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. 2024 Oct 17;22(1):947.
doi: 10.1186/s12967-024-05720-8.

Parvimonas micra forms a distinct bacterial network with oral pathobionts in colorectal cancer patients

Thyra Löwenmark  1 , Linda Köhn  1 , Therese Kellgren  2 , William Rosenbaum  1 , Vicky Bronnec  1 , Anna Löfgren-Burström  1 , Carl Zingmark  1 , Pär Larsson  1 , Michael Dahlberg  3 , Bjoern O Schroeder  4 , Sun Nyunt Wai  4 , Ingrid Ljuslinder  3 , Sofia Edin  1 , Richard Palmqvist  5
Affiliations

Parvimonas micra forms a distinct bacterial network with oral pathobionts in colorectal cancer patients

Thyra Löwenmark et al. J Transl Med. .

Abstract

Background: Mounting evidence suggests a significant role of the gut microbiota in the development and progression of colorectal cancer (CRC). In particular, an over-representation of oral pathogens has been linked to CRC. The aim of this study was to further investigate the faecal microbial landscape of CRC patients, with a focus on the oral pathogens Parvimonas micra and Fusobacterium nucleatum.

Methods: In this study, 16S rRNA sequencing was conducted using faecal samples from CRC patients (n = 275) and controls without pathological findings (n = 95).

Results: We discovered a significant difference in microbial composition depending on tumour location and microsatellite instability (MSI) status, with P. micra, F. nucleatum, and Peptostreptococcus stomatis found to be more abundant in patients with MSI tumours. Moreover, P. micra and F. nucleatum were associated with a cluster of CRC-related bacteria including Bacteroides fragilis as well as with other oral pathogens such as P. stomatis and various Porphyromonas species. This cluster was distinctly different in the control group, suggesting its potential linkage with CRC.

Conclusions: Our results suggest a similar distribution of several CRC-associated bacteria within CRC patients, underscoring the importance of considering the concomitant presence of bacterial species in studies investigating the mechanisms of CRC development and progression.

Keywords: Fusobacterium nucelatum; Parvimonas micra; Colorectal cancer; Intestinal microbiota; Oral pathobionts.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Taxonomic analysis of the faecal microbiomes of CRC patients and controls represented at the (a) phylum level and (b) genus level
Fig. 2
Fig. 2
Intestinal bacterial richness and diversity in faecal samples of CRC patients and controls: (a) alpha diversity analyses based on Ace, Chao1, Shannon, and Simpson indices; (b) beta diversity analysis using Bray–Curtis distances
Fig. 3
Fig. 3
The XGBoost algorithm was used to determine the taxa most likely to reveal a difference between faecal samples of CRC patients and controls, as illustrated by the resulting (a) ROC-curve, and (b) the top 20 most important prediction taxa. Blue and red bars represent ASVs with a higher expression in patients and controls, respectively
Fig. 4
Fig. 4
Beta diversity analysis using the Bray–Curtis distances of microbial species for (a) tumour stage, (b) tumour location, (c) MSI status, (d) BRAF mutation status, and (e) KRAS mutation status
Fig. 5
Fig. 5
Top ASVs with the most significant differences in abundance between MSI and MSS tumours as determined by the MWU test. The ASVs were sorted according to p-value, showing the ASV with the lowest p-value at the top (nominal p-values were used). A negative Cliff’s delta indicates higher abundance in MSI tumours, whereas a positive Cliff’s delta indicates higher abundance in MSS tumours
Fig. 6
Fig. 6
Walktrap network analysis: (a) cluster containing P. micra in faeces of CRC patients; (b) cluster containing P. micra in faeces of controls
Fig. 7
Fig. 7
Violin and scatter plots for the distribution of bacterial counts for each bacterium at species level identified within the P. micra cluster. The count values were centered log ratio transformed using the vegan package (v. 2.6). MWU tests were used to analyse differences between cancers and controls for each bacterium. P values were corrected for multiple comparisons using the Holm method. A P value < 0.05 was considered statistically significant
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