Gut metagenome, Indian colorectal cancer patients
30 faecal samples from colorectal cancer patients in Bhopal and Thrissur, India, examined for gut microbiome (bacteriome and virome) associations with CRC, including Flavonifractor plautii, as part of a case-control study. machine-written summary
30 samples · 30 runs · IN · Adult · 2019 · 1.14 Gbp / sample
Checked this project's papers and archive records yourself? Mark it human-verified.
what each source gave
| ✓ Sequence archive | 210 sample values (age, collection date, country, latitude, location locality, location region, longitude) |
| ○ Abstract / description | read; no usable statement |
| – Full text | no open-access full text linked |
| – Supplementary tables | no supplementary tables available (or none open access) |
| ✓ External resources | 150 sample values (antibiotic exposure, bmi, health condition, health condition detail, sex) · from curatedMetagenomicData 3 (cMD 3), GMrepo v3 |
| – User contribution | nothing uploaded yet |
| – Human verified | not yet checked by a person · I checked this project — verify it |
Genera, mean relative abundance over 28 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 | 30 | 100% | ||||
| country | 30 | 100% | 30 | |||
| health_condition | 30 | 100% | 30 | |||
| subject_id | 0 | 0% | ||||
| age_at_collection_days | 30 | 100% | 30 | |||
| antibiotic_exposure | 30 | 100% | 30 | |||
| bmi | 30 | 100% | 30 | |||
| collection_date | 30 | 100% | 30 | |||
| detailed_location | 30 | 100% | ||||
| health_condition_detail | 30 | 100% | 30 | |||
| latitude | 30 | 100% | 30 | |||
| location_locality | 30 | 100% | 30 | |||
| location_region | 30 | 100% | 30 | |||
| longitude | 30 | 100% | 30 | |||
| sex | 30 | 100% | 30 |
- Association of Flavonifractor plautii, a Flavonoid-Degrading Bacterium, with the Gut Microbiome of Colorectal Cancer Patients in India. 2019 PMID 31719139 PMC7407896 unsure
- Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. 2021 PMID 33944776 PMC8096432 unsure
- Meta-Analysis and Validation of a Colorectal Cancer Risk Prediction Model Using Deep Sequenced Fecal Metagenomes. 2022 PMID 36077748 PMC9454621 unsure
- Toward an improved definition of a healthy microbiome for healthy aging. 2022 PMID 37118093 PMC10154212 unsure
- Gut microbiota of healthy Asians and their discriminative features revealed by metagenomics approach. 2023 PMID 37457869 PMC10338424 unsure
12 more
- Machine learning approaches in microbiome research: challenges and best practices. 2023 PMID 37808286 PMC10556866
- NCRD: A non-redundant comprehensive database for detecting antibiotic resistance genes. 2023 PMID 37876810 PMC10590964
- Is There an Interplay between Environmental Factors, Microbiota Imbalance, and Cancer Chemotherapy-Associated Intestinal Mucositis? 2024 PMID 39204125 PMC11357004
- Pooled analysis of 3,741 stool metagenomes from 18 cohorts for cross-stage and strain-level reproducible microbial biomarkers of colorectal cancer. 2025 PMID 40461820 PMC12283368
- 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
- Multi-cohort metagenomics reveals strain functional heterogeneity and demonstrates fecal microbial load correction improves colorectal cancer diagnostic models. 2025 PMID 41070134 PMC12504321
- Phylo-Spec: a phylogeny-fusion deep learning model advances microbiome status identification. 2025 PMID 41283667 PMC12710343
- Cross-domain metabolic interactions link Methanobrevibacter smithii to colorectal cancer microbial ecosystems. 2026 PMID 41720792 PMC13035836
- Landscape of flavonoid metabolism in human gut microbiome and its association with health and disease. 2025 PMID 41909884 PMC12940152
- Instance-based transfer learning enables cross-cohort early detection of colorectal cancer. 2026 PMID 42106361 PMC13381771
- Differential co-occurrence analysis: a method to extract ecological modules from clinical microbiome data. 2026 PMID 42262136 PMC13387006
Archive description
Background</p><p>Recently, dysbiosis in the human gut microbiome and the potential role of several bacterial species are recognized as important factors in Colorectal cancer (CRC), which shows a high incidence in developed countries. In contrast, India is among the countries with very low incidence of CRC and it is likely that distinct gut microbiome attributable to the differences in diets and lifestyle in the Indian population, plays a role in health and disease dynamics within this population. Thus, we performed metagenomic and metabolomic association studies on faecal samples from 30 CRC patients and 30 healthy controls from two different locations in India, to provide insights into the role of the gut microbiome (bacteriome and virome) in CRC in Indian populations, in contrast with global CRC datasets.</p><p>Results</p><p>The association of CRC with Flavonifractor plautii in Indian patients profiled herein emerged as a novel finding, since this bacterium has not previously been associated with CRC. The plausible role of F. plautii appears to be linked to the degradation of beneficial anti-carcinogenic flavonoids, which was correlated with significant associations with the enzymes and modules involved in flavonoid degradation within Indian CRC samples. Thus, we hypothesise that the degradation of beneficial flavonoids might be playing a role in cancer progression within this Indian cohort. In addition, the known associations of Bacteroides and other species with CRC, as observed in previous studies were also confirmed. We also identified 20 additional microbial taxonomic markers and 33 microbial gene markers that discriminated the Indian CRC from healthy microbiomes with high accuracy based on machine learning approaches.</p><p>Conclusion</p><p>This study reveals novel insights on the CRC-associated microbiome of a unique cohort of India, the potential role of a new bacterium in CRC, and identifies cohort-specific biomarkers, which can potentially be used in non-invasive diagnosis of CRC.
