Colorectal cancer virome and bacteriome, case-control
181 stool samples from Toronto, Houston, Ann Arbor, and Boston, comparing healthy controls, colorectal adenoma, and cancer groups using 16S and metagenomic sequencing in a case-control design. machine-written summary
181 samples · 181 runs · US, CA · Age unknown · 2017 · 0.63 Gbp / sample
Checked this project's papers and archive records yourself? Mark it human-verified.
what each source gave
| ✓ Sequence archive | 893 sample values (country, health condition, health condition detail, location locality, subject id) |
| ○ Abstract / description | read; no usable statement |
| ○ Full text | read; no usable statement or table · PMC11020464 |
| ○ Supplementary tables | files read; no table could be matched to the samples |
| ✓ External resources | 335 sample values (age, antibiotic exposure, bmi, health condition, sex) · from curatedMetagenomicData 3 (cMD 3), HumanMetagenomeDB |
| – User contribution | nothing uploaded yet |
| – Human verified | not yet checked by a person · I checked this project — verify it |
longitudinal — not resolved
82 of 99 people sampled more than once (164 samples; median 2, up to 2 per person). Schedule stated by the source: “subset of adenoma/carcinoma patients resampled ~1 year after treatment (median 255 days, IQR 233-334 days)” (time origin: intervention_start); stated follow-up 8 mo.
Genera, mean relative abundance over 135 profiled gut samples of this project (Sandpiper SingleM profiles, GTDB R232; low-depth and non-metagenome profiles left out).
| field | samples | coverage | R1 | R2 | R3 | R4 |
|---|---|---|---|---|---|---|
| age_category | 77 | 43% | ||||
| country | 181 | 100% | 181 | |||
| health_condition | 181 | 100% | 169 | 12 | ||
| subject_id | 181 | 100% | 181 | |||
| detailed_location | 181 | 100% | ||||
| health_condition_detail | 181 | 100% | 181 | |||
| location_locality | 181 | 100% | 181 | |||
| age_at_collection_days | 81 | 45% | 81 | |||
| antibiotic_exposure | 81 | 45% | 81 | |||
| sex | 81 | 45% | 81 | |||
| bmi | 80 | 44% | 80 |
- Diagnostic Potential and Interactive Dynamics of the Colorectal Cancer Virome. 2018 PMID 30459201 PMC6247079 own_data
- Fecal Short-Chain Fatty Acids Are Not Predictive of Colonic Tumor Status and Cannot Be Predicted Based on Bacterial Community Structure. 2019 PMID 31266879 PMC6606814 own_data
- Benchmarking bioinformatic virus identification tools using real-world metagenomic data across biomes. 2024 PMID 38622738 PMC11020464 own_data
- Identification of microbial markers across populations in early detection of colorectal cancer. 2021 PMID 34031391 PMC8144394 unsure
- Species-Level Analysis of the Human Gut Microbiome Shows Antibiotic Resistance Genes Associated With Colorectal Cancer. 2021 PMID 34975790 PMC8715872 unsure
22 more
- Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer. 2022 PMID 35013454 PMC8748837
- Evaluating supervised and unsupervised background noise correction in human gut microbiome data. 2022 PMID 35130266 PMC8853548
- Increasing prediction performance of colorectal cancer disease status using random forests classification based on metagenomic shotgun sequencing data. 2022 PMID 35155839 PMC8801753
- Diversity and distribution of sulfur metabolic genes in the human gut microbiome and their association with colorectal cancer. 2022 PMID 35440042 PMC9016944
- Identification and Characterization of Major Bile Acid 7α-Dehydroxylating Bacteria in the Human Gut. 2022 PMID 35736002 PMC9426597
- Meta-analysis of fecal viromes demonstrates high diagnostic potential of the gut viral signatures for colorectal cancer and adenoma risk assessment. 2023 PMID 36198381 PMC10334131
- Toward an improved definition of a healthy microbiome for healthy aging. 2022 PMID 37118093 PMC10154212
- Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer. 2023 PMID 37635357 PMC10464540
- Machine learning approaches in microbiome research: challenges and best practices. 2023 PMID 37808286 PMC10556866
- Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies. 2023 PMID 37844077 PMC10602384
- The physical biogeography of <i>Fusobacterium nucleatum</i> in health and disease. 2025 PMID 40062772 PMC11980382
- Characterization of the phyllosphere virome of fresh vegetables and potential transfer to the human gut. 2025 PMID 40210629 PMC11986028
- Transmission of antimicrobial resistance genes from the environment to human gut is more pronounced in colorectal cancer patients than in healthy subjects. 2025 PMID 40236771 PMC11995172
- Individualized metagenomic network model for colorectal cancer diagnosis: insights into viral regulation of gut microecology. 2025 PMID 40462511 PMC12133679
- Megasphaera Elesdenii Dysregulates Colon Epithelial Homeostasis, Aggravates Colitis-Associated Tumorigenesis. 2025 PMID 40801433 PMC12591162
- Phylo-Spec: a phylogeny-fusion deep learning model advances microbiome status identification. 2025 PMID 41283667 PMC12710343
