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Peer-reviewedCC BYFrontiers in oncology

Ecological drivers of the colon cancer microbiome: evidence for niche-based selection over stochastic colonization.

  1. 1Department of Radiology, Affiliated Hospital of Yunnan University, Kunming, China.
  2. 2Microbiome Medicine and Advanced AI Technology, Kunming, China.
  3. 3Computational Biology and Medical Ecology Lab, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.

Abstract

Colon cancer is a leading global malignancy influenced by genetic, environmental, and microbial factors. This study integrates ecological theories, including Hubbell's neutral theory, augmented by Ning et al.'s (2019, PNAS) stochasticity analysis framework, and classic niche theory, to assess the contributions of stochastic colonization and deterministic niche selection in shaping microbiome assembly and dynamics in colon cancer tissues. Specialist species were identified using Levins' niche breadth analysis. By reanalyzing the datasets from Nejman et al., including 22 normal adjacent tissue (NT) and 22 primary tumor (PT) samples, we find: (i) deterministic niche selection appears to dominate community assembly and diversity maintenance in both NT and PT microbiomes, as demonstrated by the normalized stochasticity ratio (NSR: 0.267 for NT, 0.287 for PT; both below 0.5; Wilcoxon PT>NT, p=0.027). Only 18.2% NT and 27.3% PT samples fit the neutral model, confirming stochastic drift plays a limited role and niche selection predominates in structuring tissue microbiomes. (ii) Through niche breadth analysis, we identified 106 specialist species, of which 14 are known CRC-associated taxa, including Fusobacterium nucleatum, Escherichia coli, Enterococcus faecalis, and Peptostreptococcus anaerobius. These species have established roles in inflammation, genotoxicity, immune modulation, and chemoresistance. (iii) These findings suggest that deterministic niche-based selection, rather than stochastic colonization, appears to govern microbial community assembly in colon cancer tissues. (iv) We postulate that the tumor microenvironment imposes biochemical filters-including hypoxia, lactate accumulation, and altered pH-that select for specialist species capable of adapting to these conditions. (v) While the sample size is modest and validation in independent cohorts is needed, our study demonstrates an integrative framework for understanding colon cancer microbiome dynamics. We propose testable hypotheses for future research: (i) specialist species abundance may correlate with tumor stage or patient survival; (2) depletion of specialists may alter tumor progression in preclinical models; and (3) hybrid neutral-niche models may better fit larger longitudinal datasets. By integrating ecological theory with microbiome research, this study offers insights into the forces driving microbial community assembly in tumor tissues and highlights potential targets for microbiome-based diagnostics and therapeutics.

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BibTeX
@article{su2026ecological,
  title = {{Ecological drivers of the colon cancer microbiome: evidence for niche-based selection over stochastic colonization.}},
  author = {Wei Su and Zhanshan Sam Ma},
  journal = {Frontiers in oncology},
  year = {2026},
  month = oct,
  doi = {10.3389/fonc.2026.1888804},
  url = {https://europepmc.org/article/MED/42903147},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Ecological drivers of the colon cancer microbiome: evidence for niche-based selection over stochastic colonization.
AU  - Wei Su
AU  - Zhanshan Sam Ma
PY  - 2026
DA  - 2026/10/06
JO  - Frontiers in oncology
DO  - 10.3389/fonc.2026.1888804
UR  - https://europepmc.org/article/MED/42903147
AB  - Colon cancer is a leading global malignancy influenced by genetic, environmental, and microbial factors. This study integrates ecological theories, including Hubbell's neutral theory, augmented by Ning et al.'s (2019, PNAS) stochasticity analysis framework, and classic niche theory, to assess the contributions of stochastic colonization and deterministic niche selection in shaping microbiome assembly and dynamics in colon cancer tissues. Specialist species were identified using Levins' niche breadth analysis. By reanalyzing the datasets from Nejman et al., including 22 normal adjacent tissue (NT) and 22 primary tumor (PT) samples, we find: (i) deterministic niche selection appears to dominate community assembly and diversity maintenance in both NT and PT microbiomes, as demonstrated by the normalized stochasticity ratio (NSR: 0.267 for NT, 0.287 for PT; both below 0.5; Wilcoxon PT>NT, p=0.027). Only 18.2% NT and 27.3% PT samples fit the neutral model, confirming stochastic drift plays a limited role and niche selection predominates in structuring tissue microbiomes. (ii) Through niche breadth analysis, we identified 106 specialist species, of which 14 are known CRC-associated taxa, including Fusobacterium nucleatum, Escherichia coli, Enterococcus faecalis, and Peptostreptococcus anaerobius. These species have established roles in inflammation, genotoxicity, immune modulation, and chemoresistance. (iii) These findings suggest that deterministic niche-based selection, rather than stochastic colonization, appears to govern microbial community assembly in colon cancer tissues. (iv) We postulate that the tumor microenvironment imposes biochemical filters-including hypoxia, lactate accumulation, and altered pH-that select for specialist species capable of adapting to these conditions. (v) While the sample size is modest and validation in independent cohorts is needed, our study demonstrates an integrative framework for understanding colon cancer microbiome dynamics. We propose testable hypotheses for future research: (i) specialist species abundance may correlate with tumor stage or patient survival; (2) depletion of specialists may alter tumor progression in preclinical models; and (3) hybrid neutral-niche models may better fit larger longitudinal datasets. By integrating ecological theory with microbiome research, this study offers insights into the forces driving microbial community assembly in tumor tissues and highlights potential targets for microbiome-based diagnostics and therapeutics.
SN  - 2234-943X
ER  -