How this project entered the registry
Classified as human gut / stool shotgun DNA metagenome of adult and elderly by sonnet_x2 with confidence 0.75. Assay shotgun_dna.
- host run.host_tax_id · “9606”
- body site run.scientific_name · “human gut metagenome”
- life stage sample.attr.host_age · “58 (n=19/30)”
- life stage sample.attr.host_age · “65 (n=9/30)”
Evidence examples (one per field × route)
| sample | field | value | route | conf. | source | quote |
|---|---|---|---|---|---|---|
| SAMN11355250 | age_at_collection_days | 22627.0 | R1 | 0.85 | sample.attr.host_age | 62 |
| SAMN11355250 | antibiotic_exposure | no | R2 | 0.75 | external.cmd source | antibiotics_current_use: No |
| SAMN11355250 | bmi | 20.08 | R2 | 0.75 | external.cmd source | bmi: 20.08 |
| SAMN11355250 | collection_date | 2016-11-21 | R1 | 0.90 | biosample.attribute:collection_date | 2016-11-21 |
| SAMN11355250 | country | IN | R1 | 0.90 | sample.attr.geo_loc_name | India:Thrissur |
| SAMN11355250 | health_condition | colorectal_cancer | R2 | 0.75 | external.gmrepo source | phenotype: Colorectal Neoplasms |
| SAMN11355250 | health_condition_detail | Colorectal Neoplasms | R2 | 0.75 | external.gmrepo source | phenotype: Colorectal Neoplasms |
| SAMN11355250 | latitude | 10.5200 | R1 | 0.90 | biosample.attribute:lat_lon | 10.52 N 76.21 E |
| SAMN11355250 | location_locality | Thrissur | R1 | 0.85 | biosample.attribute:geo_loc_name | India:Thrissur |
| SAMN11355250 | location_region | Kerala | R1 | 0.60 | biosample.attribute:lat_lon | 10.52 N 76.21 E |
| SAMN11355250 | longitude | 76.2100 | R1 | 0.90 | biosample.attribute:lat_lon | 10.52 N 76.21 E |
| SAMN11355250 | sex | male | R2 | 0.75 | external.cmd source | sex: Male |
Sequencing
30 runs over 30 samples · mean 1.27 Gbp per run (median 1.14 Gbp, total 37.98 Gbp; 30 runs with a base count) · layout PAIRED 30 · platform ILLUMINA 30 · models NextSeq 500 (30) · 93% of runs with a Sandpiper profile · first public 2019-10-02
Authors (45)
Mao Y first · Liu X · Zhang N · Wang Z · Han M last · Fernandes C first · Miranda MCC · Roque CR · Paguada ALP · Mota CAR · Florêncio KGD · Pereira AF · Wong DVT · Oriá RB · Lima-Júnior RCP last · Zhang J first · Meng F · Sun Y · Xu W · Wu S · Su X last · Mohammadzadeh R first · Mahnert A · Zurabishvili T · Wink L · Kumpitsch C · Habisch H · Sprengel J · Filek K · Mertelj P · Pernitsch D · Hingerl K · Durdevic M · Gorkiewicz G · Diener C · Loy A · Kolb D · Trautwein C · Madl T · Moissl-Eichinger C last · Iacovacci J first · Cannon N · McCulloch JA · Rancati T · Trinchieri G last
Downloads
PRJNA531273.csv.gz (2.1 KB) · parquet (72.4 KB) — all 30 samples, every field with route and confidence · PRJNA531273_determinations.csv.gz (4.4 KB) — 360 evidence rows