Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1243732 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR32940201.fastq.gz | Fastqsanger.gz | |||
| SRR32940202.fastq.gz | Fastqsanger.gz | |||
| SRR32940203.fastq.gz | Fastqsanger.gz | |||
| SRR32940204.fastq.gz | Fastqsanger.gz | |||
| SRR32940205_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 52 |
Similar Datasets
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:Intrinsic subtyping of breast cancer was performed using an nCounter RUO-PAM50 gene expression assay to determine the ability of instrinsic subtyping to predict what patients may benefit from altered chemotherapy scheduling in the CALGB 9741 clinical trial population. FFPE primary breast tumor samples archived at the CALGB Pathology Coordinating Office (PCO) were used to obtain total RNA for instrinsic subtyping using the nCounter Analysis System. Gene-expression profiles were generated for 1321 of 1471 patient samples (90%) suitable for inclusion in this study.
2015-11-10 | E-GEOD-74821 | biostudies-arrayexpress
Project description:The GUSTO clinical trial (Gene expression subtypes of Urothelial carcinoma: Stratified Treatment and Oncological outcomes) uses molecular subtypes to guide neoadjuvant therapies in participants with muscle-invasive bladder cancer (MIBC). Before commencing the GUSTO trial, we needed to determine the reliability of a commercial subtyping platform (Decipher Bladder; Veracyte) when performed in an external trial laboratory as this has not been done previously. Here we report our pre-trial verification of the TCGA molecular subtyping model using gene expression profiling. Formalin fixed paraffin embedded tissue blocks of MIBC were used for gene expression subtyping by gene expression microarrays. Intra- and inter-laboratory technical reproducibility, together with quality control of laboratory and bioinformatics processes were assessed. Eighteen samples underwent analysis. RNA of sufficient quality and quantity was successfully extracted from all samples. All subtypes were represented in the cohort. Each sample was subtyped twice in our laboratory and once in a separate reference laboratory. No clinically significant discordance in subtype occurred between intra- or inter-laboratory replicates. Examination of sample histopathology showed variability of morphological appearances within and between subtypes. Overall, these results show that molecular subtyping by gene expression profiling is reproducible, robust, and suitable for use in the GUSTO clinical trial.
2024-01-18 | GSE253531 | GEO
Project description:Five molecular subtypes have been identified according to intrinsic genes transcription. Microarrays have been adopted for molecular subtyping in addition to the original NanoString nCounter assay. This study aimed to evaluate subtyping consistency among Taiwanese breast cancers. Our study showed that fundamental discrepancy exists between distinct gene expression measuring approaches, and cross platform equivalence should not be overemphasized with too much extrapolation.
2021-09-01 | GSE162228 | GEO
Project description:Breast cancer was one of the first cancer types where molecular subtyping led to explanation of interpersonal heterogeneity and resulted in improvement of treatment regimen. Several multigene classifiers have been developed and in particular those defining molecular signatures of early breast cancers possess significant prognostic information. Hence since 2014, molecular subtyping of primary breast cancers was implemented as a part of routine diagnostics with direct impact of therapy assignment. In this study, we evaluate direct and potential benefits of molecular subtyping in low-risk breast cancers as well as present the advantages of a robust molecular signature in regard to patient work-up among high-risk breast cancers.
2017-12-01 | E-MTAB-5724 | biostudies-arrayexpress