Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Biopsy bacterial signature can predict patient tissue malignancy

ABSTRACT: Biopsy bacterial signature can predict patient tissue malignancy

PROVIDER: PRJEB55383 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Biopsy bacterial signature can predict patient tissue malignancy.

Project description:Considerable recent research has indicated the presence of bacteria in a variety of human tumours and matched normal tissue. Rather than focusing on further identification of bacteria within tumour samples, we reversed the hypothesis to query if establishing the bacterial profile of a tissue biopsy could reveal its histology / malignancy status. The aim of the present study was therefore to differentiate between malignant and non-malignant fresh breast biopsy specimens, collected specifically for this purpose, based on bacterial sequence data alone. Fresh tissue biopsies were obtained from breast cancer patients and subjected to 16S rRNA gene sequencing. Progressive microbiological and bioinformatic contamination control practices were imparted at all points of specimen handling and bioinformatic manipulation. Differences in breast tumour and matched normal tissues were probed using a variety of statistical and machine-learning-based strategies. Breast tumour and matched normal tissue microbiome profiles proved sufficiently different to indicate that a classification strategy using bacterial biomarkers could be effective. Leave-one-out cross-validation of the predictive model confirmed the ability to identify malignant breast tissue from its bacterial signature with 84.78% accuracy, with a corresponding area under the receiver operating characteristic curve of 0.888. This study provides proof-of-concept data, from fit-for-purpose study material, on the potential to use the bacterial signature of tissue biopsies to identify their malignancy status.

| S-EPMC8448740 | biostudies-literature

Author Correction: Biopsy bacterial signature can predict patient tissue malignancy.

Project description: Not available

| S-EPMC9666551 | biostudies-literature

Utlizing the RA signature to predict response to TNFi

Project description:Utlizing the RA signature to predict response to TNFi

| PRJNA755760 | ENA

Utlizing the RA signature to predict response to TNFi

Project description:Utlizing the RA signature to predict response to TNFi

| PRJNA755761 | ENA

Transcription profiling of human chronic lymphocytic leukemia samples to improve prognosis and predict therapeutic response

Project description:Chronic lymphocytic leukemia (CLL) is a heterogeneous malignancy, characterized by a variable clinical course. While clinical and laboratory parameters are increasingly being used to refine prognosis, they do not accurately predict response to commonly used therapy. We used gene expression profiling to generate and further refine prognostic and predictive markers. Genomic signatures that reflect progressive disease and responses to chemotherapy or chemo-immunotherapy were created using cancer cell lines and patient leukemia samples. We validated these signatures using independent clinical data from four separate cohorts representing a total of 301 CLL patients. A prognostic genomic signature created from patient leukemic cell gene expression data coupled with clinical parameters could statistically differentiate patients with stable or progressive disease in the training dataset. The progression signature was then validated in two independent datasets, demonstrating a capacity to accurately identify patients at risk for progressive disease. In addition, two distinct genomic signatures that predict response to chlorambucil or pentostatin, cyclophosphamide, and rituximab were also generated and were shown to accurately distinguish responding and non-responding CLL patients. Microarray analysis of CLL patientsâ lymphocytes can be used to refine prognosis and predict response to different therapies. These results have direct implications for standard and investigational therapeutics in CLL patients. Experiment Overall Design: For the predictive genomic signature or response to pentostatin, cyclophosphamide, and rituximab, 20 CLL leukemia samples were used in the training set, and 20 CLL leukemia samples were used in the validation set

2009-11-11 | E-GEOD-10137 | biostudies-arrayexpress

CAF variants control the tumor-immune microenvironment and predict skin cancer malignancy

Project description:CAF variants control the tumor-immune microenvironment and predict skin cancer malignancy

| PRJNA1072630 | ENA

lgG4 lesion of EoE patient biopsy by AutoSTOMP

Project description:To test the ability of the Automated Spatially Targeted Optical Micro Proteomics (AutoSTOMP) protocol for clinical research. We collected from a patient diagnosed with active EoE (≥15 eosinophils/hpf), according to consensus guidelines, using standard endoscopy procedures, then we try to selectively obtain the proteome from the discrete lgG4+ lesions of the biopsy sections by AutoSTOMP, and try to link the lesion structure with its specially skewed constituents comparing to the rest of the tissue.

2021-08-10 | PXD026819 | Pride

Transcriptome of tumor biopsy sample of patient with granulosa cell tumor of the ovary

Project description:We profiled gene expression in formalin-fixed, paraffin-embedded (FFPE) tumor biopsy sample of the patient with granulosa cell tumor of the ovary.

2018-04-02 | GSE112579 | GEO

Using patient-derived organoids to predict locally advanced or metastatic lung cancer tumor response: a real-world study

Project description:Using patient derived lung cancer organoids to predict clinical response and find theraputic mechanisms

2022-09-26 | PXD037076 |

Light chain amyloid fibrils from an AL amyloidosis patient (case 1), abdominal fat biopsy

Project description:In this study, we successfully extracted and resolved the high-resolution structure of amyloid fibrils from biopsy sample obtained from a living AL patient using cryo-EM.

2025-12-23 | PXD065430 | Pride

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