- Deciphering the personalized functional redundancy hierarchy in the gut microbiome. 2025 PMID 41345980 PMC12781464
- Cross-domain metabolic interactions link Methanobrevibacter smithii to colorectal cancer microbial ecosystems. 2026 PMID 41720792 PMC13035836
- Instance-based transfer learning enables cross-cohort early detection of colorectal cancer. 2026 PMID 42106361 PMC13381771
- Gut fungal signatures in colorectal cancer and their potential for supporting diagnosis: a multi-cohort metagenomic analysis. 2026 PMID 42271362 PMC13281578
- Obesity-driven microbial GABA depletion promotes metabolic rewiring and colorectal cancer progression. 2026 PMID 42503520 PMC13402817
- Pangenome analysis reveals both niche-specific "specialists" and microbial "side hustlers" in colorectal cancer microbiomes. 2026 PMID 42740607 PMC13580483
Archive description
Colorectal cancer is the second leading cause of cancer-related death in the United States and is a primary cause of morbidity and mortality throughout the world. Its risk has been linked to changes in colonic bacterial community composition. Viruses are another important component of the colonic microbial community, however they have yet to be studied in colorectal cancer despite their oncogenic potential. We evaluated the colorectal cancer virome (virus community) in stool using a cohort of 90 human subjects with either healthy, adenomatous (precancerous), or cancerous colons. We utilized 16S rRNA gene, whole shotgun metagenomic, and purified virus metagenomic sequencing methods to compare the colorectal cancer virome to the bacterial community. We identified no detectable difference in diversity (alpha or beta) between healthy, adenomatous, or cancerous colonic samples, but more sophisticated random forest models identified striking changes in the virus community. The majority of the cancer-associated virome consisted of temperate bacteriophages, suggesting that the community was indirectly linked to colorectal cancer by modulating bacterial community structure and function. Our data suggested that the influential phages did not exclusively infect influential bacteria, but rather acted through the community as a whole. These results provide foundational evidence that bacteriophage communities are associated with colorectal cancer and likely impact cancer progression by altering the bacterial host communities.
How this project entered the registry
Classified as human gut / stool shotgun DNA metagenome of age unknown by sonnet_x2 with confidence 0.70. Assay shotgun_dna.
- host study.description · “cohort of 90 human subjects”
- body site study.description · “colorectal cancer virome (virus community) in stool”
Evidence examples (one per field × route)
| sample | field | value | route | conf. | source | quote |
|---|---|---|---|---|---|---|
| SAMN07211143 | age_at_collection_days | 25567.5 | R2 | 0.75 | external.cmd source | age: 70; age_unit: Year; age_group: Elderly |
| SAMN07211143 | antibiotic_exposure | no | R2 | 0.75 | external.cmd source | antibiotics_current_use: No |
| SAMN07211143 | bmi | 23.31 | R2 | 0.75 | external.cmd source | bmi: 23.30668005 |
| SAMN07211058 | country | CA | R1 | 0.90 | sample.attr.geo_loc_name | Canada:Toronto |
| SAMN07211058 | health_condition | colorectal_adenoma | R1 | 0.90 | biosample_attr | Adenoma |
| SAMN07211227 | health_condition | healthy_control | R2 | 0.70 | external.hmgdb source | HMgDB Diagnosis=control; HMgDB Control=cancer control |
| SAMN07211058 | health_condition_detail | Adenoma | R1 | 0.90 | sample.attr.diseaseclass | Adenoma |
| SAMN07211058 | location_locality | Toronto | R1 | 0.85 | biosample.attribute:geo_loc_name | Canada:Toronto |
| SAMN07211143 | sex | male | R2 | 0.75 | external.cmd source | sex: Male |
| SAMN07211058 | subject_id | A01 | R1 | 0.85 | sample.attr.subjectid | A01 |
Sequencing
181 runs over 181 samples · mean 0.73 Gbp per run (median 0.63 Gbp, total 131.61 Gbp; 181 runs with a base count) · layout PAIRED 181 · platform ILLUMINA 181 · models Illumina HiSeq 4000 (181) · 79% of runs with a Sandpiper profile · first public 2017-06-11
Authors (52)
Argentini C first · Lugli GA · Tarracchini C · Fontana F · Mancabelli L · Viappiani A · Anzalone R · Angelini L · Alessandri G · Longhi G · Bianchi MG · Taurino G · Bussolati O · Milani C · van Sinderen D · Turroni F · Ventura M last · Wu LY first · Wijesekara Y · Piedade GJ · Pappas N · Brussaard CPD · Dutilh BE last · Zhang J first · Meng F · Sun Y · Xu W · Wu S · Su X last · Jiang Y first · Che L · Li SC last · Zhang Z first · Lu T · Dong B · Liu J · Zhang Y · Li S · Liu H · Li X · Guan T · Guo H · Yan Q · Lei Z · Yu X · Wang L · Kang J · Li L · Zhao D last · Lee J first · Minot S · Dey N last
Downloads
PRJNA389927.csv.gz (6.4 KB) · parquet (78.3 KB) — all 181 samples, every field with route and confidence · PRJNA389927_determinations.csv.gz (11.8 KB) — 1,228 evidence rows