Comparison of gut microbial signatures associated with colorectal cancer across two different sample collection datasets
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DOI:
https://doi.org/10.15625/vjbt-23336Keywords:
16S rRNA metagenomics, colorectal cancer, gut microbial signatures, sample collection dataset, Vietnamese patients.Abstract
Colorectal cancer (CRC) is among the most prevalent cancers globally and in Vietnam. Diagnosing CRC is challenging due to difficulties in tumor detection, leading to thousands of deaths annually. CRC has been demonstrated to be linked with alterations in gut microbial composition and function. Various bacterial taxa have been recognized as potential biomarkers of CRC and suspected to play a crucial role in colon carcinogenesis. This pilot study explores consistent and divergent bacterial signatures in the fecal microbiota of 15 CRC patients compared to 12 healthy individuals in two different sample collection datasets. Both datasets proceeded with the same DNA extraction method, followed by amplification of the 16S rRNA gene's hypervariable regions (V3-V4), and then sequenced with Illumina MiSeq sequencing platform at two different time points. Our findings show that the gut microbiota's alpha and beta diversity did not differ statistically significantly between the healthy individuals and the CRC patients in either dataset. We observed 12 genera in the first dataset and 13 genera in the second dataset that exhibited significant differential abundance between CRC patients and healthy controls. However, due to the small sample size, after adjustment for multiple testing, only Peptostreptococcus and Fusobacterium in Dataset 1, and Parvimonas in Dataset 2, remained significantly associated with CRC according to ANCOM-BC2. Notably, Parvimonas was also detected in CRC patients but not in healthy controls in Dataset 1. This genus may potentially be used as fecal biomarker for CRC detection in Vietnamese patients. Additionally, our study underscores the importance of validating fecal bacterial biomarkers across different sample collection datasets to improve the accuracy and effectiveness of CRC diagnosis and treatment, potentially advancing personalized treatment approaches for Vietnamese patients.
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References
Andrews S. (2010). FastQC a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/.
Agronah M. and Bolker B. (2025). Investigating statistical power of differential abundance studies. PLoS One, 20(4), e0318820. https://doi.org/10.1371/journal.pone.0318820
Bray F., Laversanne M., Sung H., Ferlay J., Siegel R. L., Soerjomataram I., et al. (2024). Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 74(3), 229-263. https://doi.org/10.3322/caac.21834
Bull M. J. and Plummer N. T. (2014). Part 1: The human gut microbiome in health and disease. Integrative Medicine (Encinitas), 13(6), 17-22.
Cole J. R., Wang Q., Cardenas E., Fish J., Chai B., Farris R. J., et al. (2009). The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Research, 37 (Database issue), D141-145. https://doi.org/10.1093/nar/gkn879
Crane R. J., Parker E. P. K., Fleming S., Gwela A., Gumbi W., Ngoi J. M., et al. (2022). Cessation of exclusive breastfeeding and seasonality, but not small intestinal bacterial overgrowth, are associated with environmental enteric dysfunction: A birth cohort study amongst infants in rural Kenya. EClinicalMedicine, 47, 101403. https://doi.org/10.1016/j.eclinm.2022.101403
Duan B., Zhao Y., Bai J., Wang J., Duan X., Luo X., et al. (2022). Colorectal cancer: An overview. In J. A. Morgado-Diaz (Ed.), Gastrointestinal Cancers. Brisbane (AU).
