Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49. https://doi.org/10.3322/caac.21660.

Article  PubMed  Google Scholar 

Kang Y, Cai Y, Yang Y. The gut microbiome and hepatocellular carcinoma: implications for early diagnostic biomarkers and novel therapies. Liver Cancer. 2022;11:113–25. https://doi.org/10.1159/000521358.

Article  CAS  PubMed  Google Scholar 

Zhang HL, Yu LX, Yang W, Tang L, Lin Y, Wu H, et al. Profound impact of gut homeostasis on chemically-induced pro-tumorigenic inflammation and hepatocarcinogenesis in rats. J Hepatol. 2012;57:803–12. https://doi.org/10.1016/j.jhep.2012.06.011.

Article  PubMed  Google Scholar 

Dapito DH, Mencin A, Gwak G-Y, Pradere J-P, Jang M-K, Mederacke I, et al. Promotion of hepatocellular carcinoma by the intestinal microbiota and TLR4. Cancer Cell. 2012;21:504–16. https://doi.org/10.1016/j.ccr.2012.02.007.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lelouvier B, Servant F, Païssé S, Brunet A-C, Benyahya S, Serino M, et al. Changes in blood microbiota profiles associated with liver fibrosis in obese patients: a pilot analysis. Hepatology. 2016;64:2015–27. https://doi.org/10.1002/hep.28829.

Article  CAS  PubMed  Google Scholar 

Puri P, Liangpunsakul S, Christensen JE, Shah VH, Kamath PS, Gores GJ, et al. The circulating microbiome signature and inferred functional metagenomics in alcoholic hepatitis. Hepatology. 2018;67:1284–302. https://doi.org/10.1002/hep.29623.

Article  CAS  PubMed  Google Scholar 

Cho EJ, Leem S, Kim SA, Yang J, Lee YB, Kim SS, et al. Circulating microbiota-based metagenomic signature for detection of hepatocellular carcinoma. Sci Rep. 2019;9:7536. https://doi.org/10.1038/s41598-019-44012-w.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Whiteside SA, Razvi H, Dave S, Reid G, Burton JP. The microbiome of the urinary tract—a role beyond infection. Nat Rev Urol. 2015;12:81–90. https://doi.org/10.1038/nrurol.2014.361.

Article  PubMed  Google Scholar 

Wang H, Altemus J, Niazi F, Green H, Calhoun BC, Sturgis C, et al. Breast tissue, oral and urinary microbiomes in breast cancer. Oncotarget. 2017;8:88122–38. https://doi.org/10.18632/oncotarget.21490.

Article  PubMed  PubMed Central  Google Scholar 

Park J-Y, Kang C-S, Seo H-C, Shin J-C, Kym S-M, Park Y-S, et al. Bacteria-derived extracellular vesicles in urine as a novel biomarker for gastric cancer: integration of liquid biopsy and metagenome analysis. Cancers. 2021;13:4687. https://doi.org/10.3390/cancers13184687. PubMed PMID

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, Ban H-J, et al. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet. 2009;41:527–34. https://doi.org/10.1038/ng.357.

Article  CAS  PubMed  Google Scholar 

Kim K, Lee S, Park S-C, Kim N-E, Shin C, Lee SK, et al. Role of an unclassified Lachnospiraceae in the pathogenesis of type 2 diabetes: a longitudinal study of the urine microbiome and metabolites. Exp Mol Med. 2022;54:1125–32. https://doi.org/10.1038/s12276-022-00816-x.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Heimbach JK, Kulik LM, Finn RS, Sirlin CB, Abecassis MM, Roberts LR, et al. AASLD guidelines for the treatment of hepatocellular carcinoma. Hepatology. 2018;67:358–80. https://doi.org/10.1002/hep.29086.

Article  PubMed  Google Scholar 

Galle PR, Forner A, Llovet JM, Mazzaferro V, Piscaglia F, Raoul J-L, et al. EASL clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol. 2018;69:182–236. https://doi.org/10.1016/j.jhep.2018.03.019.

Article  Google Scholar 

Lee J-E, Kim J-H, Hong E-J, Yoo HS, Nam H-Y, Park O. National Biobank of Korea: quality control programs of collected-human biospecimens. Osong Public Health Res Perspect. 2012;3:185–9. https://doi.org/10.1016/j.phrp.2012.07.007.

Article  PubMed  PubMed Central  Google Scholar 

Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7. https://doi.org/10.1038/s41587-019-0209-9.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–2.

Article  Google Scholar 

Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3. https://doi.org/10.1038/nmeth.3869.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.

Google Scholar 

Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–D6. https://doi.org/10.1093/nar/gks1219.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Oksanen J, Blanchet F, Friendly M, Kindt R, Legendre P, Mcglinn D, et al. Vegan: community ecology package. Ordination methods, diversity analysis and other functions for community and vegetation ecologists, 05–26. Version 2.5-1. 2019.

Zhang Y, Parmigiani G, Johnson WE. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom Bioinform. 2020;2. https://doi.org/10.1093/nargab/lqaa078.

Paulson JN, Stine OC, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nat Methods. 2013;10:1200–2. https://doi.org/10.1038/nmeth.2658.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis. 2015;26:27663. https://doi.org/10.3402/mehd.v26.27663.

Article  PubMed  Google Scholar 

White JR, Nagarajan N, Pop M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLOS Comput Biol. 2009;5:e1000352. https://doi.org/10.1371/journal.pcbi.1000352.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lee C, Lee S, Park T, editors. A comparison study of statistical methods for the analysis metagenome data. In: Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 2017.

Peng X, Li G, Liu Z. Zero-inflated beta regression for differential abundance analysis with metagenomics data. J Comput Biol. 2015;23:102–10. https://doi.org/10.1089/cmb.2015.0157.

Article  CAS  PubMed  Google Scholar 

Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. https://doi.org/10.1186/s13059-014-0550-8.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2009;26:139–40. https://doi.org/10.1093/bioinformatics/btp616.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol). 1995;57:289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.

Article  Google Scholar 

Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 2011;12:77. https://doi.org/10.1186/1471-2105-12-77.

Article  Google Scholar 

Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:1–22. Epub 2010/09/03. PubMed PMID: 20808728; PubMed Central PMCID: PMCPMC2929880

Article  PubMed  PubMed Central  Google Scholar 

James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning. Springer, 2013.

Kim JR, Han K, Han Y, Kang N, Shin T-S, Park HJ, et al. Microbiome markers of pancreatic cancer based on bacteria-derived extracellular vesicles acquired from blood samples: a retrospective propensity score matching analysis. Biology [Internet]. 2021;10:219. p

Article  CAS  PubMed  Google Scholar 

Dinges SS, Hohm A, Vandergrift LA, Nowak J, Habbel P, Kaltashov IA, et al. Cancer metabolomic markers in urine: evidence, techniques and recommendations. Nat Rev Urol. 2019;16:339–62. https://doi.org/10.1038/s41585-019-0185-3.

Article  PubMed  Google Scholar 

Zhao Y, Li Y, Liu W, Xing S, Wang D, Chen J, et al. Identification of noninvasive diagnostic biomarkers for hepatocellular carcinoma by urinary proteomics. J Proteom. 2020;225:103780 https://doi.org/10.1016/j.jprot.2020.103780.

Article  CAS  Google Scholar 

Kim AK, Hamilton JP, Lin SY, Chang T-T, Hann H-W, Hu C-T, et al. Urine DNA biomarkers for hepatocellular carcinoma screening. Br J Cancer. 2022;126:1432–8. https://doi.org/10.1038/s41416-022-01706-9.

Article  CAS  PubMed  PubMed Central  Google Scholar