in Subjects with Colorectal Cancer and Healthy Individuals

• Published in: Polish journal of microbiology Vol. 74; no. 1; pp. 71 - 81
• Main Authors: Li, Jun, Zhu, Yanyun, Chang, Qing, Gong, Yuan, Wan, Jun, Xu, Shiping
• Format: Journal Article
• Language: English
• Published: Poland Sciendo 01.03.2025; De Gruyter Poland
• Subjects: Actinobacteria; Aged; Anti-Bacterial Agents - pharmacology; Antibiotic resistance; antibiotic resistant genes; Antibiotics; Bacteria - classification; Bacteria - drug effects; Bacteria - genetics; Bacteria - isolation & purification; Cancer; Case-Control Studies; Colorectal cancer; Colorectal carcinoma; Colorectal Neoplasms - microbiology; Comparative analysis; Drug Resistance, Bacterial - genetics; Drug Resistance, Microbial - genetics; Enterobacter cloacae; Feces; Feces - microbiology; Female; Gastrointestinal Microbiome - genetics; Genera; Genes; gut microbiota; Humans; Intestinal microflora; Male; Metabolism; Metagenomics; Microbiomes; Microbiota; Microorganisms; Middle Aged; Next-generation sequencing; Nostoc; Original Paper; Principal components analysis; Risk assessment; Salmonella; Actinobacteria; antibiotic resistant genes; colorectal cancer; metagenomics; gut microbiota
• DOI: 10.33073/pjm-2025-006
• ISSN: 2544-4646; 1733-1331

Alteration of the gut microbiota (GM) is associated with various diseases, including colorectal cancer (CRC). With the development of next-generation sequencing techniques, metagenomic sequencing, along with metabolic function and antibiotic-resistant gene analyses, has been used to investigate differences in GM between CRC patients and healthy controls. Fecal samples were obtained from seven CRC patients and six healthy subjects, and the sequencing data were analyzed for similarity, a-diversity, principal component analysis (PCA), and linear discriminant analyses (LDA). Regarding Actinobacteria, 3 orders, 5 families, 9 genera, and 19 species were identified with no differences between the CRC and control groups, while the levels of and were higher, and the level of was lower in the CRC group compared to the healthy controls ( = 0.053). Otherwise, 2 genera ( and ) and 7 species of bacteria ( , unclassified ) were found to be significantly differently distributed between CRC patients and healthy controls. PCA-LDA successfully classified these 2 groups with satisfactory accuracy (84.52% for metabolic function and 77.38% for resistant genes). These findings underscore the potential of GM as a diagnostic tool for CRC, offering a promising avenue for non-invasive screening and risk assessment. The identification of specific microbial signatures, particularly those linked to metabolic functions and resistance traits, could open new doors for understanding the role of the microbiome in CRC progression and treatment resistance.