Preoperative gut microbiota data of colorectal cancer patients

DNA extraction was performed using the QIAamp Fast DNA Stool Mini Kit (QIAGEN) under sterile conditions to avoid contamination. The V3–V4 region of the microbial 16S rRNA gene was sequenced using the Illumina MiSeq high-throughput sequencing platform. Raw sequencing data were subjected to quality control using Trimmomatic software to remove low-quality reads and adapter sequences.

USEARCH software was then used for sequence clustering and construction of Operational Taxonomic Units (OTUs), followed by taxonomic annotation.The dataset is primarily organized in CSV table format, including approximately 434 individual sample records, each representing the gut microbiota composition of a single patient. The row labels in the data table are “Sample ID,” and the column labels include fields such as “OTU ID,” “Genus Name,” and “Relative Abundance,” with the latter expressed as a percentage (%).

Basic clinical information (e.g., gender, age, cancer stage) is also included to facilitate analysis of the relationships between microbial structure and clinical factors. Additionally, the microbial abundance matrix is provided in BIOM format, which is compatible with microbial analysis tools such as QIIME2 and R (phyloseq package). QIIME2 can be downloaded from qiime2.org/.Due to low-quality sequences or failed DNA extraction in some samples, the final number of samples included in the analysis is slightly less than the total initially collected.

Missing data are clearly indicated in the accompanying metadata file, with missing fields marked as “NA.” Despite strict sample processing protocols, sequencing errors may still be present, primarily due to PCR amplification bias and platform-specific issues. The estimated error rate is around 1%, and quality control and filtering procedures have been implemented to minimize this impact.This dataset provides a reliable foundation for future studies on microbiota-related mechanisms in colorectal cancer and the development of preoperative risk assessment models.