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Background <p>The development of early-onset colorectal cancer (EO-CRC) is linked to environmental exposures and gut microbiota alterations. We aimed to discover the connection and develop prediction strategies.</p> Methods <p>In the observational study, we performed 16S rRNA sequencing and metagenomic sequencing on 76 samples from discovery cohort and validation cohort, and qPCR analysis of selected microbiota, along with lifestyle and dietary assessment on 298 samples from validation cohort. Mediation analysis was employed to investigate the mediating role of gut microbiota. Logistic regression analysis evaluated the optimal prediction model for EO-CRC, with the area under the receiver operating characteristic curves (AUC) assessing diagnostic value.</p> Results <p>Dysbiosis of the EO-CRC gut microbiota was characterised by evaluated abundance of <i>F</i>. <i>nucleatum, P. micra, Pks</i><sup><i>+</i></sup> <i>E. coli</i>, and <i>F. Plautii</i>. Mediation analysis showed that <i>Pks</i><sup><i>+</i></sup> <i>E. coli</i> mediated the relationship between fried food, processed meat and coffee to EO-CRC, while <i>F. nucleatum</i> mediated the adverse effects of snacks. A combination of three bacterial markers along with lifestyle and diet demonstrated strong diagnostic potential (AUC = 0.95, 95% CI = 0.92–0.98).</p> Conclusions <p>Our data suggested that the EO-CRC-enriched bacteria may mediate the effects of lifestyle and dietary factors on disease development. A predictive model combining diet, lifestyle, and gut bacteria demonstrated promising early predictive capabilities.</p>

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The relationship between gut microbiota, lifestyle habits, and early-onset colorectal cancer: shedding light on early prediction

  • 期刊论文

  • 发表日期：

    2025年12月9日

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  • Jia-Wen Deng,
  • Yi-Lu Zhou,
  • Ya-Xuan Zhang,
  • Cheng-Bei Zhou,
  • Jing-Yuan Fang

摘要

Background

The development of early-onset colorectal cancer (EO-CRC) is linked to environmental exposures and gut microbiota alterations. We aimed to discover the connection and develop prediction strategies.

Methods

In the observational study, we performed 16S rRNA sequencing and metagenomic sequencing on 76 samples from discovery cohort and validation cohort, and qPCR analysis of selected microbiota, along with lifestyle and dietary assessment on 298 samples from validation cohort. Mediation analysis was employed to investigate the mediating role of gut microbiota. Logistic regression analysis evaluated the optimal prediction model for EO-CRC, with the area under the receiver operating characteristic curves (AUC) assessing diagnostic value.

Results

Dysbiosis of the EO-CRC gut microbiota was characterised by evaluated abundance of F. nucleatum, P. micra, Pks+ E. coli, and F. Plautii. Mediation analysis showed that Pks+ E. coli mediated the relationship between fried food, processed meat and coffee to EO-CRC, while F. nucleatum mediated the adverse effects of snacks. A combination of three bacterial markers along with lifestyle and diet demonstrated strong diagnostic potential (AUC = 0.95, 95% CI = 0.92–0.98).

Conclusions

Our data suggested that the EO-CRC-enriched bacteria may mediate the effects of lifestyle and dietary factors on disease development. A predictive model combining diet, lifestyle, and gut bacteria demonstrated promising early predictive capabilities.