Altered fecal small RNA profiles in colorectal cancer reflect gut microbiome composition in stool samples
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA546574 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR9204280.fastq.gz | Fastqsanger.gz | |||
| SRR9204290.fastq.gz | Fastqsanger.gz | |||
| SRR9204300.fastq.gz | Fastqsanger.gz | |||
| SRR9204310.fastq.gz | Fastqsanger.gz | |||
| SRR9204320.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 80 |
Similar Datasets
Project description:Dysbiotic configurations of the human gut microbiota have been linked with colorectal cancer (CRC). Human small non-coding RNAs are also implicated in CRC and recent findings suggest that their release in the gut lumen contributes to shape the gut microbiota. Bacterial small RNAs (bsRNAs) may also play a role in carcinogenesis but their role is less explored. Here, we performed small RNA and shotgun sequencing on 80 stool specimens of patients with CRC, or adenomas, and healthy subjects collected in a cross-sectional study to evaluate their combined use as a predictive tool for disease detection. We reported a considerable overlap and correlation between metagenomic and bsRNA quantitative taxonomic profiles obtained from the two approaches. Furthermore, we identified a combined predictive signature composed by 32 features from human and microbial small RNAs and DNA-based microbiome able to accurately classify CRC from healthy and adenoma samples (AUC= 0.87). In summary we reported evidence that host-microbiome dysbiosis in CRC can be observed also by altered small RNA stool profiles. Integrated analyses of the microbiome and small RNAs in the human stool may provide insights for designing more accurate tools for diagnostic purposes.
2019-08-30 | GSE132236 | GEO
Project description:Dysbiotic configurations of the human gut microbiota have been linked to colorectal cancer (CRC). Human small noncoding RNAs are also implicated in CRC, and recent findings suggest that their release in the gut lumen contributes to shape the gut microbiota. Bacterial small RNAs (bsRNAs) may also play a role in carcinogenesis, but their role has been less extensively explored. Here, we performed small RNA and shotgun sequencing on 80 stool specimens from patients with CRC or with adenomas and from healthy subjects collected in a cross-sectional study to evaluate their combined use as a predictive tool for disease detection. We observed considerable overlap and a correlation between metagenomic and bsRNA quantitative taxonomic profiles obtained from the two approaches. We identified a combined predictive signature composed of 32 features from human and microbial small RNAs and DNA-based microbiome able to accurately classify CRC samples separately from healthy and adenoma samples (area under the curve [AUC] = 0.87). In the present study, we report evidence that host-microbiome dysbiosis in CRC can also be observed by examination of altered small RNA stool profiles. Integrated analyses of the microbiome and small RNAs in the human stool may provide insights for designing more-accurate tools for diagnostic purposes.IMPORTANCE The characteristics of microbial small RNA transcription are largely unknown, while it is of primary importance for a better identification of molecules with functional activities in the gut niche under both healthy and disease conditions. By performing combined analyses of metagenomic and small RNA sequencing (sRNA-Seq) data, we characterized both the human and microbial small RNA contents of stool samples from healthy individuals and from patients with colorectal carcinoma or adenoma. With the integrative analyses of metagenomic and sRNA-Seq data, we identified a human and microbial small RNA signature which can be used to improve diagnosis of the disease. Our analysis of human and gut microbiome small RNA expression is relevant to generation of the first hypotheses about the potential molecular interactions occurring in the gut of CRC patients, and it can be the basis for further mechanistic studies and clinical tests.
| S-EPMC6749105 | biostudies-literature
Project description:Background and Aims: RNA biomarkers derived from sloughed enterocytes would provide an ideal, non-invasive method for early detection of colorectal cancer (CRC) and precancerous adenomas. To realize this goal, a highly reliable method to isolate preserved human RNA from stool samples is needed. Here we develop a protocol to identify RNA biomarkers associated with CRC to assess the use of these biomarkers for noninvasive screening of disease. Methods: Stool samples were collected from 454 patients prior to a colonoscopy. A nucleic acid extraction protocol was developed to isolate human RNA from 330 stool samples and transcript abundances were estimated by microarray analysis. This 330-patient cohort was split into a training set of 265 individuals to develop a machine learning model and a testing set of 65 individuals to determine the model’s ability to detect colorectal neoplasms. Results: Analysis of the transcriptome from 265 individuals identified 200 transcript clusters as differentially expressed (p<0.03). These transcripts were used to build a Support Vector Machine (SVM) based model to classify 65 individuals within the testing set. This SVM algorithm attained a 95% sensitivity for precancerous adenomas and a 65% sensitivity for CRC (stage I-IV). The machine learning algorithm attained a specificity of 59% for healthy individuals and an overall accuracy of 72.3%. Conclusions: We developed an RNA-based neoplasm detection model that is sensitive for CRC and precancerous adenomas. The model allows for non-invasive assessment of tumors and could potentially be used to provide clinical guidance for individuals within the screening population for colorectal cancer.
