Ontology highlight
ABSTRACT:
PROVIDER: PRJNA650011 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12374827.fastq.gz | Fastqsanger.gz | |||
| SRR12374828.fastq.gz | Fastqsanger.gz | |||
| SRR12374829.fastq.gz | Fastqsanger.gz | |||
| SRR12374830.fastq.gz | Fastqsanger.gz |
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Project description:Recent progress in unbiased metagenomic next-generation sequencing (mNGS) allows simultaneous examination of microbial and host genetic material in a single test. Leveraging affordable bronchoalveolar lavage fluid (BALF) mNGS data, we employed machine learning to create a diagnostic approach distinguishing lung cancer from pulmonary infections, conditions prone to misdiagnosis in clinical settings. This prospective study analyzed BALF-mNGS data from lung cancer and pulmonary infection patients, delineating differences in DNA/RNA microbial composition, bacteriophage abundances, and host responses, including gene expression, transposable element levels, immune cell composition, and tumor fraction derived from copy number variation (CNV). Integrating these metrics into a host/microbe metagenomics-driven machine learning model (Model VI) demonstrated robustness, achieving an AUC of 0.87 (95% CI = 0.857-0.883), sensitivity = 73.8%, and specificity = 84.5% in the training cohort, and an AUC of 0.831 (95% CI = 0.819-0.843), sensitivity = 67.1%, and specificity = 94.4% in the validation cohort for distinguishing lung cancer from pulmonary infections. The application of a rule-in and rule-out strategy-based composite predictive model significantly enhances accuracy (ACC) in distinguishing between lung cancer and tuberculosis (ACC=0.913), fungal infection (ACC=0.955), and bacterial infection (ACC=0.836). These findings highlight the potential of cost-effective mNGS-based analysis as a valuable tool for early differentiation between lung cancer and pulmonary infections, offering significant benefits through a single comprehensive testing.
2024-01-08 | GSE252118 | GEO
Project description:The paper describes a model of tumor invasion to bone marrow.
Created by COPASI 4.26 (Build 213)
This model is described in the article:
Modeling invasion of metastasizing cancer cells to bone marrow utilizing ecological principles
Kun-Wan Chen, Kenneth J Pienta
Theoretical Biology and Medical Modelling 2011, 8:36
Abstract:
Background: The invasion of a new species into an established ecosystem can be directly compared to the steps involved in cancer metastasis. Cancer must grow in a primary site, extravasate and survive in the circulation to then intravasate into target organ (invasive species survival in transport). Cancer cells often lay dormant at their metastatic site for a long period of time (lag period for invasive species) before proliferating (invasive spread). Proliferation in the new site has an impact on the target organ microenvironment (ecological impact) and eventually the human host (biosphere impact).
Results: Tilman has described mathematical equations for the competition between invasive species in a structured habitat. These equations were adapted to study the invasion of cancer cells into the bone marrow microenvironment as a structured habitat. A large proportion of solid tumor metastases are bone metastases, known to usurp hematopoietic stem cells (HSC) homing pathways to establish footholds in the bone marrow. This required accounting for the fact that this is the natural home of hematopoietic stem cells and that they already occupy this structured space. The adapted Tilman model of invasion dynamics is especially valuable for modeling the lag period or dormancy of cancer cells.
Conclusions: The Tilman equations for modeling the invasion of two species into a defined space have been modified to study the invasion of cancer cells into the bone marrow microenvironment. These modified equations allow a more flexible way to model the space competition between the two cell species. The ability to model initial density, metastatic seeding into the bone marrow and growth once the cells are present, and movement of cells out of the bone marrow niche and apoptosis of cells are all aspects of the adapted equations. These equations are currently being applied to clinical data sets for verification and further refinement of the models.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide.
Please refer to CC0 Public Domain Dedication for more information.
2024-09-02 | BIOMD0000000793 | BioModels
Project description:The paper describes a model of tumor invasion to bone marrow.
