Microbiome Analysis Reveals Universal Diagnostic Biomarkers for Colorectal Cancer Across Populations and Technologies

Overview

Journal Front Microbiol

Specialty Microbiology

Date 2022 Nov 21

PMID 36406447

Authors

Huarong Zhang

Junling Wu

Daihan Ji

Yijuan Liu

Shuting Lu

Zeman Lin

Ting Chen

Lu Ao

Affiliations

Soon will be listed here.

Abstract

The gut microbial dysbiosis is a risk of colorectal cancer (CRC) and some bacteria have been reported as potential markers for CRC diagnosis. However, heterogeneity among studies with different populations and technologies lead to inconsistent results. Here, we investigated six metagenomic profiles of stool samples from healthy controls (HC), colorectal adenoma (CA) and CRC, and six and four genera were consistently altered between CRC and HC or CA across populations, respectively. In FengQ cohort, which composed with 61 HC, 47 CA, and 46 CRC samples, a random forest (RF) model composed of the six genera, denoted as signature-HC, distinguished CRC from HC with an area under the curve (AUC) of 0.84. Similarly, another RF model composed of the four universal genera, denoted as signature-CA, discriminated CRC from CA with an AUC of 0.73. These signatures were further validated in five metagenomic sequencing cohorts and six independent 16S rRNA gene sequencing cohorts. Interestingly, three genera overlapped in the two models (, and ) were with very low abundance in HC and CA, but sharply increased in CRC. A concise RF model on the three genera distinguished CRC from HC or CA with AUC of 0.87 and 0.67, respectively. Functional gene family analysis revealed that Kyoto Encyclopedia of Genes and Genomes Orthogroups categories which were significantly correlated with markers in signature-HC and signature-CA were mapped into pathways related to lipopolysaccharide and sulfur metabolism, which might be vital risk factors of CRC development. Conclusively, our study identified universal bacterial markers across populations and technologies as potential aids in non-invasive diagnosis of CRC.

Citing Articles

Differences in diversity and composition of mucosa-associated colonic microbiota in colorectal cancer and non-colorectal cancer in Indonesia.

Darnindro N, Abdullah M, Sukartini N, Rumende C, Pitarini A, Nursyirwan S World J Gastroenterol. 2025; 31(7):100051.

PMID: 39991683 PMC: 11755252. DOI: 10.3748/wjg.v31.i7.100051.

Detection of colorectal-cancer-associated bacterial taxa in fecal samples using next-generation sequencing and 19 newly established qPCR assays.

Senthakumaran T, Tannaes T, Moen A, Brackmann S, Jahanlu D, Rounge T Mol Oncol. 2024; 19(2):412-429.

PMID: 38970464 PMC: 11793011. DOI: 10.1002/1878-0261.13700.

Quantitative and dynamic profiling of human gut core microbiota by real-time PCR.

Yan Z, Hao T, Yan Y, Zhao Y, Wu Y, Tan Y Appl Microbiol Biotechnol. 2024; 108(1):396.

PMID: 38922447 PMC: 11208268. DOI: 10.1007/s00253-024-13204-4.

GDmicro: classifying host disease status with GCN and deep adaptation network based on the human gut microbiome data.

Liao H, Shang J, Sun Y Bioinformatics. 2023; 39(12).

PMID: 38085234 PMC: 10749762. DOI: 10.1093/bioinformatics/btad747.

Potential role of gut microbes in the efficacy and toxicity of immune checkpoints inhibitors.

Ma J, Wei Q, Cheng X, Zhang J, Zhang Z, Su J Front Pharmacol. 2023; 14:1170591.

PMID: 37416062 PMC: 10320001. DOI: 10.3389/fphar.2023.1170591.

References

Lowenmark T, Lofgren-Burstrom A, Zingmark C, Eklof V, Dahlberg M, Nyunt Wai S . Parvimonas micra as a putative non-invasive faecal biomarker for colorectal cancer. Sci Rep. 2020; 10(1):15250. PMC: 7499209. DOI: 10.1038/s41598-020-72132-1. View

Thomas A, Manghi P, Asnicar F, Pasolli E, Armanini F, Zolfo M . Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nat Med. 2019; 25(4):667-678. PMC: 9533319. DOI: 10.1038/s41591-019-0405-7. View

Dai Z, Coker O, Nakatsu G, Wu W, Zhao L, Chen Z . Multi-cohort analysis of colorectal cancer metagenome identified altered bacteria across populations and universal bacterial markers. Microbiome. 2018; 6(1):70. PMC: 5896039. DOI: 10.1186/s40168-018-0451-2. View