Faul F., Erdfelder E., Lang A. G., and Buchner A. (2007). G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175-191. https://doi.org/10.3758/bf03193146
Flemer B., Lynch D. B., Brown J. M., Jeffery I. B., Ryan F. J., Claesson M. J., et al. (2017). Tumour-associated and non-tumour-associated microbiota in colorectal cancer. Gut, 66(4), 633-643. https://doi.org/10.1136/gutjnl-2015-309595
Ghosh T. S., Das M., Jeffery I. B., and O'Toole P. W. (2020). Adjusting for age improves identification of gut microbiome alterations in multiple diseases. Elife, 9. https://doi.org/10.7554/eLife.50240
Hou K., Wu Z. X., Chen X. Y., Wang J. Q., Zhang D., Xiao C., et al. (2022). Microbiota in health and diseases. Signal Transduction and Targeted Therapy, 7(1), 135. https://doi.org/10.1038/s41392-022-00974-4
Jokela R., Ponsero A. J., Dikareva E., Wei X., Kolho K. L., Korpela K., et al. (2023). Sources of gut microbiota variation in a large longitudinal Finnish infant cohort. EBioMedicine, 94, 104695. https://doi.org/10.1016/j.ebiom.2023.104695
Le N. T. and Dao H. V. (2020). Colorectal cancer in Vietnam. Colorectal Cancer. IntechOpen. https://doi.org/10.5772/intechopen.93730
Lin H. and Peddada S. D. (2024). Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures. Nature Methods, 21(1), 83-91. https://doi.org/10.1038/s41592-023-02092-7
Löwenmark T., Löfgren-Burström A., Zingmark C., Eklöf V., Dahlberg M., Wai S. N., et al. (2020). Parvimonas micra as a putative non-invasive faecal biomarker for colorectal cancer. Scientific Reports, 10(1), 15250. https://doi.org/10.1038/s41598-020-72132-1
Nguyen B. N., Nguyen L. T. N., Trinh D. T. M., Nguyen H. T., and Tran T. T. T. (2025). Preliminary insights into the gut microbiota of patients with rheumatoid arthritis in Vietnam. PeerJ, 13, e20521. https://doi.org/10.7717/peerj.20521
Nhung P. T. T., Hang L. T. T., and Tam T. T. T. (2023). Preliminary assessment of gut microbiota diversity in colorectal cancer patients using 16S rRNA sequencing. Journal of 108 - Clinical Medicine and Phamarcy, 18(8). https://doi.org/10.52389/ydls.v18i8.2095.
Nhung P. T. T., Le H. T. T., Nguyen Q. H., Huyen D. T., Quyen D. V., Song L. H., et al. (2024). Identifying fecal microbiota signatures of colorectal cancer in a Vietnamese cohort. Frontiers in Microbiology, 15, 1388740. https://doi.org/10.3389/fmicb.2024.1388740
Sender R., Fuchs S., and Milo R. (2016). Revised estimates for the number of human and bacteria cells in the body. PLoS Biology, 14(8), e1002533. https://doi.org/10.1371/journal.pbio.1002533
Shen Y., Fan N., Ma S. X., Cheng X., Yang X., and Wang G. (2025). Gut microbiota dysbiosis: pathogenesis, diseases, prevention, and therapy. MedComm, 6(5), e70168. https://doi.org/10.1002/mco2.70168
Siegel R. L., Miller K. D., Goding Sauer A., Fedewa S. A., Butterly L. F., Anderson J. C., et al. (2020). Colorectal cancer statistics, 2020. CA: A Cancer Journal for Clinicians, 70(3), 145-164. https://doi.org/10.3322/caac.21601
Siegwald L., Caboche S., Even G., Viscogliosi E., Audebert C., and Chabe M. (2019). The impact of bioinformatics pipelines on microbiota studies: does the analytical "microscope" affect the biological interpretation? Microorganisms, 7(10), 393. https://doi.org/10.3390/microorganisms7100393
Suchandra G., Manisha K., and Sandhya K. (2024). Exploring the gut microbiota of rural region of Haryana (India): sociodemographic, socioeconomic factors and lifestyle. Clinical Epidemiology and Global Health 30, 101806. https://doi.org/10.1016/j.cegh.2024.101806.
Thomas A. M., Manghi P., Asnicar F., Pasolli E., Armanini F., Zolfo M., et al. (2019). Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nature Medicine, 25(4), 667-678. https://doi.org/10.1038/s41591-019-0405-7
Tito R. Y., Verbandt S., Aguirre Vazquez M., Lahti L., Verspecht C., Llorens-Rico V., et al. (2024). Microbiome confounders and quantitative profiling challenge predicted microbial targets in colorectal cancer development. Nature Medicine, 30(5), 1339-1348. https://doi.org/10.1038/s41591-024-02963-2
Wirbel J., Pyl P. T., Kartal E., Zych K., Kashani A., Milanese A., et al. (2019). Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nature Medicine, 25(4), 679-689. https://doi.org/10.1038/s41591-019-0406-6
Yu J., Feng Q., Wong S. H., Zhang D., Liang Q. Y., Qin Y., et al. (2017). Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut, 66(1), 70-78. https://doi.org/10.1136/gutjnl-2015-309800
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National Foundation for Science and Technology Development
Grant numbers 108.04-2021.22