Created by COPASI 4.26 (Build 213)
This model is described in the article:
Modeling invasion of metastasizing cancer cells to bone marrow utilizing ecological principles
Kun-Wan Chen, Kenneth J Pienta
Theoretical Biology and Medical Modelling 2011, 8:36
Abstract:
Background: The invasion of a new species into an established ecosystem can be directly compared to the steps involved in cancer metastasis. Cancer must grow in a primary site, extravasate and survive in the circulation to then intravasate into target organ (invasive species survival in transport). Cancer cells often lay dormant at their metastatic site for a long period of time (lag period for invasive species) before proliferating (invasive spread). Proliferation in the new site has an impact on the target organ microenvironment (ecological impact) and eventually the human host (biosphere impact).
Results: Tilman has described mathematical equations for the competition between invasive species in a structured habitat. These equations were adapted to study the invasion of cancer cells into the bone marrow microenvironment as a structured habitat. A large proportion of solid tumor metastases are bone metastases, known to usurp hematopoietic stem cells (HSC) homing pathways to establish footholds in the bone marrow. This required accounting for the fact that this is the natural home of hematopoietic stem cells and that they already occupy this structured space. The adapted Tilman model of invasion dynamics is especially valuable for modeling the lag period or dormancy of cancer cells.
Conclusions: The Tilman equations for modeling the invasion of two species into a defined space have been modified to study the invasion of cancer cells into the bone marrow microenvironment. These modified equations allow a more flexible way to model the space competition between the two cell species. The ability to model initial density, metastatic seeding into the bone marrow and growth once the cells are present, and movement of cells out of the bone marrow niche and apoptosis of cells are all aspects of the adapted equations. These equations are currently being applied to clinical data sets for verification and further refinement of the models.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide.
Please refer to CC0 Public Domain Dedication for more information.
2024-09-02 | BIOMD0000000795 | BioModels
Project description:Neutrophils, key cells of the innate immune system, are responsible for preventing bacterial infections. It was already demonstrated that albeit neutrophils synthesize lower levels of protein and RNA they are able to marked change its gene expression after bacteria exposure. The absence of microbial stimulation has consequences on development of the mucosal immune system as demonstrated using germ-free mice as a model. We are interested in understanding if commensal flora induces changes in the gene expression profile of neutrophils in its site of production, the bone marrow. Total RNA obtained from isolated neutrophils from Bone Marrow from Germ-free mice and control
2013-03-30 | E-GEOD-43511 | biostudies-arrayexpress
Project description:We cultured bone marrow derived dendritic cells from WT and CD11c KO mice. Then, a group of bone marrow dendritic cells were stimulated with LPS overnight. We obtained bone marrow derived dendritic cells with or without LPS stimulation and analyzed proteomics profiles.
2024-01-26 | PXD048907 |
Project description:Microbial exposure at barrier interfaces drives development and balance of the immune system, but the consequences of local infections for systemic immunity and secondary inflammation are unclear. Here, we show that dermal exposure to the bacterium Staphylococcus aureus persistently shapes the immune system of mice with specific impact on progenitor and mature bone marrow neutrophil and eosinophil populations. The infection-imposed changes in eosinophils were long-lasting with functional changes associated with epigenetic and metabolic reprogramming, indicative of trained immunity. Bacterial exposure enhanced dermal allergic sensitization and resulted in exacerbated allergen-induced lung inflammation. S. aureus-mediated reprogramming of bone marrow eosinophils was driven by the alarmin interleukin 33 and the complement cleavage fragment C5a. Our study highlights the systemic impact of skin inflammation and reveals eosinophil progenitor training and organ-crosstalk mechanisms that modulate systemic responses to allergens.
2025-02-12 | GSE287520 | GEO
Project description:Serum samples from multiple myeloma (MM) patients are collected regularly for conventional tests to quantify the M-protein longitudinally. However, the limited sensitivity of conventional tests makes quantification of minimal residual disease (MRD) difficult. MRD can be quantified with highly sensitive molecular techniques on bone marrow, but bone marrow procedures are invasive and cannot be performed regularly. In this study, we introduce highly sensitive targeted mass spectrometry (MS-MRD) to quantify M-protein from patient serum. Therefore, we can follow each patient’s course of disease and dynamically measure MRD.
2023-02-27 | PXD038370 | panorama