Wu X, Qian S, Zhang J, Feng J, Luo K, Sun L . Lipopolysaccharide promotes metastasis via acceleration of glycolysis by the nuclear factor-κB/snail/hexokinase3 signaling axis in colorectal cancer. Cancer Metab. 2021; 9(1):23. PMC: 8117511. DOI: 10.1186/s40170-021-00260-x. View

Marchesi J, Dutilh B, Hall N, Peters W, Roelofs R, Boleij A . Towards the human colorectal cancer microbiome. PLoS One. 2011; 6(5):e20447. PMC: 3101260. DOI: 10.1371/journal.pone.0020447. View

Song W, Tiruthani K, Wang Y, Shen L, Hu M, Dorosheva O . Trapping of Lipopolysaccharide to Promote Immunotherapy against Colorectal Cancer and Attenuate Liver Metastasis. Adv Mater. 2018; 30(52):e1805007. PMC: 6580426. DOI: 10.1002/adma.201805007. View

Yu J, Feng Q, Wong S, Zhang D, Liang Q, Qin Y . Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut. 2015; 66(1):70-78. DOI: 10.1136/gutjnl-2015-309800. View

Ahn J, Sinha R, Pei Z, Dominianni C, Wu J, Shi J . Human gut microbiome and risk for colorectal cancer. J Natl Cancer Inst. 2013; 105(24):1907-11. PMC: 3866154. DOI: 10.1093/jnci/djt300. View

Gao R, Zhu Y, Kong C, Xia K, Li H, Zhu Y . Alterations, Interactions, and Diagnostic Potential of Gut Bacteria and Viruses in Colorectal Cancer. Front Cell Infect Microbiol. 2021; 11:657867. PMC: 8294192. DOI: 10.3389/fcimb.2021.657867. View

10.

Zackular J, Rogers M, Ruffin 4th M, Schloss P . The human gut microbiome as a screening tool for colorectal cancer. Cancer Prev Res (Phila). 2014; 7(11):1112-21. PMC: 4221363. DOI: 10.1158/1940-6207.CAPR-14-0129. View

11.

Wang T, Cai G, Qiu Y, Fei N, Zhang M, Pang X . Structural segregation of gut microbiota between colorectal cancer patients and healthy volunteers. ISME J. 2011; 6(2):320-9. PMC: 3260502. DOI: 10.1038/ismej.2011.109. View

12.

Franzosa E, McIver L, Rahnavard G, Thompson L, Schirmer M, Weingart G . Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods. 2018; 15(11):962-968. PMC: 6235447. DOI: 10.1038/s41592-018-0176-y. View

13.

Yang T, Lee W, Tu S, Huang W, Chen H, Sun T . Enterotype-based Analysis of Gut Microbiota along the Conventional Adenoma-Carcinoma Colorectal Cancer Pathway. Sci Rep. 2019; 9(1):10923. PMC: 6662695. DOI: 10.1038/s41598-019-45588-z. View

14.

Klampfer L, Huang J, Sasazuki T, Shirasawa S, Augenlicht L . Inhibition of interferon gamma signaling by the short chain fatty acid butyrate. Mol Cancer Res. 2003; 1(11):855-62. View

15.

Caporaso J, Kuczynski J, Stombaugh J, Bittinger K, Bushman F, Costello E . QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010; 7(5):335-6. PMC: 3156573. DOI: 10.1038/nmeth.f.303. View

16.

Zhang S, Kong C, Yang Y, Cai S, Li X, Cai G . Human oral microbiome dysbiosis as a novel non-invasive biomarker in detection of colorectal cancer. Theranostics. 2020; 10(25):11595-11606. PMC: 7545992. DOI: 10.7150/thno.49515. View

17.

Feng Q, Liang S, Jia H, Stadlmayr A, Tang L, Lan Z . Gut microbiome development along the colorectal adenoma-carcinoma sequence. Nat Commun. 2015; 6:6528. DOI: 10.1038/ncomms7528. View

18.

Abubucker S, Segata N, Goll J, Schubert A, Izard J, Cantarel B . Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput Biol. 2012; 8(6):e1002358. PMC: 3374609. DOI: 10.1371/journal.pcbi.1002358. View

19.

Pasolli E, Schiffer L, Manghi P, Renson A, Obenchain V, Truong D . Accessible, curated metagenomic data through ExperimentHub. Nat Methods. 2017; 14(11):1023-1024. PMC: 5862039. DOI: 10.1038/nmeth.4468. View

20.

Liu L, Liu Y, Guo X, Jin X, Yan W, Lin B . Activation of p38 mitogen-activated protein kinase pathway by lipopolysaccharide aggravates postoperative ileus in colorectal cancer patients. J Gastroenterol Hepatol. 2021; 37(3):518-530. DOI: 10.1111/jgh.15760. View