Abstract
OBJECTIVE: This study aims to explore the differences in composition, abundance, and biological functions of the gut microbiota between colorectal cancer (CRC) patients with peripheral nerve invasion (PNI) and those without peripheral nerve invasion (NPNI). Additionally, we tried to construct a machine-learning predictive model incorporating the identified microbiota characteristics to explore the impact of gut microbiota on CRC-PNI progression and to search for new non-invasive microbiological indicators for CRC-PNI. Finally, we successfully developed a predictive model to predict PNI in CRC patients through leveraging microbial biomarkers. This innovative approach is expected to offer a novel strategy for the early detection of CRC metastasis, thereby facilitating more informed decisions regarding treatment options. METHOD: This study included 132 colorectal cancer (CRC) patients, who were divided into two separate groups according to whether they exhibited PNI. The gut microbiota of these participants were subjected to 16S rRNA gene sequencing, followed by a thorough analysis to identify any significant differences between the groups. We applied a cell sorting algorithm to convert the transcriptome sequencing data obtained from 8 colorectal cancer patients into a matrix representing immune cell abundance. Following this, the matrix was utilized to investigate the associations among the PNI-related distinct gut microbiota, immune cells, and immune-related genes, and PNI-related differentially expressed genes (or molecular markers, pathways), as well as their associations with KEGG pathways. Based on the differential gut microbiota, we constructed Random Forest (RF) and Multilayer Perceptron (MLP) models to predict PNI in CRC patients. RESULT: Comparative analysis of α-diversity and β-diversity in the gut microbiota of CRC patients with and without PNI revealed no statistically significant differences (P > 0.05). However, Linear Discriminant Analysis effect size (LEfSe) identified 35 distinct gut microbiota, with 28 species enriched in the PNI group and 7 species significantly enriched in the NPNI group. By analyzing the gut microbiota significantly associated with PNI, we successfully constructed predictive models using RF and MLP that can predict the occurrence of PNI in CRC patients. Both models have demonstrated robust performance. CONCLUSIONS: In the PNI and NPNI groups, 35 gut microbiota species exhibited significant variations in abundance. The differential intestinal microbiota associated with PNI in colorectal cancer may modulate the neuroinvasion process via a variety of potential biological mechanisms. The RF and MLP predictive models show considerable accuracy in predicting CRC-PNI status and are of reference value.