What does this research mean for the field?

Tumor-associated microbiome signals in primary colorectal cancer, particularly total bacterial load and Ruminococcus spp., correlate more strongly with tumor differentiation grade than with overall disease stage. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to explore the associations between tumor-associated microorganisms and clinicopathological characteristics in colorectal cancer.

June 1, 2026Open Access

PCR profiling of tumor-associated microorganisms in tissue of primary epithelial malignant tumors of colorectal origin: associations with key clinicopathological characteristics

Key Points

This research aims to explore the associations between tumor-associated microorganisms and clinicopathological characteristics in colorectal cancer.
Profiled microorganisms in primary colorectal adenocarcinoma specimens from 192 patients using targeted quantitative PCR.
Evaluated associations with tumor differentiation grade, T category, and disease stage using various statistical analyses.
Employed regularized logistic regression for disease classification and signature identification.
Tumor differentiation was linked to higher bacterial load and detection of Ruminococcus spp., particularly in G2–G3 tumors.
Logistic regression model showed moderate discrimination for high-grade disease (AUC ≈0.71).
Models for T category and overall stage achieved lower discrimination performance (AUC ≤0.59).

Abstract

Introduction Colorectal cancer is one of the most common malignancies worldwide, and microbiome research has strong potential to advance understanding of tumor biology and to support biomarker development and microbiome-targeted interventions. Most colorectal cancer microbiome studies rely on stool or mucosal sampling, but routine pathology archives contain abundant formalin-fixed, paraffin-embedded primary tumor tissue that could enable scalable assessment of tumor-associated microorganisms. Methods We profiled tumor-associated microorganisms in primary colorectal adenocarcinoma specimens from 192 patients using a targeted quantitative PCR panel and evaluated associations with clinicopathological variables (tumor differentiation grade, primary tumor T category, and disease stage) using non-parametric tests, ordinal regression, regularized logistic regression, and within-panel profile ordination with permutation-based inference. Results The most consistent signals were linked to tumor differentiation rather than stage. Tumors in the G2–G3 versus G1 comparison showed higher total bacterial load and a higher detection frequency of Ruminococcus spp., with both associations remaining significant after false discovery rate control; regression analyses corroborated these findings. A regularized logistic model achieved moderate discrimination for high-grade (G2-G3) disease (mean area under the receiver operating characteristic curve ≈0.71) and yielded a compact signature dominated by total bacterial load and detection of Ruminococcus spp., with additional contributions from low-frequency taxa that warrant cautious interpretation. In contrast, models targeting primary tumor T category, invasiveness (T1–T2 vs T3–T4), or overall stage showed low discriminative performance (area under the curve ≤0.59), and within-panel profile distances did not reveal robust global separation of groups. Conclusions Targeted quantitative PCR profiling of archived primary tumor tissue identifies reproducible microbiome signals that track tumor differentiation grade more strongly than stage, suggesting that tissue bacterial burden and selected taxa may reflect microenvironmental features associated with the G2–G3 versus G1 contrast. Broader tumor microbiome profiling should be required to capture diversity and refine clinically informative signatures.

KI fragen

Bookmark

View Full Paper