Recurrence Mechanisms of Colorectal Adenoma After Resection and a Multi-Omics Research Protocol

Overview

We aim to establish a single-cell atlas of primary adenomas, recurrent adenomas, and paired normal mucosa, identify recurrence-associated cell subpopulations and transcriptional pathways, and construct a risk prediction model for recurrence (integrating multi-omics features and clinical variables). Ultimately, we propose a panel of biomarkers that can be used to optimize follow-up strategies and guide potential microbiome-based interventions. Under the premise of meeting routine clinicopathological requirements, we will collect tissue samples from 30 patients with colorectal adenomas. Each collected adenoma sample will be divided into three equal portions for scRNA-seq, scATAC-seq, and microbiome diversity sequencing, respectively. Two years after sampling, enrolled patients will undergo follow-up colonoscopy. Those in whom adenomas are detected will be classified as the recurrence group, while those without adenomas will be classified as the non-recurrence group. Sequencing data will be jointly analyzed in conjunction with clinical medical records. The tissue specimens obtained will not affect routine diagnosis or treatment outcomes.

This study aims to: (1) establish a single-cell atlas of primary adenomas, recurrent adenomas, and paired normal mucosa, and identify recurrence-associated cell subpopulations and transcriptional pathways; (2) construct a recurrence risk prediction model integrating multi-omics features and clinical variables, and propose a panel of biomarkers for optimizing follow-up strategies and guiding potential microbiome-based interventions. The study plans to collect colorectal adenoma tissues from 30 patients who are first diagnosed by endoscopy. Each specimen will be equally divided into three portions for single-cell RNA sequencing (scRNA-seq), single-cell ATAC sequencing (scATAC-seq), and microbiome diversity sequencing. Two years after sampling, all enrolled patients will undergo follow-up colonoscopy; those found to have adenomas will be assigned to the recurrence group, while those without adenomas will be assigned to the non-recurrence group. By integrating multi-omics data for association analysis between the two groups, we aim to elucidate the key drivers of post-polypectomy recurrence, and explore whether there exists an interpretable causal chain linking host epigenetic status, cellular transcriptional programs, and mucosa-associated microbiota that can explain recurrence risk and yield translatable biomarkers. A total of 30 cases will be included. Inclusion criteria: patients with colorectal adenoma confirmed by initial endoscopic examination and pathology. Exclusion criteria: (1) use of antibiotics within the past two weeks; (2) patients without colorectal adenoma; (3) patients with familial adenomatous polyposis; (4) patients with special dietary habits such as vegetarianism, alcoholism, binge eating, or anorexia. Under the premise of meeting routine clinicopathological requirements, 30 colorectal adenoma tissue samples will be collected and each sample will be divided into three equal parts for scRNA-seq, scATAC-seq, and microbiome diversity sequencing. Two years after sampling, patients will undergo follow-up colonoscopy; those with adenomas detected will be classified as the recurrence group, and those without as the non-recurrence group. Through integrated multi-omics correlation analysis, a single-cell integrative atlas of "mucosal microbiota - host epigenome (scATAC) - transcriptome (scRNA)" will be constructed to decipher the key cell states, regulatory elements, and microenvironmental signals underlying post-adenomectomy recurrence, and to establish a multidimensional biomarker panel for risk stratification. Statistical analysis will be performed using SPSS 26.0. Univariate statistical tests will be applied to compare differences between groups. For normally distributed data, ANOVA (for multiple groups) and Student's t-test (for two groups) will be used; for non-normally distributed data, the Kruskal-Wallis H test (for multiple groups) and Mann-Whitney U test (for two groups) will be employed.