Browse
Submit Data
Databases
API
Help

Dataset Information

23 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Characterization of the consensus mucosal microbiome of colorectal cancer.

ABSTRACT: Dysbioisis is an imbalance of an organ's microbiome and plays a role in colorectal cancer pathogenesis. Characterizing the bacteria in the microenvironment of a cancer through genome sequencing has advantages compared to culture-based profiling. However, there are notable technical and analytical challenges in characterizing universal features of tumor microbiomes. Colorectal tumors demonstrate microbiome variation among different studies and across individual patients. To address these issues, we conducted a computational study to determine a consensus microbiome for colorectal cancer, analyzing 924 tumors from eight independent RNA-Seq data sets. A standardized meta-transcriptomic analysis pipeline was established with quality control metrics. Microbiome profiles across different cohorts were compared and recurrently altered microbial shifts specific to colorectal cancer were determined. We identified cancer-specific set of 114 microbial species associated with tumors that were found among all investigated studies. Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria were among the four most abundant phyla for the colorectal cancer microbiome. Member species of Clostridia were depleted and Fusobacterium nucleatum was one of the most enriched bacterial species in tumors. Associations between the consensus species and specific immune cell types were noted. Our results are available as a web data resource for other researchers to explore (https://crc-microbiome.stanford.edu).

SUBMITTER: Zhao L

PROVIDER: S-EPMC8693571 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Differential Mucosal Microbiome Profiles across Stages of Human Colorectal Cancer.

Project description:Emerging evidences link gut microbiota to colorectal cancer (CRC) initiation and development. However, the CRC stage- and spatial-specific bacterial taxa were less investigated, especially in a Chinese cohort, leading to our incomplete understanding of the functional roles of gut microbiota in promoting CRC progression and recurrence. Here, we report the composition and structure of gut microbiota across CRC stages I, II and III, by analyzing the gut mucosal microbiomes of 75 triplet-paired samples collected from on-tumor, adjacent-tumor and off-tumor sites and 26 healthy controls. We observed tumor-specific pattern of mucosal microbiome profiles as CRC progressed and identified ten bacterial taxa with high abundances (>1%) as potential biomarkers for tumor initiation and development. Peptostreptococcus and Parvimonas can serve as biomarkers for CRC stage I. Fusobacterium, Streptococcus, Parvimonas, Burkholderiales, Caulobacteraceae, Delftia and Oxalobacteraceae can serve as biomarkers for CRC stage II, while Fusobacterium, Burkholderiales, Caulobacteraceae, Oxalobacteraceae, Faecalibacterium and Sutterella can serve as biomarkers for CRC stage III. These biomarkers classified CRC stages I, II and III distinguished from each other with an area under the receiver-operating curve (AUC) > 0.5. Moreover, co-occurrence and co-excluding network analysis of these genera showed strong correlations in CRC stage I, which were subsequently reduced in CRC stages II and III. Our findings provide a reference index for stage-specific CRC diagnosis and suggest stage-specific roles of Peptostreptococcus, Fusobacterium, Streptococcus and Parvimonas in driving CRC progression.

| S-EPMC8401903 | biostudies-literature

Distinct gut microbiome patterns associate with consensus molecular subtypes of colorectal cancer.

Project description:Colorectal cancer (CRC) is a heterogeneous disease and recent advances in subtype classification have successfully stratified the disease using molecular profiling. The contribution of bacterial species to CRC development is increasingly acknowledged, and here, we sought to analyse CRC microbiomes and relate them to tumour consensus molecular subtypes (CMS), in order to better understand the relationship between bacterial species and the molecular mechanisms associated with CRC subtypes. We classified 34 tumours into CRC subtypes using RNA-sequencing derived gene expression and determined relative abundances of bacterial taxonomic groups using 16S rRNA amplicon metabarcoding. 16S rRNA analysis showed enrichment of Fusobacteria and Bacteroidetes, and decreased levels of Firmicutes and Proteobacteria in CMS1. A more detailed analysis of bacterial taxa using non-human RNA-sequencing reads uncovered distinct bacterial communities associated with each molecular subtype. The most highly enriched species associated with CMS1 included Fusobacterium hwasookii and Porphyromonas gingivalis. CMS2 was enriched for Selenomas and Prevotella species, while CMS3 had few significant associations. Targeted quantitative PCR validated these findings and also showed an enrichment of Fusobacterium nucleatum, Parvimonas micra and Peptostreptococcus stomatis in CMS1. In this study, we have successfully associated individual bacterial species to CRC subtypes for the first time.

| S-EPMC5599497 | biostudies-literature

Gut mucosal microbiome across stages of colorectal carcinogenesis.