2019-02-25 | GSE99573 | GEO
Project description:Fecal immunochemical tests (FIT) detecting hemoglobin in stool are widely used for non-invasive colorectal cancer (CRC) screening, but their sensitivity leaves room for improvement. Our aim is to identify novel protein biomarkers in stool that outperform or complement hemoglobin in detecting CRCs and advanced adenomas (AAs). Stool samples (one series of 12 CRCs and 10 controls, and a second series of 81 CRCs, 40 AAs, 43 non-advanced adenomas and 129 controls) were analyzed by mass-spectrometry and searched for human proteins. Classification and regression tree and logistic regression analyses were performed to identify protein combinations that differentiated CRCs and/or AAs from controls. Antibody-based assays for four selected proteins were performed on an independent series of FIT samples (14 CRCs, 16 AAs, 18 non-advanced adenomas and 24 controls) Results: In total, 834 human proteins were identified, of which 29 were significantly enriched in CRCs versus controls in both stool sample series. Combinations of four proteins reached sensitivities of 80% and 45% for detecting CRCs and AAs, at 95% specificity, which was higher than hemoglobin alone.
2020-05-26 | PXD007767 | Pride
Project description:Increasing importance in the onset and progression from colonic adenomatous polyps (AP) to colorectal cancer (CRC) has been attributed to the gut microbiota and the oncometabolites they may produce. To comprehensively study the microbial spatial variations and role of microbiota in CRC progression, multiple niches from the gastrointestinal system have to be investigated. We collected saliva, tissue and stool samples from 61 patients, including 46 CRC patients and 15 AP patients, well matched in age and sex, who were undergoing surgery in 2018 at the Careggi University Hospital (Florence, Italy). For all samples and locations we surveyed microbial composition through 16S ribosomal RNA and metabolites using NMR, and compared them across tissues and disease state, also considering CRC TNM staging. Our result suggest the importance of microbiota communities and derived oncometabolites in CRC development. Such association can be a forerunner for future studies on CRC/AP management.
2023-05-03 | GSE217490 | GEO
Project description:Longitudinal analysis of Salmonella typhimurium mRNA from superspeader mouse cecal content and stool compared to in vitro Salmonella typhimurium mRNA.
2023-03-22 | GSE207764 | GEO
Project description:Mice stool Genome sequencing and assembly altered by LI01 and LI05
| PRJNA579877 | ENA
Project description:Cardioviruses are a genus of picornaviruses that cause severe illnesses in rodents, but little is known about the prevalence, diversity, or spectrum of disease of such agents among humans. We report the identification of a group of human cardioviruses that have been detected and cloned directly from patient specimens (Chiu and DeRisi, et al, PNAS, 2008). This series includes 9 arrays (both raw and normalized data) used to detect cardioviruses in human respiratory and stool specimens. The arrays employed here are capable of pan-viral detection (Wang and DeRisi, et al., PNAS, 2002). Keywords: viral detection, cardiovirus, TMEV, gastroenteritis The series includes 3 arrays from respiratory samples and 6 arrays from stool samples. Among the 3 arrays from respiratory sample, 1 array has a signature for an adenovirus, 1 array has a signature for human metapneumovirus, and 1 array has a signature for cardiovirus UC1 (see Chiu and DeRisi, et al., PNAS, in 2008). All 6 arrays from stool samples are cardiovirus-positive; some show evidence of dual infection with other gastroenteritis viruses (i.e. norovirus, rotavirus, etc.). Data in Sample records fed to E-Predict (Urisman, et al, Genome Biology, 2005) E-Predict normalization metrics Array Normalization: Sum E-Matrix Normalization: Quadratic Distance Metric: Pearson Uncentered
2008-09-17 | E-GEOD-11569 | biostudies-arrayexpress