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Ontology highlight
ABSTRACT:
PROVIDER: PRJNA784939 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR25246600_1.fastq.gz | Fastqsanger.gz | |||
| SRR25246600_2.fastq.gz | Fastqsanger.gz | |||
| SRR25246700_1.fastq.gz | Fastqsanger.gz | |||
| SRR25246700_2.fastq.gz | Fastqsanger.gz | |||
| SRR25246800_1.fastq.gz | Fastqsanger.gz |
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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.
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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.
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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.
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Project description:Transcriptional profiling of pre-malignant and malignant colorectal cancer lesions provides a means for temporally monitoring key molecular events underlying neoplastic progression. Unfortunately, the most widely used central dataset for colorectal cancer samples from The Cancer Genome Atlas (TCGA) does not contain adenoma samples, putting a greater reliance of in silico analyses and pre-clinical modelling on a handful of independent microarray experiments. Due to the differences in sample acquisition, preparation, downstream analysis and other parameters, results are often incongruent, hindering consensus building. Here, we developed a microarray meta-dataset consisting of 231 normal, 132 adenoma, and 342 colon cancer tissue samples (705 samples total) sourced from 12 independent microarray studies all using the Affymetrix HG U133 Plus 2.0 (GPL570) chip platform including GSE4183, GSE8671,GSE9348, GSE15960, GSE20916, GSE21510, GSE22598, GSE23194, GSE23878, GSE32323, GSE33113, and GSE37364. Individual datasets were pre-processed and normalized by frozen robust multiarray averaging (fRMA) before merging by matching probe sets. Batch effects were subsequently identified by Principal Component Analysis (PCA) and removed using ComBat. In addition, low variant probes were filtered from the meta-dataset before downstream analysis. Finally, biological signatures corresponding to cancer and adenoma samples were both quantitatively and functionally validated. Quantitative validation was performed by correlation analysis of LogFC values with the TCGA-COAD or other external GEO microarray datasets, respectively. Functional validation was carried out through predictive analyses using Ingenuity Pathway Analysis (IPA) and Gene Set Enrichment Analysis (GSEA). Overall, our meta-dataset provides a powerful tool for studying transcriptome-wide changes which occur during early dysplasia and malignant transformation of adenomas as well as colorectal cancer in general.
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Project description:Expression profiling is a well established tool for the genome-wide analysis of the transcriptional activity of human neoplasia. However, the high sensitivity of this approach combined with the well-known cellular and molecular heterogeneity of cancer often result in extremely complex and extended expression signatures of difficult functional interpretation. The majority of sporadic colorectal cancer cases are triggered by mutations in the APC tumor suppressor gene leading to constitutive activation of the Wnt/b-catenin signaling pathway and adenoma formation. Notwithstanding this common genetic basis, colorectal cancers are very heterogeneous in their degree of differentiation, growth rate and malignant potential. Here, we applied cross-species comparison of expression profiles of intestinal polyps derived from hereditary colorectal cancer patients carrying APC germline mutations, and from a mouse model carrying a targeted inactivating mutation in the mouse homologue Apc. This comparative approach resulted in the establishment of a conserved signature encompassing 166 genes differentially expressed between adenomas and normal intestinal mucosa in both species. Functional analysis of the conserved genes revealed a general increase in cell proliferation and the activation of the Wnt/b-catenin signal transduction pathway, as expected from the selection of APC/Apc-mutant intestinal adenomas. Moreover, the conserved signature is able to resolve expression profiles from hereditary polyposis patients carrying APC germline mutations from those with bi-allelic incativation of the MYH gene. Keywords: normal versus tumor comparison; compare expression profiles from microdissected samples with distinct germline mutations (FAP vs. MAP). Here, we report the expression profiles from normal and intestinal adenomatous tissue from FAP (familial adenomatous polyposis) and MAP (MUTY-associated polyposis) patients with established APC and MUTY germline mutations. Total RNA samples were obtianed by laser capture microdissection.
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Project description:Colorectal cancer can be divided into four consensus molecular subtypes, which might associate with distinct precursor lesions. The aim of this study was to determine the subtype affiliation of two types of colorectal adenomas: tubular adenomas (TAs) and sessile serrated adenomas (SSAs) and to determine the activity of TGFβ signaling and the role of this cytokine in subtype affiliation. Adenoma samples were collected in the Academic Medical Center (AMC), Amsterdam, The Netherlands. Tubular adenomas (TAs) were obtained from familial adenomatous polyposis (FAP) patients and sessile serrated adenomas (SSAs) were collected from serrated polyposis syndrome (SPS) patients. Gene expression was analyzed for 7 sessile serrated adenomas (SSA) and 9 tubular adenomas (TA).
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