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"accession": "MGYS00005190",
"bioproject": "PRJNA396815",
"samples-count": 1993,
"is-private": false,
"last-update": "2020-02-18T13:08:42",
"secondary-accession": "SRP136041",
"centre-name": "Hangzhou Guhe Information and Technology Company",
"public-release-date": null,
"study-abstract": "Objective To establish methods and effective microbial biomarkers for early colorectal cancer (CRC)-specific-screening via gut microbiota detection in the clinic.Design 5,588 stool samples 16S ribosomal RNA (rRNA) gene were sequenced, and used the relative abundance of taxonomic and functional features to develop a model resembling the populations that consisted of 3 cohorts and 3 classification models (RF, XGBoost, and GBM) that distinguished the CRC group from the control group and identified potential microbial biomarkers.Results Microbiome-based classification distinguished patients with CRC from normal participants and excluded other CRC-relevant diseases. The area under the receiver operator characteristic (AUROC) curve was 92.2%. Known associations with oral pathogenic features, benefits-generated features, and functional features of CRC were confirmed by the model.Conclusions Our optimized prediction model was established using large-scale experimental population-based data and other sequence-based faecal microbial community data. This model can be used to identify high-risk groups and has the potential to become a novel screening method for CRC biomarkers due to its low 1-specificity (i.e. false-positive rate) and good stability.",
"study-name": "An optimized model for screening colorectal cancer established using large-scale experimental sequence-based faecal microbial community data",
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