Project description:Gut microbial dysbiosis contributes to the development of colorectal cancer (CRC). Here we catalogue the microbial communities in human gut mucosae at different stages of colorectal tumorigenesis. We analyse the gut mucosal microbiome of 47 paired samples of adenoma and adenoma-adjacent mucosae, 52 paired samples of carcinoma and carcinoma-adjacent mucosae and 61 healthy controls. Probabilistic partitioning of relative abundance profiles reveals that a metacommunity predominated by members of the oral microbiome is primarily associated with CRC. Analysis of paired samples shows differences in community configurations between lesions and the adjacent mucosae. Correlations of bacterial taxa indicate early signs of dysbiosis in adenoma, and co-exclusive relationships are subsequently more common in cancer. We validate these alterations in CRC-associated microbiome by comparison with two previously published data sets. Our results suggest that a taxonomically defined microbial consortium is implicated in the development of CRC.

| S-EPMC4640069 | biostudies-literature

16S rRNA of Mucosal Colon Microbiome and CCL2 Circulating Levels Are Potential Biomarkers in Colorectal Cancer.

Project description:Colorectal cancer (CRC) is one of the most common malignancies in the Western world and intestinal dysbiosis might contribute to its pathogenesis. The mucosal colon microbiome and C-C motif chemokine 2 (CCL2) were investigated in 20 healthy controls (HC) and 20 CRC patients using 16S rRNA sequencing and immunoluminescent assay, respectively. A total of 10 HC subjects were classified as overweight/obese (OW/OB_HC) and 10 subjects were normal weight (NW_HC); 15 CRC patients were classified as OW/OB_CRC and 5 patients were NW_CRC. Results: Fusobacterium nucleatum and Escherichia coli were more abundant in OW/OB_HC than in NW_HC microbiomes. Globally, Streptococcus intermedius, Gemella haemolysans, Fusobacterium nucleatum, Bacteroides fragilis and Escherichia coli were significantly increased in CRC patient tumor/lesioned tissue (CRC_LT) and CRC patient unlesioned tissue (CRC_ULT) microbiomes compared to HC microbiomes. CCL2 circulating levels were associated with tumor presence and with the abundance of Fusobacterium nucleatum, Bacteroides fragilis and Gemella haemolysans. Our data suggest that mucosal colon dysbiosis might contribute to CRC pathogenesis by inducing inflammation. Notably, Fusobacterium nucleatum, which was more abundant in the OW/OB_HC than in the NW_HC microbiomes, might represent a putative link between obesity and increased CRC risk.

| S-EPMC8509685 | biostudies-literature

Mucosal microbiome associates with progression to gastric cancer.

Project description:Background & Aims: Dysbiosis is associated with gastric cancer (GC) development. However, no longitudinal study was carried out to identify key bacteria that could predict for GC progression. Here, we aimed to investigate changes in bacterial metagenome prior to GC and develop a microbiome-based predictive model to accurately classify patients at risk of GC. Methods: Bacterial 16S rDNA was sequenced from 89 gastric antral biopsies obtained from 43 participants. This study was nested in a prospective, longitudinal study, whereby study participants underwent screening gastroscopy, with further 1-2 yearly surveillance gastroscopies for at least 5 years. Putative bacterial taxonomic and functional features associated with GC carcinogenesis were identified by comparing between controls, patients with gastric intestinal metaplasia (IM) and patients with early gastric neoplasia (EGN). Results: Patients with EGN had enrichment of Proteobacteria (in particular Proteus genus) and depletion of Bacteroidetes (in particular S24-7 family) in their gastric mucosa. Sequencing identified more patients with Helicobacter pylori compared to histopathological assessment, while H. pylori was also significantly enriched in EGN. Furthermore, a total of 261 functional features, attributing to 97 KEGG pathways were differentially abundant at baseline between patients who subsequent developed EGN (n = 13/39) and those who did not. At the same time, a constellation of six microbial taxonomic features present at baseline, provided the highest classifying power for subsequent EGN (AUC = 0.82). Conclusion: Our study highlights early microbial changes associated with GC carcinogenesis, suggesting a potential role for prospective microbiome surveillance for GC.

| S-EPMC8690935 | biostudies-literature

The consensus molecular subtypes of colorectal cancer.

Project description:Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression-based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features: CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable and strong immune activation; CMS2 (canonical, 37%), epithelial, marked WNT and MYC signaling activation; CMS3 (metabolic, 13%), epithelial and evident metabolic dysregulation; and CMS4 (mesenchymal, 23%), prominent transforming growth factor-? activation, stromal invasion and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intratumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC-with clear biological interpretability-and the basis for future clinical stratification and subtype-based targeted interventions.

| S-EPMC4636487 | biostudies-literature

Eastern Canadian Colorectal Cancer Consensus Conference 2017.

Project description:The annual Eastern Canadian Gastrointestinal Cancer Consensus Conference 2017 was held in St. John's, Newfoundland and Labrador, 28-30 September. Experts in radiation oncology, medical oncology, surgical oncology, and cancer genetics who are involved in the management of patients with gastrointestinal malignancies participated in presentations and discussion sessions for the purpose of developing the recommendations presented here. This consensus statement addresses multiple topics in the management of gastric, rectal, and colon cancer, including ? identification and management of hereditary gastric and colorectal cancer (crc);? palliative systemic therapy for metastatic gastric cancer;? optimum duration of preoperative radiation in rectal cancer-that is, short- compared with long-course radiation;? management options for peritoneal carcinomatosis in crc;? implications of tumour location for treatment and prognosis in crc; and? new molecular markers in crc.

| S-EPMC6092057 | biostudies-literature

A prospective analysis of mucosal microbiome-metabonome interactions in colorectal cancer using a combined MAS 1HNMR and metataxonomic strategy.

Project description:Colon cancer induces a state of mucosal dysbiosis with associated niche specific changes in the gut microbiota. However, the key metabolic functions of these bacteria remain unclear. We performed a prospective observational study in patients undergoing elective surgery for colon cancer without mechanical bowel preparation (n = 18). Using 16 S rRNA gene sequencing we demonstrated that microbiota ecology appears to be cancer stage-specific and strongly associated with histological features of poor prognosis. Fusobacteria (p < 0.007) and ε- Proteobacteria (p < 0.01) were enriched on tumour when compared to adjacent normal mucosal tissue, and fusobacteria and β-Proteobacteria levels increased with advancing cancer stage (p = 0.014 and 0.002 respecitvely). Metabonomic analysis using 1H Magic Angle Spinning Nuclear Magnetic Resonsance (MAS-NMR) spectroscopy, demonstrated increased abundance of taurine, isoglutamine, choline, lactate, phenylalanine and tyrosine and decreased levels of lipids and triglycerides in tumour relative to adjacent healthy tissue. Network analysis revealed that bacteria associated with poor prognostic features were not responsible for the modification of the cancer mucosal metabonome. Thus the colon cancer mucosal microbiome evolves with cancer stage to meet the demands of cancer metabolism. Passenger microbiota may play a role in the maintenance of cancer mucosal metabolic homeostasis but these metabolic functions may not be stage specific.

| S-EPMC5566496 | biostudies-literature

Consensus molecular subtype transition during progression of colorectal cancer

Project description:This SuperSeries is composed of the SubSeries listed below.

2023-07-13 | GSE237249 | GEO

Immunological landscape of consensus clusters in colorectal cancer.

Project description:Recent, large-scale expression-based subtyping has advanced our understanding of the genomic landscape of colorectal cancer (CRC) and resulted in a consensus molecular classification that enables the categorization of most CRC tumors into one of four consensus molecular subtypes (CMS). Currently, major progress in characterization of immune landscape of tumor-associated microenvironment has been made especially with respect to microsatellite status of CRCs. While these studies profoundly improved the understanding of molecular and immunological profile of CRCs heterogeneity less is known about repertoire of the tumor infiltrating immune cells of each CMS. In order to comprehensively characterize the immune landscape of CRC we re-analyzed a total of 15 CRC genome-wide expression data sets encompassing 1597 tumors and 125 normal adjacent colon tissues. After quality filtering, CRC clusters were discovered using a combination of multiple clustering algorithms and multiple validity metrics. CIBERSORT algorithm was used to compute relative proportions of 22 human leukocyte subpopulations across CRC clusters and normal colon tissue. Subsequently, differential expression specific to tumor epithelial cells was calculated to characterize mechanisms of tumor escape from immune surveillance occurring in particular CRC clusters. Our results not only characterize the common and cluster-specific influx of immune cells into CRCs but also identify several deregulated gene targets that may contribute to improvement of immunotherapeutic strategies in CRC.

| S-EPMC5739639 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data